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Alie MS, Negesse Y, Kindie K, Merawi DS. Machine learning algorithms for predicting COVID-19 mortality in Ethiopia. BMC Public Health 2024; 24:1728. [PMID: 38943093 PMCID: PMC11212371 DOI: 10.1186/s12889-024-19196-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 06/19/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia. METHODS Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC). RESULTS The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features. CONCLUSION Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.
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Affiliation(s)
- Melsew Setegn Alie
- Department Public Health, School of Public Health, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia.
| | - Yilkal Negesse
- Department of Public Health, College of Medicine and Health Science, Debre Markos University, Gojjam, Ethiopia
| | - Kassa Kindie
- Department Nursing, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia
| | - Dereje Senay Merawi
- Department of Information Technology, Faculty of Technology, Debre Tabor University, Gonder, Ethiopia
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Rahmatinejad Z, Dehghani T, Hoseini B, Rahmatinejad F, Lotfata A, Reihani H, Eslami S. A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department. Sci Rep 2024; 14:3406. [PMID: 38337000 PMCID: PMC10858239 DOI: 10.1038/s41598-024-54038-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/07/2024] [Indexed: 02/12/2024] Open
Abstract
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.
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Affiliation(s)
- Zahra Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Toktam Dehghani
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Toos Institute of Higher Education, Mashhad, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - Hamidreza Reihani
- Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Saeid Eslami
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands.
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Zahran TE, Al Hassan S, Al Karaki V, Hammoud L, Helou CE, Khalifeh M, Al Hariri M, Tamim H, Majzoub IE. Outcomes of critically ill COVID-19 patients boarding in the emergency department of a tertiary care center in a developing country: a retrospective cohort study. Int J Emerg Med 2023; 16:73. [PMID: 37833683 PMCID: PMC10576402 DOI: 10.1186/s12245-023-00551-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Boarding of critically ill patients in the emergency department (ED) has long been known to compromise patient care and affect outcomes. During the COVID-19 pandemic, multiple hospitals worldwide experienced overcrowded emergency rooms. Large influx of patients outnumbered hospital beds and required prolonged length of stay (LOS) in the ED. Our aim was to assess the ED LOS effect on mortality and morbidity, in addition to the predictors of in-hospital mortality, intubation, and complications of critically ill COVID-19 ED boarder patients. METHODS This was a retrospective cohort study, investigating 145 COVID-19-positive adult patients who were critically ill, required intensive care unit (ICU), and boarded in the ED of a tertiary care center in Lebanon. Data on patients who boarded in the emergency from January 1, 2020, till January 31, 2021, was gathered and studied. RESULTS Overall, 66% of patients died, 60% required intubation, and 88% developed complications. Multiple risk factors were associated with mortality naming age above 65 years, vasopressor use, severe COVID pneumonia findings on CT chest, chemotherapy treatment in the previous year, cardiovascular diseases, chronic kidney diseases, prolonged ED LOS, and low SaO2 < 95% on triage. In addition, our study showed that staying long hours in the ED increased the risk of developing complications. CONCLUSION To conclude, all efforts need to be drawn to re-establish mitigation strategies and models of critical care delivery in the ED to alleviate the burden of critical boarders during pandemics, thus decreasing morbidity and mortality rates. Lessons from this pandemic should raise concern for complications seen in ED ICU boarders and allow the promotion of health measures optimizing resource allocation in future pandemic crises.
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Affiliation(s)
- Tharwat El Zahran
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon.
| | - Sally Al Hassan
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Victoria Al Karaki
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Lina Hammoud
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Christelle El Helou
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Malak Khalifeh
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Moustafa Al Hariri
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
- QU Health, Qatar University, Doha, Qatar
| | - Hani Tamim
- Department of Internal Medicine, American University of Beirut Medical Center, Beirut, Lebanon
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Imad El Majzoub
- Department of Emergency Medicine, American University of Beirut Medical Center, Beirut, Lebanon
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Sagah GA, Elmansy AM. Comparison of different scores as predictors of mechanical ventilation and intensive care unit admission in acute theophylline poisoning. Toxicol Res (Camb) 2023; 12:990-997. [PMID: 37915483 PMCID: PMC10615812 DOI: 10.1093/toxres/tfad093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/06/2023] [Accepted: 09/15/2023] [Indexed: 11/03/2023] Open
Abstract
Background Theophylline is commonly used to control respiratory diseases, especially in developing countries. Theophylline has a narrowed therapeutic index, and its toxicity is associated with morbidity and mortality. Physicians should be aware of the early prediction of the need for intensive care unit admission (ICU) and mechanical ventilation (MV). Aim This study aimed to assess the power of the Rapid Emergency Medicine Score (REMS), Modified Early Warning Score (MEWS) and Simple Clinical Score (SCS) in predicting the need for ICU admission and/or MV in acute theophylline-poisoned patients. Patients and methods This cross-sectional study included 58 patients with acute theophylline poisoning who were admitted to our Poison Control Center from the 1st of July 2022 to the 31st of January 2023. The REMS, MEWS and SCS were calculated for all patients on arrival at the hospital. The area under the curve (AUC) and receiver operating characteristics were tested to compare scores. Results The median values of all studied scores were significantly high among patients who needed MV and/or ICU admission. The AUC of SCS was >0.9, with a sensitivity of 92.9% and specificity of 90.9% for the prediction of ICU admission. Meanwhile, MEWS was an excellent predictor of the need for MV (AUC = 0.996, 95% CI = 0.983-1.000). Conclusions We recommend using SCS as an early predictor for ICU admission in acute theophylline-poisoned patients. However, MEWS could effectively predict MV requirements in acute theophylline-poisoned patients.
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Affiliation(s)
- Ghada Attia Sagah
- Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Medical collages complex, 6 th Floor, Al-Geish Street, Tanta University, Tanta, Elgharbya 31527, Egypt
| | - Alshaimma Mahmoud Elmansy
- Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Medical collages complex, 6 th Floor, Al-Geish Street, Tanta University, Tanta, Elgharbya 31527, Egypt
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Santiago González N, García-Hernández MDL, Cruz-Bello P, Chaparro-Díaz L, Rico-González MDL, Hernández-Ortega Y. Modified Early Warning Score: Clinical Deterioration of Mexican Patients Hospitalized with COVID-19 and Chronic Disease. Healthcare (Basel) 2023; 11:2654. [PMID: 37830691 PMCID: PMC10572652 DOI: 10.3390/healthcare11192654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/14/2023] Open
Abstract
The objective was to evaluate the Modified Early Warning Score in patients hospitalized for COVID-19 plus chronic disease. METHODS Retrospective observational study, 430 hospitalized patients with COVID-19 and chronic disease. Instrument, Modified Early Warning Score (MEWS). Data analysis, with Cox and logistic regression, to predict survival and risk. RESULTS Of 430 patients, 58.6% survived, and 41.4% did not. The risk was: low 53.5%, medium 23.7%, and high 22.8%. The MEWS score was similar between survivors 3.02, p 0.373 (95% CI: -0.225-0.597) and non-survivors 3.20 (95% CI: -0.224-0.597). There is a linear relationship between MEWS and mortality risk R 0.920, ANOVA 0.000, constant 4.713, and coefficient 4.406. The Cox Regression p 0.011, with a risk of deterioration of 0.325, with a positive coefficient, the higher the risk, the higher the mortality, while the invasive mechanical ventilation coefficient was negative -0.757. By providing oxygen and ventilation, mortality is lower. CONCLUSIONS The predictive value of the modified early warning score in patients hospitalized for COVID-19 and chronic disease is not predictive with the MEWS scale. Additional assessment is required to prevent complications, especially when patients are assessed as low-risk.
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Affiliation(s)
- Nicolás Santiago González
- Hospital Regional de Alta Especialidad Ixtapaluca (HRAEI), Universidad Autónoma del Estado de México (UAEMex), Ixtapaluca 56530, Mexico;
| | - María de Lourdes García-Hernández
- Facultad de Enfermería y Obstetricia, Universidad Autónoma del Estado de México (UAEMéx), Toluca 50000, Mexico; (P.C.-B.); (M.d.L.R.-G.); (Y.H.-O.)
| | - Patricia Cruz-Bello
- Facultad de Enfermería y Obstetricia, Universidad Autónoma del Estado de México (UAEMéx), Toluca 50000, Mexico; (P.C.-B.); (M.d.L.R.-G.); (Y.H.-O.)
| | - Lorena Chaparro-Díaz
- Nursing Department, Faculty of Nursing, Universidad Nacional de Colombia, Sede Bogotá, Bogotá 111321, Colombia;
| | - María de Lourdes Rico-González
- Facultad de Enfermería y Obstetricia, Universidad Autónoma del Estado de México (UAEMéx), Toluca 50000, Mexico; (P.C.-B.); (M.d.L.R.-G.); (Y.H.-O.)
| | - Yolanda Hernández-Ortega
- Facultad de Enfermería y Obstetricia, Universidad Autónoma del Estado de México (UAEMéx), Toluca 50000, Mexico; (P.C.-B.); (M.d.L.R.-G.); (Y.H.-O.)
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Annareddy S, Ghewade B, Jadhav U, Wagh P. Unraveling the Predictive Potential of Rapid Scoring in Pleural Infection: A Critical Review. Cureus 2023; 15:e44515. [PMID: 37789994 PMCID: PMC10544591 DOI: 10.7759/cureus.44515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/31/2023] [Indexed: 10/05/2023] Open
Abstract
Pleural infection, or pleural empyema, is a severe medical condition associated with high morbidity and mortality rates. Timely and accurate prognostication is crucial for optimizing patient outcomes and resource allocation. Rapid scoring systems have emerged as promising tools in pleural infection prognostication, integrating various clinical and laboratory parameters to assess disease severity and quantitatively predict short-term and long-term outcomes. This review article critically evaluates existing rapid scoring systems, including CURB-65 (confusion, uremia, respiratory rate, blood pressure, age ≥ 65 years), A-DROP (age (male >70 years, female >75 years), dehydration, respiratory failure, orientation disturbance, and low blood pressure), and APACHE II (acute physiology and chronic health evaluation II), assessing their predictive accuracy and limitations. Our analysis highlights the potential clinical implications of rapid scoring, including risk stratification, treatment tailoring, and follow-up planning. We discuss practical considerations and challenges in implementing rapid scoring such as data accessibility and potential sources of bias. Furthermore, we emphasize the importance of validation, transparency, and multidisciplinary collaboration to refine and enhance the clinical applicability of these scoring systems. The prospects for rapid scoring in pleural infection management are promising, with ongoing research and data science advances offering improvement opportunities. Ultimately, the successful integration of rapid scoring into clinical practice can potentially improve patient care and outcomes in pleural infection management.
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Affiliation(s)
- Srinivasulareddy Annareddy
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Babaji Ghewade
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ulhas Jadhav
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Pankaj Wagh
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Zakariaee SS, Naderi N, Ebrahimi M, Kazemi-Arpanahi H. Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data. Sci Rep 2023; 13:11343. [PMID: 37443373 PMCID: PMC10345104 DOI: 10.1038/s41598-023-38133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for mortality prediction of COVID-19 patients. The prognostic performances of the machine learning (ML)-based models for predicting clinical outcomes of COVID-19 patients had been mainly evaluated using demographics, risk factors, clinical manifestations, and laboratory results. There is a lack of information about the prognostic role of imaging manifestations in combination with demographics, clinical manifestations, and laboratory predictors. The purpose of the present study is to develop an efficient ML prognostic model based on a more comprehensive dataset including chest CT severity score (CT-SS). Fifty-five primary features in six main classes were retrospectively reviewed for 6854 suspected cases. The independence test of Chi-square was used to determine the most important features in the mortality prediction of COVID-19 patients. The most relevant predictors were used to train and test ML algorithms. The predictive models were developed using eight ML algorithms including the J48 decision tree (J48), support vector machine (SVM), multi-layer perceptron (MLP), k-nearest neighbourhood (k-NN), Naïve Bayes (NB), logistic regression (LR), random forest (RF), and eXtreme gradient boosting (XGBoost). The performances of the predictive models were evaluated using accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) metrics. After applying the exclusion criteria, a total of 815 positive RT-PCR patients were the final sample size, where 54.85% of the patients were male and the mean age of the study population was 57.22 ± 16.76 years. The RF algorithm with an accuracy of 97.2%, the sensitivity of 100%, a precision of 94.8%, specificity of 94.5%, F1-score of 97.3%, and AUC of 99.9% had the best performance. Other ML algorithms with AUC ranging from 81.2 to 93.9% had also good prediction performances in predicting COVID-19 mortality. Results showed that timely and accurate risk stratification of COVID-19 patients could be performed using ML-based predictive models fed by routine data. The proposed algorithm with the more comprehensive dataset including CT-SS could efficiently predict the mortality of COVID-19 patients. This could lead to promptly targeting high-risk patients on admission, the optimal use of hospital resources, and an increased probability of survival of patients.
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Affiliation(s)
| | - Negar Naderi
- Department of Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Mahdi Ebrahimi
- Department of Emergency Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
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Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, Rahmatinejad F, Eslami S. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023; 27:416-425. [PMID: 37378368 PMCID: PMC10291668 DOI: 10.5005/jp-journals-10071-24463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 04/19/2023] [Indexed: 06/29/2023] Open
Abstract
Background The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). Materials and methods A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation. Results The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. Conclusion The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors. How to cite this article Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.
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Affiliation(s)
- Zahra Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamidreza Reihani
- Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ameen Abu Hanna
- Department of Medical Informatics, Amsterdam UMC – Location AMC, University of Amsterdam, the Netherlands
| | - Ali Pourmand
- Department of Emergency Medicine, The George Washington University, School of Medicine and Health Sciences, Washington DC, United States
| | - Seyyed Mohammad Tabatabaei
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Rahmatinejad
- Department of Health Information Technology, Faculty of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Eslami
- Department of Medical Informatics, Faculty of Medicine; Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, Amsterdam UMC – Location AMC, University of Amsterdam, the Netherlands
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Ruangsomboon O, Phanprasert N, Jirathanavichai S, Puchongmart C, Boonmee P, Thirawattanasoot N, Dorongthom T, Praphruetkit N, Monsomboon A. The utility of the Rapid Emergency Medicine Score (REMS) compared with three other early warning scores in predicting in-hospital mortality among COVID-19 patients in the emergency department: a multicenter validation study. BMC Emerg Med 2023; 23:45. [PMID: 37101141 PMCID: PMC10132401 DOI: 10.1186/s12873-023-00814-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 04/12/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes of COVID-19 in the Emergency Department (ED), including the quick Sequential Organ Failure Assessment (qSOFA), the Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has not been widely validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of qSOFA, MEWS, and NEWS for predicting mortality in emergency COVID-19 patients. METHODS We conducted a multi-center retrospective study at five EDs of various levels of care in Thailand. Adult patients visiting the ED who tested positive for COVID-19 prior to ED arrival or within the index hospital visit between January and December 2021 were included. Their EWSs at ED arrival were calculated and analysed. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation. RESULTS A total of 978 patients were included in the study; 254 (26%) died at hospital discharge, and 155 (15.8%) were intubated. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.771 (95% confidence interval (CI) 0.738, 0.804)), which was significantly higher than qSOFA (AUROC 0.620 (95%CI 0.589, 0.651); p < 0.001), MEWS (AUROC 0.657 (95%CI 0.619, 0.694); p < 0.001), and NEWS (AUROC 0.732 (95%CI 0.697, 0.767); p = 0.037). REMS was also the best EWS in terms of calibration, overall model performance, and balanced diagnostic accuracy indices at its optimal cutoff. REMS also performed better than other EWSs for mechanical ventilation. CONCLUSION REMS was the early warning score with the highest prognostic utility as it outperformed qSOFA, MEWS, and NEWS in predicting in-hospital mortality in COVID-19 patients in the ED.
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Affiliation(s)
- Onlak Ruangsomboon
- Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol University, Bangkok, Thailand
| | - Nutthida Phanprasert
- Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol University, Bangkok, Thailand
| | - Supawich Jirathanavichai
- Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol University, Bangkok, Thailand
| | | | - Phetsinee Boonmee
- Department of Emergency Medicine, Ratchaburi Hospital, Ratchaburi, Thailand
| | | | - Thawonrat Dorongthom
- Department of Emergency Medicine and Forensic Medicine, Prachuap Khiri Khan hospital, Prachuap Khiri Khan, Thailand
| | - Nattakarn Praphruetkit
- Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol University, Bangkok, Thailand
| | - Apichaya Monsomboon
- Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol University, Bangkok, Thailand.
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Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 2023; 38:355-372. [PMID: 36840867 PMCID: PMC9958330 DOI: 10.1007/s10654-023-00973-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 01/28/2023] [Indexed: 02/26/2023]
Abstract
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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Affiliation(s)
- Chepkoech Buttia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
- Epistudia, Bern, Switzerland
| | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- ELKH-DE Public Health Research Group of the Hungarian Academy of Sciences, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Epistudia, Bern, Switzerland
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Hamidreza Raeisi-Dehkordi
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lum Kastrati
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mojgan Amiri
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renald Meçani
- Department of Pediatrics, “Mother Teresa” University Hospital Center, Tirana, University of Medicine, Tirana, Albania
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Petek Eylul Taneri
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- HRB-Trials Methodology Research Network College of Medicine, Nursing and Health Sciences University of Galway, Galway, Ireland
| | | | - Peter Francis Raguindin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
- Faculty of Health Sciences, University of Lucerne, Lucerne, Switzerland
| | - Faina Wehrli
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Farnaz Khatami
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Octavio Pano Espínola
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Preventive Medicine and Public Health, University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
| | - Lyda Z. Rojas
- Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia
| | | | | | - Fadi Alijla
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Beatrice Minder
- Public Health and Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Alexander B. Leichtle
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, and Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
| | - Nora Lüthi
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Simone Ehrhard
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Yok-Ai Que
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurenz Kopp Fernandes
- Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf Hautz
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Epistudia, Bern, Switzerland
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Shen B, Hou W, Jiang Z, Li H, Singer AJ, Hoshmand-Kochi M, Abbasi A, Glass S, Thode HC, Levsky J, Lipton M, Duong TQ. Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients. Diagnostics (Basel) 2023; 13:diagnostics13061107. [PMID: 36980414 PMCID: PMC10047384 DOI: 10.3390/diagnostics13061107] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.
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Affiliation(s)
- Beiyi Shen
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Wei Hou
- Department of Family Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Zhao Jiang
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Haifang Li
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Adam J. Singer
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mahsa Hoshmand-Kochi
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Almas Abbasi
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Samantha Glass
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Henry C. Thode
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Jeffrey Levsky
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA
| | - Michael Lipton
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA
| | - Tim Q. Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA
- Correspondence: ; Tel.: +718-920-6268
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Evaluation of Early Warning Scores on In-Hospital Mortality in COVID-19 Patients: A Tertiary Hospital Study from Taiwan. Medicina (B Aires) 2023; 59:medicina59030464. [PMID: 36984465 PMCID: PMC10057579 DOI: 10.3390/medicina59030464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) remains a global pandemic. Early warning scores (EWS) are used to identify potential clinical deterioration, and this study evaluated the ability of the Rapid Emergency Medicine score (REMS), National Early Warning Score (NEWS), and Modified EWS (MEWS) to predict in-hospital mortality in COVID-19 patients. This study retrospectively analyzed data from COVID-19 patients who presented to the emergency department and were hospitalized between 1 May and 31 July 2021. The area under curve (AUC) was calculated to compare predictive performance of the three EWS. Data from 306 COVID-19 patients (61 ± 15 years, 53% male) were included for analysis. REMS had the highest AUC for in-hospital mortality (AUC: 0.773, 95% CI: 0.69–0.85), followed by NEWS (AUC: 0.730, 95% CI: 0.64–0.82) and MEWS (AUC: 0.695, 95% CI: 0.60–0.79). The optimal cut-off value for REMS was 6.5 (sensitivity: 71.4%; specificity: 76.3%), with positive and negative predictive values of 27.9% and 95.4%, respectively. Computing REMS for COVID-19 patients who present to the emergency department can help identify those at risk of in-hospital mortality and facilitate early intervention, which can lead to better patient outcomes.
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Prognostic Value of Physiological Scoring Systems in COVID-19 Patients: A Prospective Observational Study. Adv Emerg Nurs J 2023; 45:77-85. [PMID: 36757751 DOI: 10.1097/tme.0000000000000445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The objective of this study was to investigate the accuracy of the Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Rapid Acute Physiology Score (RAPS), Worthing Physiological Scoring System (WPSS), and Revised Trauma Score (RTS) for predicting the inhospital mortality of COVID-19 patients. This diagnostic accuracy study was conducted in Tehran, Iran, from November 15, 2020, to March 10, 2021. The participants consisted of 246 confirmed cases of COVID-19 patients who were admitted to the emergency department. The patients were followed from the point of admission up until discharge from the hospital. The mortality status of patients (survivor or nonsurvivor) was reported at the discharge time, and the receiver operating characteristic curve analysis of each scoring system for predicting inhospital mortality was estimated. The area under the curve of REMS was significantly higher than other scoring systems and in cutoff value of 6 and greater had a sensitivity and specificity of 89.13% and 55.50%, respectively. Among the five scoring systems employed in this study, REMS had the best accuracy to predict the inhospital mortality rate of COVID-19 patients and RAPS had the lowest accuracy for inhospital mortality. Thus, REMS is a useful tool that can be employed in identifying high-risk COVID-19 patients.
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Koya S, Ponnam S, Salenius S, Pamidighantam S. A Markov Chain Monte Carlo (MCMC) Multivariate Analysis of the Association of Vital Parameter Variation With the Lunar Cycle in Patients Hospitalized With COVID-19. Cureus 2023; 15:e34290. [PMID: 36860231 PMCID: PMC9970724 DOI: 10.7759/cureus.34290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 01/30/2023] Open
Abstract
INTRODUCTION Over the last three years, the world has been battling a long-drawn pandemic resulting from the coronavirus outbreak. Despite the safety measures, there have been multiple pandemic waves happening throughout the world. Therefore, it is necessary to understand the fundamental characteristics of COVID-19 transmission and pathogenesis to overcome the threat of the pandemic. This study focused on hospitalized COVID-19 patients because of their high mortality rate, which indicates the need to improve inpatient management. METHODS Based on the cyclic nature of the pandemic, observations were made to examine the influence of lunar phases on six vital parameters of COVID-19 patients. A multivariate analysis was carried out to study the interactions of lunar phase pairwise on COVID-19 statuses and COVID-19 status pairwise on lunar phases by treating six vital parameters as independent entities. RESULTS The results of multivariate analysis on the data of 215,220 vital values showed that lunar phases are associated with trends in variations in the vital parameters of COVID-19-infected patients. CONCLUSION In summary, our results show that patients infected with COVID-19 appear to be more susceptible to lunar influence compared to non-COVID-19 patients. Furthermore, this study shows a vital parameter destabilization window (DSW) that can help identify which hospitalized COVID-19 patients can recover. Our pilot study forms the basis for future studies to eventually establish the incorporation of variation of vital signs with the lunar cycle into the standard of care for COVID-19 patients.
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Affiliation(s)
- Supriya Koya
- Hematology and Oncology, GenesisCare, Ponca City, USA
- Hematology and Medical Oncology, Hillcrest Medical Center, Tulsa, USA
| | - Sreeja Ponnam
- Internal Medicine, University of Missouri - Kansas City School of Medicine, Kansas City, USA
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Zakariaee SS, Abdi AI, Naderi N, Babashahi M. Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023; 54:73. [PMCID: PMC10116092 DOI: 10.1186/s43055-023-01022-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 04/13/2023] [Indexed: 04/05/2024] Open
Abstract
Background The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Methods This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Results After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (± SD) age of participants was 57.22(± 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusions The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.
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Affiliation(s)
- Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Aza Ismail Abdi
- Department of Radiology, Erbil Medical Technical Institute, Erbil Polytechnic University, Erbil, Iraq
| | - Negar Naderi
- Department of Midwifery, Faculty of Nursing and Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Mashallah Babashahi
- Department of Pathology, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
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Stanevich OV, Bakin EA, Korshunova AA, Gudkova AY, Afanasev AA, Shlyk IV, Lioznov DA, Polushin YS, Kulikov AN. Informativeness estimation for the main clinical and laboratory parameters in patients with severe COVID-19. TERAPEVT ARKH 2022; 94:1225-1233. [PMID: 37167158 DOI: 10.26442/00403660.2022.11.201941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Indexed: 12/27/2022]
Abstract
Aim. To conduct a retrospective assessment of the clinical and laboratory data of patients with severe forms of COVID-19 hospitalized in the intensive care and intensive care unit, in order to assess the contribution of various indicators to the likelihood of death.
Materials and methods. A retrospective assessment of data on 224 patients with severe COVID-19 admitted to the intensive care unit was carried out. The analysis included the data of biochemical, clinical blood tests, coagulograms, indicators of the inflammatory response. When transferring to the intensive care units (ICU), the indicators of the formalized SOFA and APACHE scales were recorded. Anthropometric and demographic data were downloaded separately.
Results. Analysis of obtained data, showed that only one demographic feature (age) and a fairly large number of laboratory parameters can serve as possible markers of an unfavorable prognosis. We identified 12 laboratory features the best in terms of prediction: procalcitonin, lymphocytes (absolute value), sodium (ABS), creatinine, lactate (ABS), D-dimer, oxygenation index, direct bilirubin, urea, hemoglobin, C-reactive protein, age, LDH. The combination of these features allows to provide the quality of the forecast at the level of AUC=0.85, while the known scales provided less efficiency (APACHE: AUC=0.78, SOFA: AUC=0.74).
Conclusion. Forecasting the outcome of the course of COVID-19 in patients in ICU is relevant not only from the position of adequate distribution of treatment measures, but also from the point of view of understanding the pathogenetic mechanisms of the development of the disease.
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Afrash MR, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms. J Biomed Phys Eng 2022; 12:611-626. [PMID: 36569564 PMCID: PMC9759642 DOI: 10.31661/jbpe.v0i0.2105-1334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 01/20/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA). OBJECTIVE This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. MATERIAL AND METHODS In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models. RESULTS A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. CONCLUSION The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.
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Affiliation(s)
- Mohammad Reza Afrash
- PhD, Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- PhD, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- PhD, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- PhD, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Rauseo M, Perrini M, Gallo C, Mirabella L, Mariano K, Ferrara G, Santoro F, Tullo L, La Bella D, Vetuschi P, Cinnella G. Machine learning and predictive models: 2 years of Sars-CoV-2 pandemic in a single-center retrospective analysis. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2022; 2:42. [PMID: 37386654 PMCID: PMC9568961 DOI: 10.1186/s44158-022-00071-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/03/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND Since January 2020, coronavirus disease 19 (COVID-19) has rapidly spread all over the world. An early assessment of illness severity is crucial for the stratification of patients in order to address them to the right intensity path of care. We performed an analysis on a large cohort of COVID-19 patients (n=581) hospitalized between March 2020 and May 2021 in our intensive care unit (ICU) at Policlinico Riuniti di Foggia hospital. Through an integration of the scores, demographic data, clinical history, laboratory findings, respiratory parameters, a correlation analysis, and the use of machine learning our study aimed to develop a model to predict the main outcome. METHODS We deemed eligible for analysis all adult patients (age >18 years old) admitted to our department. We excluded all the patients with an ICU length of stay inferior to 24 h and the ones that declined to participate in our data collection. We collected demographic data, medical history, D-dimers, NEWS2, and MEWS scores on ICU admission and on ED admission, PaO2/FiO2 ratio on ICU admission, and the respiratory support modalities before the orotracheal intubation and the intubation timing (early vs late with a 48-h hospital length of stay cutoff). We further collected the ICU and hospital lengths of stay expressed in days of hospitalization, hospital location (high dependency unit, HDU, ED), and length of stay before and after ICU admission; the in-hospital mortality; and the in-ICU mortality. We performed univariate, bivariate, and multivariate statistical analyses. RESULTS SARS-CoV-2 mortality was positively correlated to age, length of stay in HDU, MEWS, and NEWS2 on ICU admission, D-dimer value on ICU admission, early orotracheal intubation, and late orotracheal intubation. We found a negative correlation between the PaO2/FiO2 ratio on ICU admission and NIV. No significant correlations with sex, obesity, arterial hypertension, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, diabetes mellitus, dyslipidemia, and neither MEWS nor NEWS on ED admission were observed. Considering all the pre-ICU variables, none of the machine learning algorithms performed well in developing a prediction model accurate enough to predict the outcome although a secondary multivariate analysis focused on the ventilation modalities and the main outcome confirmed how the choice of the right ventilatory support with the right timing is crucial. CONCLUSION In our cohort of COVID patients, the choice of the right ventilatory support at the right time has been crucial, severity scores, and clinical judgment gave support in identifying patients at risk of developing a severe disease, comorbidities showed a lower weight than expected considering the main outcome, and machine learning method integration could be a fundamental statistical tool in the comprehensive evaluation of such complex diseases.
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Affiliation(s)
- Michela Rauseo
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy.
| | - Marco Perrini
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Crescenzio Gallo
- Department of Clinical and Experimental Medicine "InfoLab" Bioinformatics Facility Head, University Hospital "Policlinico Riuniti", Viale Pinto 1, 71122, Foggia, Italy
| | - Lucia Mirabella
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Karim Mariano
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Giuseppe Ferrara
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Filomena Santoro
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Livio Tullo
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Daniela La Bella
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Paolo Vetuschi
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
| | - Gilda Cinnella
- Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy
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Tirandi A, Ramoni D, Montecucco F, Liberale L. Predicting mortality in hospitalized COVID-19 patients. Intern Emerg Med 2022; 17:1571-1574. [PMID: 35704169 PMCID: PMC9198615 DOI: 10.1007/s11739-022-03017-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/23/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Amedeo Tirandi
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, 6 viale Benedetto XV, 16132, Genoa, Italy
| | - Davide Ramoni
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, 6 viale Benedetto XV, 16132, Genoa, Italy
| | - Fabrizio Montecucco
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, 6 viale Benedetto XV, 16132, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino Genova-Italian Cardiovascular Network, Genoa, Italy
| | - Luca Liberale
- First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, 6 viale Benedetto XV, 16132, Genoa, Italy.
- IRCCS Ospedale Policlinico San Martino Genova-Italian Cardiovascular Network, Genoa, Italy.
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Karsy M, Hunsaker JC, Hamrick F, Sanford MN, Breviu A, Couldwell WT, Horton D. A Retrospective Cohort Study Evaluating the Use of the Modified Early Warning Score to Improve Outcome Prediction in Neurosurgical Patients. Cureus 2022; 14:e28558. [PMID: 36185926 PMCID: PMC9517581 DOI: 10.7759/cureus.28558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction The modified early warning score (mEWS) has been used to identify decompensating patients in critical care settings, potentially leading to better outcomes and safer, more cost-effective patient care. We examined whether the admission or maximum mEWS of neurosurgical patients was associated with outcomes and total patient costs across neurosurgical procedures. Methods This retrospective cohort study included all patients hospitalized at a quaternary care hospital for neurosurgery procedures during 2019. mEWS were automatically generated during a patient’s hospitalization from data available in the electronic medical record. Primary and secondary outcome measures were the first mEWS at admission, maximum mEWS during hospitalization, length of stay (LOS), discharge disposition, mortality, cost of hospitalization, and patient biomarkers (i.e., white blood cell count, erythrocyte sedimentation rate, C-reactive protein, and procalcitonin). Results In 1,408 patients evaluated, a mean first mEWS of 0.5 ± 0.9 (median: 0) and maximum mEWS of 2.6 ± 1.4 (median: 2) were observed. The maximum mEWS was achieved on average one day (median = 0 days) after admission and correlated with other biomarkers (p < 0.0001). Scores correlated with continuous outcomes (i.e., LOS and cost) distinctly based on disease types. Multivariate analysis showed that the maximum mEWS was associated with longer stay (OR = 1.8; 95% CI = 1.6-1.96, p = 0.0001), worse disposition (OR = 0.82, 95% CI = 0.71-0.95, p = 0.0001), higher mortality (OR = 1.7; 95% CI = 1.3-2.1, p = 0.0001), and greater cost (OR = 1.2, 95% CI = 1.1-1.3, p = 0.001). Machine learning algorithms suggested that logistic regression, naïve Bayes, and neural networks were most predictive of outcomes. Conclusion mEWS was associated with outcomes in neurosurgical patients and may be clinically useful. The composite score could be integrated with other clinical factors and was associated with LOS, discharge disposition, mortality, and patient cost. mEWS also could be used early during a patient's admission to stratify risk. Increase in mEWS scores correlated with the outcome to a different degree in distinct patient/disease types. These results show the potential of the mEWS to predict outcomes in neurosurgical patients and suggest that it could be incorporated into clinical decision-making and/or monitoring of neurosurgical patients during admission. However, further studies and refinement of mEWS are needed to better integrate it into patient care.
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Prosepe I, Groenwold RHH, Knevel R, Pajouheshnia R, van Geloven N. The Disconnect Between Development and Intended Use of Clinical Prediction Models for Covid-19: A Systematic Review and Real-World Data Illustration. FRONTIERS IN EPIDEMIOLOGY 2022; 2:899589. [PMID: 38455309 PMCID: PMC10910889 DOI: 10.3389/fepid.2022.899589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/23/2022] [Indexed: 03/09/2024]
Abstract
Background The SARS-CoV-2 pandemic has boosted the appearance of clinical predictions models in medical literature. Many of these models aim to provide guidance for decision making on treatment initiation. Special consideration on how to account for post-baseline treatments is needed when developing such models. We examined how post-baseline treatment was handled in published Covid-19 clinical prediction models and we illustrated how much estimated risks may differ according to how treatment is handled. Methods Firstly, we reviewed 33 Covid-19 prognostic models published in literature in the period up to 5 May 2020. We extracted: (1) the reported intended use of the model; (2) how treatment was incorporated during model development and (3) whether the chosen analysis strategy was in agreement with the intended use. Secondly, we used nationwide Dutch data on hospitalized patients who tested positive for SARS-CoV-2 in 2020 to illustrate how estimated mortality risks will differ when using four different analysis strategies to model ICU treatment. Results Of the 33 papers, 21 (64%) had misalignment between intended use and analysis strategy, 7 (21%) were unclear about the estimated risk and only 5 (15%) had clear alignment between intended use and analysis strategy. We showed with real data how different approaches to post-baseline treatment yield different estimated mortality risks, ranging between 33 and 46% for a 75 year-old patient with two medical conditions. Conclusions Misalignment between intended use and analysis strategy is common in reported Covid-19 clinical prediction models. This can lead to considerable under or overestimation of intended risks.
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Affiliation(s)
- Ilaria Prosepe
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Rolf H. H. Groenwold
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
| | - Romin Pajouheshnia
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, Netherlands
| | - Nan van Geloven
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
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22
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Zeng H, He X, Liu W, Kan J, He L, Zhao J, Chen C, Zhang J, Chen S. A New Coronavirus Estimation Global Score for Predicting Mortality During Hospitalization in Patients with COVID-19. CARDIOLOGY DISCOVERY 2022; 2:69-76. [PMID: 36540720 PMCID: PMC9749948 DOI: 10.1097/cd9.0000000000000052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 02/15/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE Coronavirus disease 2019 (COVID-19) exists as a pandemic. Mortality during hospitalization is multifactorial, and there is urgent need for a risk stratification model to predict in-hospital death among COVID-19 patients. Here we aimed to construct a risk score system for early identification of COVID-19 patients at high probability of dying during in-hospital treatment. METHODS In this retrospective analysis, a total of 821 confirmed COVID-19 patients from 3 centers were assigned to developmental (n = 411, between January 14, 2020 and February 11, 2020) and validation (n = 410, between February 14, 2020 and March 13, 2020) groups. Based on demographic, symptomatic, and laboratory variables, a new Coronavirus estimation global (CORE-G) score for prediction of in-hospital death was established from the developmental group, and its performance was then evaluated in the validation group. RESULTS The CORE-G score consisted of 18 variables (5 demographics, 2 symptoms, and 11 laboratory measurements) with a sum of 69.5 points. Goodness-of-fit tests indicated that the model performed well in the developmental group (H = 3.210, P = 0.880), and it was well validated in the validation group (H = 6.948, P = 0.542). The areas under the receiver operating characteristic curves were 0.955 in the developmental group (sensitivity, 94.1%; specificity, 83.4%) and 0.937 in the validation group (sensitivity, 87.2%; specificity, 84.2%). The mortality rate was not significantly different between the developmental (n = 85,20.7%) and validation (n = 94, 22.9%, P = 0.608) groups. CONCLUSIONS The CORE-G score provides an estimate of the risk of in-hospital death. This is the first step toward the clinical use of the CORE-G score for predicting outcome in COVID-19 patients.
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Affiliation(s)
- Hesong Zeng
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China
| | - Xingwei He
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China
| | - Wanjun Liu
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China
| | - Jing Kan
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China
| | - Liqun He
- Division of Cardiology, Wuhan First Hospital, Wuhan, Hubei 430022, China
| | - Jinhe Zhao
- Division of Cardiology, Tianyou Hospital affiliated to Wuhan University of Science & Technology, Wuhan, Hubei 430064, China
| | - Cynthia Chen
- Mailman School of Public Health, Columbia University, New York, New York 10027, USA
| | - Junjie Zhang
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China
| | - Shaoliang Chen
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China
- College of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu 210002, China
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23
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Lee Y, Jehangir Q, Li P, Gudimella D, Mahale P, Lin CH, Apala DR, Krishnamoorthy G, Halabi AR, Patel K, Poisson L, Balijepally V, Sule AA, Nair GB. Venous thromboembolism in COVID-19 patients and prediction model: a multicenter cohort study. BMC Infect Dis 2022; 22:462. [PMID: 35562677 PMCID: PMC9100286 DOI: 10.1186/s12879-022-07421-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/25/2022] [Indexed: 12/22/2022] Open
Abstract
Background Patients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in the prevention, early identification, and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients.
Method This is a multicenter, retrospective database of four main health systems in Southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models, including the random forest, multiple logistic regression, multilinear regression, and decision trees, were built on the primary outcome of in-hospital acute deep vein thrombosis (DVT) and pulmonary embolism (PE) and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using the area under the receiver operating characteristic curve and confusion matrix.
Results The cohort included 3531 admissions, 3526 had discharge diagnoses, and 6.68% of patients developed acute VTE (N = 236). VTE group had a longer hospital and ICU LOS than the non-VTE group (hospital LOS 12.2 days vs. 8.8 days, p < 0.001; ICU LOS 3.8 days vs. 1.9 days, p < 0.001). 9.8% of patients in the VTE group required more advanced oxygen support, compared to 2.7% of patients in the non-VTE group (p < 0.001). Among all four models, the random forest model had the best performance. The model suggested that blood pressure, electrolytes, renal function, hepatic enzymes, and inflammatory markers were predictors for in-hospital VTE in COVID-19 patients. Conclusions Patients with COVID-19 have a high risk for VTE, and patients who developed VTE had a prolonged hospital and ICU stay. This random forest prediction model for VTE in COVID-19 patients identifies predictors which could aid physicians in making a clinical judgment on empirical dosages of anticoagulation. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07421-3.
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Affiliation(s)
- Yi Lee
- Department of Medicine, St. Joseph Mercy Oakland Hospital, 44405 Woodward Avenue, Pontiac, MI, 48341, USA.
| | - Qasim Jehangir
- Department of Medicine, St. Joseph Mercy Oakland Hospital, 44405 Woodward Avenue, Pontiac, MI, 48341, USA
| | - Pin Li
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | - Deepthi Gudimella
- School of Business Administration, Oakland University, Rochester, MI, USA
| | - Pooja Mahale
- School of Business Administration, Oakland University, Rochester, MI, USA
| | - Chun-Hui Lin
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | - Dinesh R Apala
- Division of Cardiology, St. Joseph Mercy Oakland Hospital, Pontiac, MI, USA
| | - Geetha Krishnamoorthy
- Department of Medicine, St. Joseph Mercy Oakland Hospital, 44405 Woodward Avenue, Pontiac, MI, 48341, USA
| | - Abdul R Halabi
- Division of Cardiology, St. Joseph Mercy Oakland Hospital, Pontiac, MI, USA.,Oakland University William Beaumont School of Medicine, Auburn Hills, MI, USA
| | - Kiritkumar Patel
- Division of Cardiology, St. Joseph Mercy Oakland Hospital, Pontiac, MI, USA
| | - Laila Poisson
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | | | - Anupam A Sule
- Department of Medicine, St. Joseph Mercy Oakland Hospital, 44405 Woodward Avenue, Pontiac, MI, 48341, USA.,Department of Informatics, St. Joseph Mercy Oakland Hospital, Pontiac, MI, USA
| | - Girish B Nair
- Oakland University William Beaumont School of Medicine, Auburn Hills, MI, USA.,Division of Pulmonary and Critical Care Medicine, Beaumont Hospital, Royal Oak, MI, USA
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Degarege A, Naveed Z, Kabayundo J, Brett-Major D. Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis. Pathogens 2022; 11:563. [PMID: 35631084 PMCID: PMC9147100 DOI: 10.3390/pathogens11050563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 02/07/2023] Open
Abstract
This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.
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Affiliation(s)
- Abraham Degarege
- Department of Epidemiology, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA; (Z.N.); (J.K.); (D.B.-M.)
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Predictive Ability of the MEWS, REMS, and RAPS in Geriatric Patients With SARS-CoV-2 Infection in the Emergency Department. Disaster Med Public Health Prep 2022; 17:e174. [PMID: 35492014 PMCID: PMC9253434 DOI: 10.1017/dmp.2022.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The aim of this study was to compare the ability of the Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), and Rapid Acute Physiology Score (RAPS) to predict 30-d mortality in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection aged 65 y and over. METHODS This prospective, single-center, observational study was carried out with 122 volunteers aged 65 y and over with patients confirmed to have SARS-CoV-2 infection according to the reverse transcriptase-polymerase chain reaction (RT-PCR) test, who presented to the emergency department between March 1, 2020, and May 1, 2020. Demographic data, comorbidities, vital parameters, hematological parameters, and MEWS, REMS, and RAPS values of the patients were recorded prospectively. RESULTS Among the 122 patients included in the study, the median age was 71 (25th-75th quartile: 67-79) y. The rate of 30-d mortality was 10.7% for the study cohort. The area under the receiver operating characteristic curve values for MEWS, RAPS, and REMS were 0.512 (95% confidence interval [CI]: 0.420-0.604; P = 0.910), 0.500 (95% CI: 0.408-0.592; P = 0.996), and 0.675 (95% CI: 0.585-0.757; P = 0.014), respectively. The odds ratios of MEWS (≥2), RAPS (>2), and REMS (>5) for 30-d mortality were 0.374 (95% CI: 0.089-1.568; P = 0.179), 1.696 (95% CI: 0.090-31.815; P = 0.724), and 1.008 (95% CI: 0.257-3.948; P = 0.991), respectively. CONCLUSIONS REMS, RAPS, and MEWS do not seem to be useful in predicting 30-d mortality in geriatric patients with SARS-CoV-2 infection presenting to the emergency department.
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26
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Ng SM, Pan J, Mouyis K, Kondapally Seshasai SR, Kapil V, Rice KM, Gupta AK. Quantifying the Excess Risk of Adverse COVID-19 Outcomes in Unvaccinated Individuals With Diabetes Mellitus, Hypertension, Ischaemic Heart Disease or Myocardial Injury: A Meta-Analysis. Front Cardiovasc Med 2022; 9:871151. [PMID: 35557537 PMCID: PMC9090337 DOI: 10.3389/fcvm.2022.871151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/01/2022] [Indexed: 12/03/2022] Open
Abstract
Background More than 80% of individuals in low and middle-income countries (LMICs) are unvaccinated against coronavirus disease 2019 (COVID-19). In contrast, the greatest burden of cardiovascular disease is seen in LMIC populations. Hypertension (HTN), diabetes mellitus (DM), ischaemic heart disease (IHD) and myocardial injury have been variably associated with adverse COVID-19 outcomes. A systematic comparison of their impact on specific COVID-19 outcomes is lacking. We quantified the impact of DM, HTN, IHD and myocardial injury on six adverse COVID-19 outcomes: death, acute respiratory distress syndrome (ARDS), invasive mechanical ventilation (IMV), admission to intensive care (ITUadm), acute kidney injury (AKI) and severe COVID-19 disease (SCov), in an unvaccinated population. Methodology We included studies published between 1st December 2019 and 16th July 2020 with extractable data on patients ≥18 years of age with suspected or confirmed SARS-CoV-2 infection. Odds ratios (OR) for the association between DM, HTN, IHD and myocardial injury with each of six COVID-19 outcomes were measured. Results We included 110 studies comprising 48,809 COVID-19 patients. Myocardial injury had the strongest association for all six adverse COVID-19 outcomes [death: OR 8.85 95% CI (8.08–9.68), ARDS: 5.70 (4.48–7.24), IMV: 3.42 (2.92–4.01), ITUadm: 4.85 (3.94–6.05), AKI: 10.49 (6.55–16.78), SCov: 5.10 (4.26–6.05)]. HTN and DM were also significantly associated with death, ARDS, ITUadm, AKI and SCov. There was substantial heterogeneity in the results, partly explained by differences in age, gender, geographical region and recruitment period. Conclusion COVID-19 patients with myocardial injury are at substantially greater risk of death, severe disease and other adverse outcomes. Weaker, yet significant associations are present in patients with HTN, DM and IHD. Quantifying these associations is important for risk stratification, resource allocation and urgency in vaccinating these populations. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, registration no: CRD42020201435 and CRD42020201443.
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Affiliation(s)
- Sher May Ng
- St. Bartholomew's Hospital, London, United Kingdom
| | - Jiliu Pan
- Royal Brompton and Harefield Hospitals, London, United Kingdom
| | - Kyriacos Mouyis
- Royal Free London NHS Foundation Trust, London, United Kingdom
| | - Sreenivasa Rao Kondapally Seshasai
- Cardiovascular Clinical Academic Group, Molecular and Clinical Sciences Research Institute, St. George's University of London, St. George's University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Vikas Kapil
- William Harvey Research Institute, Queen Mary University London, London, United Kingdom
| | - Kenneth M. Rice
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Ajay K. Gupta
- William Harvey Research Institute, Queen Mary University London, London, United Kingdom
- *Correspondence: Ajay K. Gupta
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27
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Feng A. A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:448-456. [PMID: 35309012 PMCID: PMC8861740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imaging after COVID-19 diagnosis. In this project, we developed an innovative supervised machine learning pipeline using longitudinal Electronic Health Records (EHR) to accurately predict COVID-19 related health outcomes including mortality, ventilation, days in hospital or ICU. In particular, we developed unique and effective data processing algorithms, including data cleaning, initial feature screening, vector representation. Then we trained models using state-of-the-art machine learning strategies combined with different parameter settings. Based on routinely collected EHR, our machine learning pipeline not only consistently outperformed those developed by other research groups using the same set of data, but also achieved similar accuracy as those trained on medical data that were only available after COVID-19 diagnosis. In addition, top risk factors for COVID-19 were identified, and are consistent with epidemiologic findings.
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Fabião J, Sassi B, Pedrollo E, Gerchman F, Kramer C, Leitão C, Pinto L. Why do men have worse COVID-19-related outcomes? A systematic review and meta-analysis with sex adjusted for age. Braz J Med Biol Res 2022; 55:e11711. [PMID: 35195196 PMCID: PMC8856598 DOI: 10.1590/1414-431x2021e11711] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/19/2021] [Indexed: 01/09/2023] Open
Abstract
We aimed to study the mechanism behind worse coronavirus disease-19 (COVID-19) outcomes in men and whether the differences between sexes regarding mortality as well as disease severity are influenced by sex hormones. To do so, we used age as a covariate in the meta-regression and subgroup analyses. This was a systematic search and meta-analysis of observational cohorts reporting COVID-19 outcomes. The PubMed (Medline) and Cochrane Library databases were searched. The primary outcome was COVID-19-associated mortality and the secondary outcome was COVID-19 severity. The study was registered at PROSPERO: 42020182924. For mortality, men had a relative risk of 1.36 (95%CI: 1.17 to 1.59; I2 63%, P for heterogeneity <0.01) compared to women. Age was not a significant covariate in meta-analysis heterogeneity (P=0.393) or subgroup analysis. For disease severity, being male was associated with a relative risk of 1.29 (95%CI: 1.19 to 1.40; I2 48%, P for heterogeneity <0.01) compared to the relative risk of women. Again, age did not influence the outcomes of the meta-regression (P=0.914) or subgroup analysis. Men had a higher risk of COVID-19 mortality and severity regardless of age, decreasing the odds of hormonal influences in the described outcomes.
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Affiliation(s)
- J. Fabião
- Divisão de Medicina Interna, Hospital Nossa Senhora da
Conceição, Porto Alegre, RS, Brasil
| | - B. Sassi
- Divisão de Medicina Interna, Hospital Nossa Senhora da
Conceição, Porto Alegre, RS, Brasil
| | - E.F. Pedrollo
- Programa de Pós-Graduação em Ciências Médicas: Endocrinologia,
Divisão de Endocrinologia, Hospital de Clínicas de Porto Alegre, Universidade
Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil
| | - F. Gerchman
- Programa de Pós-Graduação em Ciências Médicas: Endocrinologia,
Divisão de Endocrinologia, Hospital de Clínicas de Porto Alegre, Universidade
Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil
| | - C.K. Kramer
- Mount Sinai Hospital, University of Toronto, Toronto, Ontario,
Canada
| | - C.B. Leitão
- Programa de Pós-Graduação em Ciências Médicas: Endocrinologia,
Divisão de Endocrinologia, Hospital de Clínicas de Porto Alegre, Universidade
Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil
| | - L.C. Pinto
- Divisão de Medicina Interna, Hospital Nossa Senhora da
Conceição, Porto Alegre, RS, Brasil
- Programa de Pós-Graduação em Ciências Médicas: Endocrinologia,
Divisão de Endocrinologia, Hospital de Clínicas de Porto Alegre, Universidade
Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil
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Caruso D, Zerunian M, Polici M, Pucciarelli F, Guido G, Polidori T, Rucci C, Bracci B, Tremamunno G, Laghi A. Diagnostic performance of CT lung severity score and quantitative chest CT for stratification of COVID-19 patients. Radiol Med 2022; 127:309-317. [PMID: 35157241 PMCID: PMC8852873 DOI: 10.1007/s11547-022-01458-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 01/21/2022] [Indexed: 12/23/2022]
Abstract
Purpose Lung severity score (LSS) and quantitative chest CT (QCCT) analysis could have a relevant impact to stratify patients affected by COVID-19 pneumonia at the hospital admission. The study aims to assess LSS and QCCT performances in severity stratification of COVID-19 patients. Materials and methods From April 19, 2020, until May 3, 2020, patients with chest CT suggestive for interstitial pneumonia and tested positive for COVID-19 were retrospectively enrolled and stratified for hospital admission as Group 1, 2 and 3 (home isolation, low intensive care and intensive care, respectively). For LSS, lungs were divided in 20 regions and visually assessed by two radiologists who scored for each region from non-lung involvement as 0, < 50% assigned as 1 and > 50% as 2. QCCT was performed with a dedicated software that extracts pulmonary involvement expressed in liters and percentage. LSS and QCCT were analyzed with ROC curve analysis to predict the performance of both methods. P values < 0.05 were considered statistically significant. Results Final population enrolled included 136 patients (87 males, mean age 66 ± 16), 19 patients in Group 1, 86 in Group 2 and 31 in Group 3. Significant differences for LSS were observed in almost all comparisons, especially in Group 1 vs 3 (AUC 0.850, P < 0,0001) and Group 1 + 2 vs 3 (AUC 0.783, P < 0,0001). QCCT showed significant results in almost all comparisons, especially between Group 1 vs 3 (AUC 0.869, P < 0,0001). LSS and QCCT comparison between Group 1 and Group 2 did not show significant differences. Conclusions LSS and QCCT could represent promising tools to stratify COVID-19 patient severity at the admission.
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Affiliation(s)
- Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Gisella Guido
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Tiziano Polidori
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Carlotta Rucci
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Benedetta Bracci
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Giuseppe Tremamunno
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
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Lupei MI, Li D, Ingraham NE, Baum KD, Benson B, Puskarich M, Milbrandt D, Melton GB, Scheppmann D, Usher MG, Tignanelli CJ. A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19. PLoS One 2022; 17:e0262193. [PMID: 34986168 PMCID: PMC8730444 DOI: 10.1371/journal.pone.0262193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/20/2021] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.
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Affiliation(s)
- Monica I. Lupei
- Division of Critical Care, Department of Anesthesiology, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Danni Li
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Nicholas E. Ingraham
- Division of Pulmonary and Critical Care, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Karyn D. Baum
- Division of General Internal Medicine, Department of Medicine, Section of Hospital Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Bradley Benson
- Division of General Internal Medicine, Department of Medicine, Section of Hospital Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Michael Puskarich
- Department of Emergency Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - David Milbrandt
- Department of Emergency Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Genevieve B. Melton
- Department of Surgery, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Daren Scheppmann
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Michael G. Usher
- Division of General Internal Medicine, Department of Medicine, Section of Hospital Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Christopher J. Tignanelli
- Department of Surgery, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States of America
- Division of Critical Care and Acute Care Surgery, Department of Surgery, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
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Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Med Inform Decis Mak 2022; 22:2. [PMID: 34983496 PMCID: PMC8724649 DOI: 10.1186/s12911-021-01742-0] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/28/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.
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Affiliation(s)
- Khadijeh Moulaei
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Zahra Mohammadi-Taghiabad
- Department of Health Information Management, School of Health Management and Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
- Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran.
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Yang H, Fan Y, Zhu Z, Wu H, Chen Z, Hu X, Wu T, Zhang M. Strategies for the Emergency Treatment of Pregnant Women with Neurological Symptoms during the COVID-19 Pandemic. Aging Dis 2022; 14:290-298. [PMID: 37008058 PMCID: PMC10017149 DOI: 10.14336/ad.2022.0718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Coronavirus disease-19 (COVID-19) has been spreading all over the world for more than two years. Though several kinds of vaccines are currently available, emergence of new variants, spike mutations and immune escape have raised new challenges. Pregnant women are vulnerable to respiratory infections due to their altered immune defence and surveillance functions. Besides, whether pregnant persons should receive a COVID-19 vaccine is still under debate because limited data are available on the efficacy and safety of receiving a vaccine during pregnancy. Physiological features and lack of effective protection making pregnant women at high risk of getting infected. Another concern is that pregnancy may trigger the onset of underlying existing neurological disease, which is highly similar to those neurological symptoms of pregnant women caused by COVID-19. These similarities interfere with diagnosis and delay timely and effective management. Therefore, providing efficient emergency support for pregnant women suffering from neurological symptoms caused by COVID-19 remains a challenge among neurologists and obstetricians. To improve the diagnosis and treatment efficiency of pregnant women with neurological symptoms, we propose an emergency management framework based on the clinicians' experience and available resources. This emergency care system aimed at addressing the conundrums faced by the emergency guarantee system under COVID-19 pandemic and could serve as a potential multisystem project for clinical practice and medical education.
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Affiliation(s)
- Haojun Yang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yishu Fan
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ziqing Zhu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Haiyue Wu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhuohui Chen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xinhang Hu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Tong Wu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Mengqi Zhang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Correspondence should be addressed to: Dr. Mengqi Zhang, Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China. .
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Boussen S, Cordier PY, Malet A, Simeone P, Cataldi S, Vaisse C, Roche X, Castelli A, Assal M, Pepin G, Cot K, Denis JB, Morales T, Velly L, Bruder N. Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning. Comput Biol Med 2021; 142:105192. [PMID: 34998220 PMCID: PMC8719000 DOI: 10.1016/j.compbiomed.2021.105192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/27/2021] [Accepted: 12/27/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals. METHODS We recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay. RESULTS Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). CONCLUSIONS Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.
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Affiliation(s)
- Salah Boussen
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France; Aix Marseille Université, IFSTTAR, LBA UMR_T 24, 13916, Marseille, France.
| | - Pierre-Yves Cordier
- Aix Marseille Université, IFSTTAR, LBA UMR_T 24, 13916, Marseille, France; Intensive Care Unit, Laveran Military Teaching Hospital, 34, boulevard Laveran, 13384, Marseille, France
| | - Arthur Malet
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Pierre Simeone
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France; Institut des Neurociences de la Timone, CNRS UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
| | - Sophie Cataldi
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Camille Vaisse
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Xavier Roche
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Alexandre Castelli
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Mehdi Assal
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Guillaume Pepin
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Kevin Cot
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Jean-Baptiste Denis
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Timothée Morales
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - Lionel Velly
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France; Aix Marseille Université, IFSTTAR, LBA UMR_T 24, 13916, Marseille, France; Intensive Care Unit, Laveran Military Teaching Hospital, 34, boulevard Laveran, 13384, Marseille, France; Institut des Neurociences de la Timone, CNRS UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
| | - Nicolas Bruder
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
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van Dam PMEL, Zelis N, van Kuijk SMJ, Linkens AEMJH, Brüggemann RAG, Spaetgens B, van der Horst ICC, Stassen PM. Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study. Ann Med 2021; 53:402-409. [PMID: 33629918 PMCID: PMC7919920 DOI: 10.1080/07853890.2021.1891453] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 02/12/2021] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). METHODS In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). RESULTS We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. CONCLUSION The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.
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Affiliation(s)
- Paul M. E. L. van Dam
- Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Noortje Zelis
- Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sander M. J. van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Aimée E. M. J. H. Linkens
- Department of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Renée A. G. Brüggemann
- Department of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Bart Spaetgens
- Department of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Iwan C. C. van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Patricia M. Stassen
- Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
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Nopour R, Kazemi-Arpanahi H, Shanbehzadeh M, Azizifar A. Performance analysis of data mining algorithms for diagnosing COVID-19. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2021; 10:405. [PMID: 35071611 PMCID: PMC8719570 DOI: 10.4103/jehp.jehp_138_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 02/22/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND An outbreak of atypical pneumonia termed COVID-19 has widely spread all over the world since the beginning of 2020. In this regard, designing a prediction system for the early detection of COVID-19 is a critical issue in mitigating virus spread. In this study, we have applied selected machine learning techniques to select the best predictive models based on their performance. MATERIALS AND METHODS The data of 435 suspicious cases with COVID-19 which were recorded from the Imam Khomeini Hospital database between May 9, 2020 and December 20, 2020, have been taken into consideration. The Chi-square method was used to determine the most important features in diagnosing the COVID-19; eight selected data mining algorithms including multilayer perceptron (MLP), J-48, Bayesian Net (Bayes Net), logistic regression, K-star, random forest, Ada-boost, and sequential minimal optimization (SMO) were applied in data mining. Finally, the most appropriate diagnostic model for COVID-19 was obtained based on comparing the performance of the selected algorithms. RESULTS As the result of using the Chi-square method, 21 variables were identified as the most important diagnostic criteria in COVID-19. The results of evaluating the eight selected data mining algorithms showed that the J-48 with true-positive rate = 0.85, false-positive rate = 0.173, precision = 0.85, recall = 0.85, F-score = 0.85, Matthews Correlation Coefficient = 0.68, and area under the receiver operator characteristics = 0.68, respectively, had the higher performance than the other algorithms. CONCLUSION The results of evaluating the performance criteria showed that the J-48 can be considered as a suitable computational prediction model for diagnosing COVID-19 disease.
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Affiliation(s)
- Raoof Nopour
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- Student Research Committee Department, Abadan University of Medical Sciences, Abadan, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Akbar Azizifar
- Department of English Language, School of Medicine, Ilam University of Medical Science, Ilam, Iran
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Zeng J, Qi XX, Cai WW, Pan YP, Xie Y. Retrospective analysis of influencing factors on the efficacy of mechanical ventilation in severe and critical COVID-19 patients. World J Clin Cases 2021; 9:9481-9490. [PMID: 34877282 PMCID: PMC8610873 DOI: 10.12998/wjcc.v9.i31.9481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/28/2021] [Accepted: 08/25/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The novel coronavirus disease 2019 (COVID-19) has spread widely around the world with strong infectivity, rapid mutation and a high mortality rate. Mechanical ventilation has been included in the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 8) as an important treatment for severe and critical COVID-19 patients, but its clinical efficacy in COVID-19 patients is various. Therefore, it is necessary to study the influencing factors on the efficacy of mechanical ventilation in severe and critical COVID-19 patients. AIM The aim of this study was to determine the influencing factors on the efficacy of mechanical ventilation in severe and critical COVID-19 patients. METHODS A total of 27 severe and critical COVID-19 patients were enrolled in this study and treated with mechanical ventilation at the Optical Valley Campus of Hubei Maternal and Child Health Care Hospital (Wuhan, Hubei Province) from February 20, 2020 to April 5, 2020. According to the final treatment outcomes, the patients were divided into the "effective group" and "death group." The clinical data of the two groups, such as the treatment process and final outcome, were retrospectively analyzed in order to determine the specific curative effects on the two groups and the reasons for the differences in such curative effects, as well as to explore the factors related to death. RESULTS This study enrolled 27 severe and critical COVID-19 patients, including 17 males (63.0%) and 10 females (37.0%). Their ages were 74.41 ± 11.73-years-old, and 19 patients (70.4%) were over 70-years-old. Severe COVID-19 patients over 70-years-old who were treated with mechanical ventilation died in 14 cases (82.4%); thus, this was the peak age. A total of 17 patients died of basic disease, 16 of whom had more than two basic diseases. The basic diseases were hypertension, diabetes, and cardiovascular and cerebrovascular diseases. At the same time, 13 patients (76.5%) died from an abnormal increase in blood glucose. Among them, eight had diabetes before contracting COVID-19 and five had a stress-induced increase in blood glucose after contracting COVID-19. Diabetic ketoacidosis occurred in one case. The use of tocilizumab may be a double-edged sword that carries a certain risk in clinical usage. Among the patients who died, 16 (94.1%) went into septic shock at the end. There were significant differences in the degree of infection, cardiac and renal function, and blood glucose between the death group and effective group. CONCLUSION Age, blood glucose, cardiac and renal function, and inflammatory reaction are important indicators of poor prognosis for mechanical ventilation in severe and critical COVID-19 patients.
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Affiliation(s)
- Jia Zeng
- Department of Aviation Disease, Naval Medical Center of PLA, Shanghai 200052, China
- No. 1 Department of Infection, Optical Valley Campus of Hubei Maternal and Child Health Care Hospital, Wuhan 430073, Hubei Province, China
| | - Xiao-Xia Qi
- Department of Traditional Chinese Medicine, Naval Medical Center of PLA, Shanghai 200052, China
| | - Wan-Wan Cai
- Department of Naval Diving Medicine, Naval Medical Center of PLA, Shanghai 200052, China
| | - Ya-Ping Pan
- Traditional Chinese Medicine, Naval Medical Center of PLA, Shanghai 200052, China
| | - Yi Xie
- Department of Hepatobiliary Surgery, Naval Medical Center of PLA, Shanghai 200052, China
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van Rensen IHT, Hensgens KRC, Lekx AW, van Osch FHM, Knarren LHH, van Kampen-van den Boogaart VEM, Mehagnoul-Schipper JDJ, Wyers CE, van den Bergh JP, Barten DG. Early detection of hospitalized patients with COVID-19 at high risk of clinical deterioration: Utility of emergency department shock index. Am J Emerg Med 2021; 49:76-79. [PMID: 34087575 PMCID: PMC8137356 DOI: 10.1016/j.ajem.2021.05.049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 05/02/2021] [Accepted: 05/16/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The COVID-19 outbreak has put an unprecedented strain on Emergency Departments (EDs) and other critical care resources. Early detection of patients that are at high risk of clinical deterioration and require intensive monitoring, is key in ED evaluation and disposition. A rapid and easy risk-stratification tool could aid clinicians in early decision making. The Shock Index (SI: heart rate/systolic blood pressure) proved useful in detecting hemodynamic instability in sepsis and myocardial infarction patients. In this study we aim to determine whether SI is discriminative for ICU admission and in-hospital mortality in COVID-19 patients. METHODS Retrospective, observational, single-center study. All patients ≥18 years old who were hospitalized with COVID-19 (defined as: positive result on reverse transcription polymerase chain reaction (PCR) test) between March 1, 2020 and December 31, 2020 were included for analysis. Data were collected from electronic medical patient records and stored in a protected database. ED shock index was calculated and analyzed for its discriminative value on in-hospital mortality and ICU admission by a ROC curve analysis. RESULTS In total, 411 patients were included. Of all patients 249 (61%) were male. ICU admission was observed in 92 patients (22%). Of these, 37 patients (40%) died in the ICU. Total in-hospital mortality was 28% (114 patients). For in-hospital mortality the optimal cut-off SI ≥ 0.86 was not discriminative (AUC 0.49 (95% CI: 0.43-0.56)), with a sensitivity of 12.3% and specificity of 93.6%. For ICU admission the optimal cut-off SI ≥ 0.57 was also not discriminative (AUC 0.56 (95% CI: 0.49-0.62)), with a sensitivity of 78.3% and a specificity of 34.2%. CONCLUSION In this cohort of patients hospitalized with COVID-19, SI measured at ED presentation was not discriminative for ICU admission and was not useful for early identification of patients at risk of clinical deterioration.
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Affiliation(s)
- Inge H T van Rensen
- VieCuri Medical Center, Department of Emergency Medicine, Venlo, the Netherlands.
| | - Kirsten R C Hensgens
- VieCuri Medical Center, Department of Emergency Medicine, Venlo, the Netherlands; VieCuri Medical Center, Intensive Care Unit, Venlo, the Netherlands.
| | - Anita W Lekx
- VieCuri Medical Center, Department of Emergency Medicine, Venlo, the Netherlands.
| | - Frits H M van Osch
- VieCuri Medical Center, Department of Epidemiology, Venlo, the Netherlands; Maastricht University Medical Center, School of Nutrition and Metabolism (NUTRIM), Maastricht, the Netherlands.
| | - Lieve H H Knarren
- VieCuri Medical Center, Department of Internal Medicine, Venlo, the Netherlands.
| | | | | | - Caroline E Wyers
- Maastricht University Medical Center, School of Nutrition and Metabolism (NUTRIM), Maastricht, the Netherlands; VieCuri Medical Center, Department of Internal Medicine, Venlo, the Netherlands; Maastricht University Medical Center, Department of Internal Medicine, Maastricht, the Netherlands.
| | - Joop P van den Bergh
- Maastricht University Medical Center, School of Nutrition and Metabolism (NUTRIM), Maastricht, the Netherlands; VieCuri Medical Center, Department of Internal Medicine, Venlo, the Netherlands; Maastricht University Medical Center, Department of Internal Medicine, Maastricht, the Netherlands.
| | - Dennis G Barten
- VieCuri Medical Center, Department of Emergency Medicine, Venlo, the Netherlands.
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Huang SH, Hsieh MS, Hu SY, Huang SC, Tsai CA, Hsu CY, Lin TC, Lee YC, Liao SH. Performance of Scoring Systems in Predicting Clinical Outcomes in Patients with Bacteremia of Listeria monocytogenes: A 9-Year Hospital-Based Study. BIOLOGY 2021; 10:1073. [PMID: 34827066 PMCID: PMC8615254 DOI: 10.3390/biology10111073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Listeria monocytogenes (LM) is a facultative anaerobe, Gram-positive bacillus which is widely distributed in nature, and can be separated from soil, water, and rotten vegetables. Immunocompetent people are less likely to suffer from LM infection or may only show gastrointestinal symptoms. However, immunocompromised elderly people, pregnant women, and newborns may develop life-threatening invasive infections. The mortality rate of LM infection is as high as 25-30%. The aim of this study is to investigate clinical scores of patients with bacteremia of LM confirmed by one or more blood cultures. We analyzed their demographics and laboratory findings in relation to their clinical outcomes. MATERIALS AND METHODS This was a hospital-based retrospective study on patients with bacteremia of LM. Data were collected from the electronic clinical database of Taichung Veterans General Hospital between January 2012 and December 2020. Bacteremia of LM was confirmed by at least one blood culture. Demographics, clinical characteristics, and laboratory data were collected for analysis. A variety of clinical scoring systems were used to predict the clinical outcome. RESULTS A total of 39 patients had confirmed bacteremia of LM. Among them, 1 neonatal patient was excluded. The remaining 38 patients were studied. They included 16 males (42.1%) and 22 females (57.9%), with a mean age of 59.9 ± 19.6 years. Their hospital stay averaged 23.3 ± 20.9 days. The in-hospital mortality rate was 36.8%. Mortality in Emergency Department Sepsis (MEDS) Score was 6.6 ± 4.0 for survivors and 12.4 ± 4.4 for non-survivors (P < 0.001). The National Early Warning Score (NEWS) was 3.9 ± 2.8 for survivors and 7.8 ± 3.1 for non-survivors (P = 0.001). Regarding the prediction of mortality risk, the AUC of ROC was 0.829 for MEDS and 0.815 for NEWS. CONCLUSIONS MEDS and NEWS were both good predictors of the clinical outcome in LM bacteremic patients. In those with higher scores of MEDS (≥10) and NEWS (≥8), we recommended an early goal-directed therapy and appropriate antibiotic treatment as early as possible to reduce mortality. Further large-scale studies are required to gain a deeper understanding of this disease and to ensure patient safety.
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Affiliation(s)
- Shang-Hsuan Huang
- Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan; (S.-H.H.); (T.-C.L.)
- Department of Emergency Medicine, Taichung Armed Forces General Hospital, Taichung 40466, Taiwan
| | - Ming-Shun Hsieh
- Department of Emergency Medicine, Taipei Veterans General Hospital, Taoyuan Branch, Taoyuan 330, Taiwan; (M.-S.H.); (Y.-C.L.)
- Department of Emergency Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Sung-Yuan Hu
- Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan; (S.-H.H.); (T.-C.L.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- School of Medicine, National Chung Hsing University, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Nursing, College of Health, National Taichung University of Science and Technology, Taichung 404, Taiwan
| | - Shih-Che Huang
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
- Department of Emergency Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Lung Cancer Research Center, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Che-An Tsai
- Division of Infectious Disease, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - Chiann-Yi Hsu
- Biostatistics Task Force, Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - Tzu-Chieh Lin
- Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan; (S.-H.H.); (T.-C.L.)
- School of Medicine, National Chung Hsing University, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Yi-Chen Lee
- Department of Emergency Medicine, Taipei Veterans General Hospital, Taoyuan Branch, Taoyuan 330, Taiwan; (M.-S.H.); (Y.-C.L.)
| | - Shu-Hui Liao
- Department of Pathology and Laboratory, Taipei Veterans General Hospital, Taoyuan Branch, Taoyuan 330, Taiwan;
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Lombardi Y, Azoyan L, Szychowiak P, Bellamine A, Lemaitre G, Bernaux M, Daniel C, Leblanc J, Riller Q, Steichen O. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals. Intensive Care Med 2021; 47:1426-1439. [PMID: 34585270 PMCID: PMC8478265 DOI: 10.1007/s00134-021-06524-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 08/30/2021] [Indexed: 12/21/2022]
Abstract
Purpose The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19. Methods We used “COVID-19 Evidence Alerts” (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. Results We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome. Conclusion Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06524-w.
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Affiliation(s)
- Yannis Lombardi
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Loris Azoyan
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Piotr Szychowiak
- Médecine Intensive-Réanimation, Centre Hospitalier Régional Universitaire de Tours, Tours, France.,Université de Tours, Tours, France
| | | | | | - Mélodie Bernaux
- Strategy and Transformation Department, AP-HP, Paris, France
| | | | - Judith Leblanc
- Institut Pierre Louis d'Épidémiologie et de Santé Publique, UMR-S 1136 , Sorbonne Université, INSERM, Paris, France.,Clinical Research Platform, Saint Antoine Hospital, AP-HP, Paris, France
| | - Quentin Riller
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Olivier Steichen
- Institut Pierre Louis d'Épidémiologie et de Santé Publique, UMR-S 1136 , Sorbonne Université, INSERM, Paris, France. .,Internal Medicine Department, Tenon Hospital, AP-HP, Sorbonne Université, Paris, France. .,Service de Médecine Interne, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France.
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Kuroda S, Matsumoto S, Sano T, Kitai T, Yonetsu T, Kohsaka S, Torii S, Kishi T, Komuro I, Hirata KI, Node K, Matsue Y. External validation of the 4C Mortality Score for patients with COVID-19 and pre-existing cardiovascular diseases/risk factors. BMJ Open 2021; 11:e052708. [PMID: 34497086 PMCID: PMC8438580 DOI: 10.1136/bmjopen-2021-052708] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Predictive algorithms to inform risk management decisions are needed for patients with COVID-19, although the traditional risk scores have not been adequately assessed in Asian patients. We aimed to evaluate the performance of a COVID-19-specific prediction model, the 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score, along with other conventional critical care risk models in Japanese nationwide registry data. DESIGN Retrospective cohort study. SETTING AND PARTICIPANTS Hospitalised patients with COVID-19 and cardiovascular disease or coronary risk factors from January to May 2020 in 49 hospitals in Japan. MAIN OUTCOME MEASURES Two different types of outcomes, in-hospital mortality and a composite outcome, defined as the need for invasive mechanical ventilation and mortality. RESULTS The risk scores for 693 patients were tested by predicting in-hospital mortality for all patients and composite endpoint among those not intubated at baseline (n=659). The number of events was 108 (15.6%) for mortality and 178 (27.0%) for composite endpoints. After missing values were multiply imputed, the performance of the 4C Mortality Score was assessed and compared with three prediction models that have shown good discriminatory ability (RISE UP score, A-DROP score and the Rapid Emergency Medicine Score (REMS)). The area under the receiver operating characteristic curve (AUC) for the 4C Mortality Score was 0.84 (95% CI 0.80 to 0.88) for in-hospital mortality and 0.78 (95% CI 0.74 to 0.81) for the composite endpoint. It showed greater discriminatory ability compared with other scores, except for the RISE UP score, for predicting in-hospital mortality (AUC: 0.82, 95% CI 0.78 to 0.86). Similarly, the 4C Mortality Score showed a positive net reclassification improvement index over the A-DROP and REMS for mortality and over all three scores for the composite endpoint. The 4C Mortality Score model showed good calibration, regardless of outcome. CONCLUSIONS The 4C Mortality Score performed well in an independent external COVID-19 cohort and may enable appropriate disposition of patients and allocation of medical resources.Trial registration number UMIN000040598.
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Affiliation(s)
- Shunsuke Kuroda
- Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Shingo Matsumoto
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine, Ota-ku, Japan
| | - Takahide Sano
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Ota-ku, Japan
| | - Takeshi Kitai
- Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Taishi Yonetsu
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Bunkyo-ku, Japan
| | - Shun Kohsaka
- Department of Cardiology, Keio University School of Medicine, Shinjuku-ku, Japan
| | - Sho Torii
- Department of Cardiology, Tokai University School of Medicine, Isehara, Japan
| | - Takuya Kishi
- Department of Graduate School of Medicine (Cardiology), International University of Health and Welfare, Fukuoka, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan
| | - Ken-Ichi Hirata
- Division of Cardiovascular Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Koichi Node
- Department of Cardiovascular Medicine, Saga University, Saga, Japan
| | - Yuya Matsue
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Japan
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Goto N, Wada Y, Ikuyama Y, Akahane J, Kosaka M, Ushiki A, Kitaguchi Y, Yasuo M, Yamamoto H, Matsuo A, Hachiya T, Ideura G, Yamazaki Y, Hanaoka M. The usefulness of a combination of age, body mass index, and blood urea nitrogen as prognostic factors in predicting oxygen requirements in patients with coronavirus disease 2019. J Infect Chemother 2021; 27:1706-1712. [PMID: 34412984 PMCID: PMC8360991 DOI: 10.1016/j.jiac.2021.08.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/09/2021] [Accepted: 08/06/2021] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Risk factors for seriously ill coronavirus disease 19 (COVID-19) patients have been reported in several studies. However, to date, few studies have reported simple risk assessment tools for distinguishing patients becoming severely ill after initial diagnosis. Hence, this study aimed to develop a simple clinical risk nomogram predicting oxygenation risk in patients with COVID-19 at the first triage. METHODS This retrospective study involved a chart review of the medical records of 84 patients diagnosed with COVID-19 between February 2020 and March 2021 at ten medical facilities. The patients were divided into requiring no oxygen therapy (non-severe group) and requiring oxygen therapy (severe group). Patient characteristics were compared between the two groups. We utilized univariate logistic regression analysis to confirm determinants of high risks of requiring oxygen therapy in patients with moderate COVID-19. RESULTS Thirty-five patients ware in severe group and forty-nine patients were in non-severe group. In comparison with patients in the non-severe group, patients in the severe group were significantly older with higher body mass index (BMI), and had a history of hypertension and diabetes. Serum blood urea nitrogen (BUN), lactic acid dehydrogenase (LDH), and C-reactive protein (CRP) levels were significantly higher in the severe group. Multivariate analysis showed that older age, higher BMI, and higher BUN levels were significantly associated with oxygen requirements. CONCLUSIONS This study demonstrated that age, BMI, and BUN were independent risk factors in the moderate-to-severe COVID-19 group. Elderly patients with higher BMI and BUN require close monitoring and early treatment initiation.
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Affiliation(s)
- Norihiko Goto
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
| | - Yosuke Wada
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan.
| | - Yuichi Ikuyama
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
| | - Jumpei Akahane
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
| | - Makoto Kosaka
- Center of Infectious Diseases, Nagano Prefectural Shinshu Medical Center, 1332, Suzaka, Suzaka City, Nagano, 382-8577, Japan
| | - Atsuhito Ushiki
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
| | - Yoshiaki Kitaguchi
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
| | - Masanori Yasuo
- Departments of Clinical Laboratory Sciences, Shinshu University School of Health Sciences, Matsumoto, Nagano, 390-8621, Japan
| | - Hiroshi Yamamoto
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
| | - Akemi Matsuo
- Department of Respiratory Medicine, Minaminagano Medical Center, Shinonoi General Hospital, 666-1 Ai, Shinonoi, Nagano City, Nagano, 388-8004, Japan
| | - Tsutomu Hachiya
- Department of Respiratory Medicine, Japanese Red Cross Society Suwa Hospital, 5-11-50, Kogandori, Suwa City, Nagano, 392-8510, Japan
| | - Gen Ideura
- Department of Respiratory Medicine, National Hospital Organization Shinshu Ueda Medical Center, 386-8610, Japan
| | - Yoshitaka Yamazaki
- Center of Infectious Diseases, Nagano Prefectural Shinshu Medical Center, 1332, Suzaka, Suzaka City, Nagano, 382-8577, Japan
| | - Masayuki Hanaoka
- First Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Nagano, 390-8621, Japan
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Aktar S, Talukder A, Ahamad MM, Kamal AHM, Khan JR, Protikuzzaman M, Hossain N, Azad AKM, Quinn JMW, Summers MA, Liaw T, Eapen V, Moni MA. Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19. Diagnostics (Basel) 2021; 11:1383. [PMID: 34441317 PMCID: PMC8393412 DOI: 10.3390/diagnostics11081383] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/12/2021] [Accepted: 07/29/2021] [Indexed: 02/06/2023] Open
Abstract
Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes, and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.
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Affiliation(s)
- Sakifa Aktar
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (S.A.); (M.M.A.); (M.P.)
| | - Ashis Talukder
- Statistics Discipline, Khulna University, Khulna 9208, Bangladesh;
| | - Md. Martuza Ahamad
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (S.A.); (M.M.A.); (M.P.)
| | - A. H. M. Kamal
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh 2220, Bangladesh;
| | - Jahidur Rahman Khan
- Health Research Institute, University of Canberra, Canberra, ACT 2617, Australia;
| | - Md. Protikuzzaman
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (S.A.); (M.M.A.); (M.P.)
| | - Nasif Hossain
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki 852-8523, Japan;
| | - A. K. M. Azad
- Faculty of Science, Engineering & Technology, Swinburne University of Technology Sydney, Sydney, VIC 2150, Australia;
| | - Julian M. W. Quinn
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia; (J.M.W.Q.); (M.A.S.)
| | - Mathew A. Summers
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia; (J.M.W.Q.); (M.A.S.)
- St Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW 2010, Australia
| | - Teng Liaw
- School of Health & Rehabilitation Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;
| | - Valsamma Eapen
- World Health Organization (WHO) Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Mohammad Ali Moni
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW 2010, Australia; (J.M.W.Q.); (M.A.S.)
- School of Health & Rehabilitation Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;
- World Health Organization (WHO) Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
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Katzenschlager S, Zimmer AJ, Gottschalk C, Grafeneder J, Schmitz S, Kraker S, Ganslmeier M, Muth A, Seitel A, Maier-Hein L, Benedetti A, Larmann J, Weigand MA, McGrath S, Denkinger CM. Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome? PLoS One 2021; 16:e0255154. [PMID: 34324560 PMCID: PMC8321230 DOI: 10.1371/journal.pone.0255154] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 07/10/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19. METHODS This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies. RESULTS Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73). DISCUSSION This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.
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Affiliation(s)
| | - Alexandra J. Zimmer
- Departments of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Claudius Gottschalk
- Division of Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Grafeneder
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Stephani Schmitz
- Division of Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Sara Kraker
- Division of Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Marlene Ganslmeier
- Division of Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Amelie Muth
- Division of Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Seitel
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andrea Benedetti
- Departments of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Jan Larmann
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Markus A. Weigand
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Sean McGrath
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Claudia M. Denkinger
- Division of Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Infection Research (DZIF), partner site Heidelberg, Heidelberg, Germany
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Shi C, Wang L, Ye J, Gu Z, Wang S, Xia J, Xie Y, Li Q, Xu R, Lin N. Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis. BMC Infect Dis 2021; 21:663. [PMID: 34238232 PMCID: PMC8264491 DOI: 10.1186/s12879-021-06369-0] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS PubMed, EMBASE, the Cochrane Library, and three electronic Chinese databases were searched from December 1, 2019 to April 29, 2020. Eligible studies reporting potential predictors of mortality in patients with COVID-19 were identified. Unadjusted prognostic effect estimates were pooled using the random-effects model if data from at least two studies were available. Adjusted prognostic effect estimates were presented by qualitative analysis. RESULTS Thirty-six observational studies were identified, of which 27 were included in the meta-analysis. A total of 106 potential risk factors were tested, and the following important predictors were associated with mortality: advanced age, male sex, current smoking status, preexisting comorbidities (especially chronic kidney, respiratory, and cardio-cerebrovascular diseases), symptoms of dyspnea, complications during hospitalization, corticosteroid therapy and a severe condition. Additionally, a series of abnormal laboratory biomarkers of hematologic parameters, hepatorenal function, inflammation, coagulation, and cardiovascular injury were also associated with fatal outcome. CONCLUSION We identified predictors of mortality in patients with COVID-19. These findings could help healthcare providers take appropriate measures and improve clinical outcomes in such patients.
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Affiliation(s)
- Changcheng Shi
- Department of Clinical Pharmacy, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Limin Wang
- Department of Respiratory Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jian Ye
- Department of Respiratory Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhichun Gu
- Department of Pharmacy, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shuying Wang
- Department of Nosocomial Infection Control, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junbo Xia
- Department of Respiratory Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yaping Xie
- Department of Hematology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingyu Li
- Department of Clinical Pharmacy, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Renjie Xu
- Department of Clinical Pharmacy, Shaoxing Women and Children's Hospital, Shaoxing, China
| | - Nengming Lin
- Department of Clinical Pharmacy, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China. .,Department of Clinical Pharmacology, Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261 Huansha Road, Hangzhou, China.
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Neves MT, de Matos LV, Vasques AC, Sousa IE, Ferreira I, Peres S, Jesus S, Fonseca C, Mansinho K. COVID-19 and aging: Identifying measures of severity. SAGE Open Med 2021; 9:20503121211027462. [PMID: 34249362 PMCID: PMC8239978 DOI: 10.1177/20503121211027462] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 06/03/2021] [Indexed: 12/16/2022] Open
Abstract
INTRODUCTION We aimed to compare clinical features of older age group and young and middle-aged patients with COVID-19 and analyze mortality predictors. METHODS Retrospective analysis of ongoing collection of prespecified data, on a single institution, including patients hospitalized consecutively due to COVID-19 infection, from March to June 2020. RESULTS Of 195 patients, 56.9% were ⩾65 years (older age group). Older age group had multimorbidity (p < 0.001). At admission Early Warning Score-2 (p < 0.001), C-reactive protein, D-dimer, creatinine, anemia and lymphopenia were higher in older age group, as well as median time of hospitalization (14 vs 10 days, p = 0.004). Complications were more common in older age group, but there were no significant differences in admission to intensive care. There were 18 deaths, 16 in older age group. Modified Early Warning Score at admission (odds ratio = 1.60, 95% confidence interval = 1.07-1.37, p = 0.021) and C-reactive protein >5 mg/dL (odds ratio = 2.12, 95% confidence interval = 1.13-26.26, p = 0.034) were independent predictors of inhospital mortality in older age group but not in young and middle-aged. CONCLUSION Older age group was at higher risk for complications and inhospital mortality. Identification of specific scores of severity for this population is essential to ensure that best care is provided.
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Affiliation(s)
- Maria Teresa Neves
- Department of Medical Oncology Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Leonor Vasconcelos de Matos
- Department of Medical Oncology Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Ana Carolina Vasques
- Department of Medical Oncology Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Inês Egídio Sousa
- Department of Internal Medicine, Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Inês Ferreira
- Department of Internal Medicine, Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Susana Peres
- Department of Infectious Diseases, Hospital Egas Moniz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Susana Jesus
- Department of Internal Medicine, Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Cândida Fonseca
- Department of Internal Medicine, Hospital São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Kamal Mansinho
- Department of Infectious Diseases, Hospital Egas Moniz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
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Tyagi A, Tyagi S, Agrawal A, Mohan A, Garg D, Salhotra R, Saxena AK, Goel A. Early Warning Scores at Time of ICU Admission to Predict Mortality in Critically Ill COVID-19 Patients. Disaster Med Public Health Prep 2021; 16:1-5. [PMID: 34140066 PMCID: PMC8376854 DOI: 10.1017/dmp.2021.208] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/03/2021] [Accepted: 06/05/2021] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To assess ability of National Early Warning Score 2 (NEWS2), systemic inflammatory response syndrome (SIRS), quick Sequential Organ Failure Assessment (qSOFA), and CRB-65 calculated at the time of intensive care unit (ICU) admission for predicting ICU mortality in patients of laboratory confirmed coronavirus disease 2019 (COVID-19) infection. METHODS This prospective data analysis was based on chart reviews for laboratory confirmed COVID-19 patients admitted to ICUs over a 1-mo period. The NEWS2, CRB-65, qSOFA, and SIRS were calculated from the first recorded vital signs upon admission to ICU and assessed for predicting mortality. RESULTS Total of 140 patients aged between 18 and 95 y were included in the analysis of whom majority were >60 y (47.8%), with evidence of pre-existing comorbidities (67.1%). The most common symptom at presentation was dyspnea (86.4%). Based upon the receiver operating characteristics area under the curve (AUC), the best discriminatory power to predict ICU mortality was for the CRB-65 (AUC: 0.720 [95% confidence interval [CI]: 0.630-0.811]) followed closely by NEWS2 (AUC: 0.712 [95% CI: 0.622-0.803]). Additionally, a multivariate Cox regression model showed Glasgow Coma Scale score at time of admission (P < 0.001; adjusted hazard ratio = 0.808 [95% CI: 0.715-0.911]) to be the only significant predictor of ICU mortality. CONCLUSIONS CRB-65 and NEWS2 scores assessed at the time of ICU admission offer only a fair discriminatory value for predicting mortality. Further evaluation after adding laboratory markers such as C-reactive protein and D-dimer may yield a more useful prediction model. Much of the earlier data is from developed countries and uses scoring at time of hospital admission. This study was from a developing country, with the scores assessed at time of ICU admission, rather than the emergency department as with existing data from developed countries, for patients with moderate/severe COVID-19 disease. Because the scores showed some utility for predicting ICU mortality even when measured at time of ICU admission, their use in allocation of limited ICU resources in a developing country merits further research.
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Affiliation(s)
- Asha Tyagi
- Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
| | - Surbhi Tyagi
- Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
| | - Ananya Agrawal
- Hamdard Institute of Medical Sciences & Research, New Delhi, India
| | - Aparna Mohan
- Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
| | - Devansh Garg
- Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
| | - Rashmi Salhotra
- Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
| | - Ashok Kumar Saxena
- Department of Anaesthesiology & Critical Care, University College of Medical Sciences & GTB Hospital, Delhi, India
| | - Ashish Goel
- Department of Medicine, University College of Medical Sciences & GTB Hospital, Delhi, India
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Özdemir S, Akça HŞ, Algın A, Altunok İ, Eroğlu SE. Effectiveness of the rapid emergency medicine score and the rapid acute physiology score in prognosticating mortality in patients presenting to the emergency department with COVID-19 symptoms. Am J Emerg Med 2021; 49:259-264. [PMID: 34171720 PMCID: PMC8191303 DOI: 10.1016/j.ajem.2021.06.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/01/2021] [Accepted: 06/07/2021] [Indexed: 11/11/2022] Open
Abstract
Objective We investigated the effectiveness of the Rapid Emergency Medicine Score and the Rapid Acute Physiology Score in identifying critical patients among those presenting to the emergency department with COVID-19 symptoms. Material and methods This prospective, observational, cohort study included patients with COVID-19 symptoms presenting to the emergency department over a two-month period. Demographics, clinical characteristics, and the data of all-cause mortality within 30 days after admission were noted, and the Rapid Emergency Medicine Score and the Rapid Acute Physiology Score were calculated by the researchers. The receiver operating characteristic curve analysis was performed to determine the discriminative ability of the scores. Results A total of 555 patients with a mean of age of 49.4 ± 16.8 years were included in the study. The rate of 30-day mortality was 3.9% for the whole study cohort, 7.2% for the patients with a positive rt-PCR test result for SARS-CoV-2, and 1.2% for those with a negative rt-PCR test result for SARS-CoV-2. In the group of patients with COVID-19 symptoms, according to the best Youden's index, the cut-off value for the Rapid Emergency Medicine Score was determined as 3.5 (sensitivity: 81.82%, specificity: 73.08%), and the area under curve (AUC) value was 0.840 (95% confidence interval 0.768–0.913). In the same group, according to the best Youden's index, the cut-off value for the Rapid Acute Physiology Score was 2.5 (sensitivity: 90.9%, specificity: 97.38%), and the AUC value was 0.519 (95% confidence interval 0.393–0.646). Conclusion REMS is able to predict patients with COVID-19-like symptoms without positive rt-PCR for SARS-CoV-2 that are at a high-risk of 30-day mortality. Prospective multicenter cohort studies are needed to provide best scoring system for triage in pandemic clinics.
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Affiliation(s)
- Serdar Özdemir
- Department of Emergency Medicine, University of Health Sciences Umraniye Training and Research Hospital, Istanbul, Turkey.
| | - Hatice Şeyma Akça
- Department of Emergency Medicine, University of Health Sciences Umraniye Training and Research Hospital, Istanbul, Turkey
| | - Abdullah Algın
- Department of Emergency Medicine, University of Health Sciences Umraniye Training and Research Hospital, Istanbul, Turkey
| | - İbrahim Altunok
- Department of Emergency Medicine, University of Health Sciences Umraniye Training and Research Hospital, Istanbul, Turkey
| | - Serkan Emre Eroğlu
- Department of Emergency Medicine, University of Health Sciences Umraniye Training and Research Hospital, Istanbul, Turkey
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Sun W, Zhang Y, Wu C, Wang S, Xie Y, Zhang D, Yuan H, Zhang Y, Cui L, Li M, Zhang Y, Li Y, Wang J, Yang Y, Lv Q, Zhang L, Haines P, Wu WC, Xie M. Early vs. Late Onset Cardiac Injury and Mortality in Hospitalized COVID-19 Patients in Wuhan. Front Cardiovasc Med 2021; 8:645587. [PMID: 34124189 PMCID: PMC8193922 DOI: 10.3389/fcvm.2021.645587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/29/2021] [Indexed: 01/02/2023] Open
Abstract
Background: Increasing evidence points to cardiac injury (CI) as a common coronavirus disease 2019 (COVID-19) related complication. The characteristics of early CI (occurred within 72 h of admission) and late CI (occurred after 72 h of admission) and its association with mortality in COVID-19 patients is unknown. Methods: This retrospective study analyzed patients confirmed with COVID-19 in Union Hospital (Wuhan, China) from Jan 29th to Mar 15th, 2020. Clinical outcomes (discharge, or death) were monitored to April 15, 2020, the latest date of follow-up. Demographic, clinical, laboratory, as well as treatment and prognosis were collected and analyzed in patients with early, late CI and without CI. Results: A total of 196 COVID-19 patients were included for analysis. The median age was 65 years [interquartile range (IQR) 56–73 years], and 112 (57.1%) were male. Of the 196 COVID-19 patients, 49 (25.0%) patients had early and 20 (10.2%) patients had late CI, 56.6% developed Acute-Respiratory-Distress-Syndrome (ARDS) and 43 (21.9%) patients died. Patients with any CI were more likely to have developed ARDS (87.0 vs. 40.2%) and had a higher in-hospital mortality than those without (52.2 vs. 5.5%, P < 0.001). Among CI subtypes, a significantly higher risk of in-hospital death was found in patients with early CI with recurrence [19/49 patients, adjusted odds ratio (OR) = 7.184, 95% CI 1.472–35.071] and patients with late CI (adjusted OR = 5.019, 95% CI 1.125–22.388) compared to patients with early CI but no recurrence. Conclusions: CI can occur early on or late after, the initial 72 h of admission and is associated with ARDS and an increased risk of in-hospital mortality. Both late CI and recurrent CI after the initial episode were associated with worse outcomes than patients with early CI alone. This study highlights the importance of early examination and periodical monitoring of cardiac biomarkers, especially for patients with early CI or at risk of clinical deterioration.
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Affiliation(s)
- Wei Sun
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yanting Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chun Wu
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Shuyuan Wang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuji Xie
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Danqing Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Hongliang Yuan
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yongxing Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Li Cui
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Meng Li
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yiwei Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuman Li
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jing Wang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yali Yang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Qing Lv
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Li Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Philip Haines
- Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Wen-Chih Wu
- Department of Medicine, Providence VA Medical Center, Brown University Warren Alpert Medical School, Providence, RI, United States
| | - Mingxing Xie
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Province Clinical Research Center for Medical Imaging, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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Youssef A, Kouchaki S, Shamout F, Armstrong J, El-Bouri R, Taylor T, Birrenkott D, Vasey B, Soltan A, Zhu T, Clifton DA, Eyre DW. Development and validation of early warning score systems for COVID-19 patients. Healthc Technol Lett 2021; 8:105-117. [PMID: 34221413 PMCID: PMC8239612 DOI: 10.1049/htl2.12009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/22/2021] [Accepted: 03/19/2021] [Indexed: 12/15/2022] Open
Abstract
COVID‐19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high‐flow nasal oxygen, continuous positive airways pressure, non‐invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub‐optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.
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Affiliation(s)
- Alexey Youssef
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - Samaneh Kouchaki
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Centre for Vision, Speech, and Signal Processing University of Surrey Guildford UK
| | - Farah Shamout
- Engineering Division New York University Abu Dhabi Abu Dhabi United Arab Emirates
| | - Jacob Armstrong
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Big Data Institute Nuffield Department of Population Health University of Oxford Oxford UK
| | - Rasheed El-Bouri
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - Thomas Taylor
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - Drew Birrenkott
- Stanford School of Medicine Stanford University Palo Alto USA
| | - Baptiste Vasey
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Nuffield Department of Surgical Sciences University of Oxford Oxford UK
| | - Andrew Soltan
- John Radcliffe Hospital Oxford University Hospitals NHS Foundation Trust Oxford UK.,Division of Cardiovascular Medicine Radcliffe Department of Medicine John Radcliffe Hospital University of Oxford Oxford UK
| | - Tingting Zhu
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK
| | - David A Clifton
- Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford UK.,Oxford-Suzhou Centre for Advanced Research Suzhou China
| | - David W Eyre
- Big Data Institute Nuffield Department of Population Health University of Oxford Oxford UK.,John Radcliffe Hospital Oxford University Hospitals NHS Foundation Trust Oxford UK
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50
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Chen A, Zhao Z, Hou W, Singer AJ, Li H, Duong TQ. Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study. Front Med (Lausanne) 2021; 8:661940. [PMID: 33996864 PMCID: PMC8116568 DOI: 10.3389/fmed.2021.661940] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/06/2021] [Indexed: 12/15/2022] Open
Abstract
Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Setting: Stony Brook University Hospital (New York) and Tongji Hospital. Patients: Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, N = 1,002) and testing (20%, N = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. Intervention: None. Measurements and Main Results: Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors (p < 0.001). Conclusion: This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration.
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Affiliation(s)
- Anne Chen
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Zirun Zhao
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Wei Hou
- Department of Family Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Adam J Singer
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Haifang Li
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Tim Q Duong
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States
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