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Tamal M, Althobaiti M, Alhashim M, Alsanea M, Hegazi TM, Deriche M, Alhashem AM. Radiomic features based automatic classification of CT lung findings for COVID-19 patients. Biomed Phys Eng Express 2024; 11:015012. [PMID: 39530647 DOI: 10.1088/2057-1976/ad9157] [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: 08/24/2024] [Accepted: 11/12/2024] [Indexed: 11/16/2024]
Abstract
Introduction. The lung CT images of COVID-19 patients can be typically characterized by three different findings- Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.Methods. Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with (1) K-Nearest-Neighbor (GA-KNN), (2) binary-decision-tree (GA-BDT) and (3) Artificial-Neural-Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.Results. Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).Conclusion. Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.
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Affiliation(s)
- Mahbubunnabi Tamal
- Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Murad Althobaiti
- Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Maryam Alhashim
- Department of medical physics, King Fahad Specialist Hospital Dammam, Dammam 32253, Saudi Arabia
- Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam 31441, Saudi Arabia
| | - Maram Alsanea
- Department of medical physics, King Fahad Specialist Hospital Dammam, Dammam 32253, Saudi Arabia
| | - Tarek M Hegazi
- Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam 31441, Saudi Arabia
| | - Mohamed Deriche
- Artificial Intelligence Research Centre, AIRC, Ajman University, United Arab Emirates
| | - Abdullah M Alhashem
- Neuroradiology Consultant, Radiology Department, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
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Obe -A- Ndzem Holenn SE, Mazoba TK, Mukanga DY, Zokere TB, Lungela D, Makulo JR, Ahuka S, Mbongo AT, Molua AA. Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa. Pulm Med 2024; 2024:5520174. [PMID: 38699403 PMCID: PMC11065491 DOI: 10.1155/2024/5520174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/24/2024] [Accepted: 04/06/2024] [Indexed: 05/05/2024] Open
Abstract
Methods We included all patients with respiratory symptoms (dyspnea, fever, and cough) and/or respiratory failure admitted to the SOS Médecins de nuit SARL hospital, DR Congo, during the 2nd and 3rd waves of the COVID-19 pandemic. The diagnosis of COVID-19 was established based on RT-PCR anti-SARS-CoV-2 tests (G1 (RT-PCR positive) vs. G2 (RT-PCR negative)), and all patients had a chest CT on the day of admission. We retrieved the digital files of patients, precisely the clinical, biological, and chest CT parameters of the day of admission as well as the vital outcome (survival or death). Chest CT were read by a very high-definition console using Advantage Windows software and exported to the hospital network using the RadiAnt DICOM viewer. To determine the threshold for the percentage of lung lesions associated with all-cause mortality, we used ROC curves. Factors associated with death, including chest CT parameters, were investigated using logistic regression analysis. Results The study included 200 patients (average age 56.2 ± 15.2 years; 19% diabetics and 4.5% obese), and COVID-19 was confirmed among 56% of them (G1). Chest CT showed that ground glass (72.3 vs. 39.8%), crazy paving (69.6 vs. 17.0%), and consolidation (83.9 vs. 22.7%), with bilateral and peripheral locations (68.8 vs. 30.7%), were more frequent in G1 vs. G2 (p < 0.001). No case of pulmonary embolism and fibrosis had been documented. The lung lesions affecting 30% of the parenchyma were informative in predicting death (area under the ROC curve at 0.705, p = 0.017). In multivariate analysis, a percentage of lesions affecting 50% of the lung parenchyma increased the risk of dying by 7.194 (1.656-31.250). Conclusion The chest CT demonstrated certain characteristic lesions more frequently in patients in whom the diagnosis of COVID-19 was confirmed. The extent of lesions affecting at least half of the lung parenchyma from the first day of admission to hospital increases the risk of death by a factor of 7.
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Affiliation(s)
- Serge Emmanuel Obe -A- Ndzem Holenn
- Department of Radiology and Medical Imaging, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Intensive Care Unit, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Tacite Kpanya Mazoba
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Interdisciplinary Center for Research in Medical Imaging (CIRIMED), University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Désiré Yaya Mukanga
- Department of Radiology and Medical Imaging, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
| | - Tyna Bongosepe Zokere
- Department of Radiology and Medical Imaging, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
| | - Djo Lungela
- Intensive Care Unit, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
| | - Jean-Robert Makulo
- COVID-19 Treatment Center, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Steve Ahuka
- Department of Microbiology, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Angèle Tanzia Mbongo
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Interdisciplinary Center for Research in Medical Imaging (CIRIMED), University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Antoine Aundu Molua
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Interdisciplinary Center for Research in Medical Imaging (CIRIMED), University of Kinshasa, Kinshasa, Democratic Republic of the Congo
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White SJ, Phua QS, Lu L, Yaxley KL, McInnes MDF, To MS. Heterogeneity in Systematic Reviews of Medical Imaging Diagnostic Test Accuracy Studies: A Systematic Review. JAMA Netw Open 2024; 7:e240649. [PMID: 38421646 PMCID: PMC10905313 DOI: 10.1001/jamanetworkopen.2024.0649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 01/09/2024] [Indexed: 03/02/2024] Open
Abstract
Importance Systematic reviews of medical imaging diagnostic test accuracy (DTA) studies are affected by between-study heterogeneity due to a range of factors. Failure to appropriately assess the extent and causes of heterogeneity compromises the interpretability of systematic review findings. Objective To assess how heterogeneity has been examined in medical imaging DTA studies. Evidence Review The PubMed database was searched for systematic reviews of medical imaging DTA studies that performed a meta-analysis. The search was limited to the 40 journals with highest impact factor in the radiology, nuclear medicine, and medical imaging category in the InCites Journal Citation Reports of 2021 to reach a sample size of 200 to 300 included studies. Descriptive analysis was performed to characterize the imaging modality, target condition, type of meta-analysis model used, strategies for evaluating heterogeneity, and sources of heterogeneity identified. Multivariable logistic regression was performed to assess whether any factors were associated with at least 1 source of heterogeneity being identified in the included meta-analyses. Methodological quality evaluation was not performed. Data analysis occurred from October to December 2022. Findings A total of 242 meta-analyses involving a median (range) of 987 (119-441 510) patients across a diverse range of disease categories and imaging modalities were included. The extent of heterogeneity was adequately described (ie, whether it was absent, low, moderate, or high) in 220 studies (91%) and was most commonly assessed using the I2 statistic (185 studies [76%]) and forest plots (181 studies [75%]). Heterogeneity was rated as moderate to high in 191 studies (79%). Of all included meta-analyses, 122 (50%) performed subgroup analysis and 87 (36%) performed meta-regression. Of the 242 studies assessed, 189 (78%) included 10 or more primary studies. Of these 189 studies, 60 (32%) did not perform meta-regression or subgroup analysis. Reasons for being unable to investigate sources of heterogeneity included inadequate reporting of primary study characteristics and a low number of included primary studies. Use of meta-regression was associated with identification of at least 1 source of variability (odds ratio, 1.90; 95% CI, 1.11-3.23; P = .02). Conclusions and Relevance In this systematic review of assessment of heterogeneity in medical imaging DTA meta-analyses, most meta-analyses were impacted by a moderate to high level of heterogeneity, presenting interpretive challenges. These findings suggest that, despite the development and availability of more rigorous statistical models, heterogeneity appeared to be incomplete, inconsistently evaluated, or methodologically questionable in many cases, which lessened the interpretability of the analyses performed; comprehensive heterogeneity assessment should be addressed at the author level by improving personal familiarity with appropriate statistical methodology for assessing heterogeneity and involving biostatisticians and epidemiologists in study design, as well as at the editorial level, by mandating adherence to methodologic standards in primary DTA studies and DTA meta-analyses.
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Affiliation(s)
- Samuel J. White
- Adelaide Medical School Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Qi Sheng Phua
- College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
| | - Lucy Lu
- College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
| | - Kaspar L. Yaxley
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, South Australia, Australia
| | - Matthew D. F. McInnes
- Department of Radiology, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, South Australia, Australia
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Nassanga R, Mubuuke AG, Mangun R, Tumusiime MC, Geoffrey E, Nabbosa V, Olweny F, Ameda F, Bugeza S. High resolution chest computed tomography findings in patients with clinically suspected COVID-19 pneumonia in Uganda: a cross-sectional study. Afr Health Sci 2023; 23:85-101. [PMID: 38974254 PMCID: PMC11225467 DOI: 10.4314/ahs.v23i4.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024] Open
Abstract
Background The alarming spread of the COVID-19 pandemic has led to a shortage of RT-PCR kits in Uganda necessitating the use of high-resolution chest Computed Tomography to guide patient management and treatment. Main Objective To describe the chest HRCT findings in patients with clinically suspected COVID-19 pneumonia and to compare its diagnostic accuracy to RT-PCR. . Methods In this cross-sectional study, chest HRCT findings of 384 patients and available RT-PCR laboratory results were reviewed and recorded in the data collection form. Results The commonest chest HRCT findings were bilateral ground glass opacities (78.2%). Out of the 31.7% patients that took the PCR test only 26.9% tested positive. 16 out of 17 patients who tested negative, were classified under CORADS 5.The sensitivity of chest HRCT was 90.4%, 95% CI (82.6-95.5), positive predictive value of 84.2%, 95% CI (75.6-90.7), and accuracy of 77.5%, 95% CI (71.5-87.1). Conclusions HRCT was found superior to RT-PCR in diagnosing COVID-19. A patient with positive HRCT findings should be treated as COVID 19 when RT-PCR is inaccessible or results are negative. A patient with negative HRCT requires complimentary RT-PCR and possibly follow up CT scans if symptoms persist before treating for COVID 19.
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Affiliation(s)
- Rita Nassanga
- Makerere University College of Health Sciences, Department of Radiology
- St Francis Hospital, Nsambya, Department of Radiology
| | | | | | | | - Erem Geoffrey
- Makerere University College of Health Sciences, Department of Radiology
- St Francis Hospital, Nsambya, Department of Radiology
| | - Valeria Nabbosa
- St Francis Hospital, Nsambya, Department of Radiology
- Uganda Cancer Institute
| | - Francis Olweny
- Makerere University College of Health Sciences, Department of Epidemiology and Biostatics
| | - Faith Ameda
- Makerere University College of Health Sciences, Department of Radiology
| | - Sam Bugeza
- Makerere University College of Health Sciences, Department of Radiology
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Brière O, Otekpo M, Asfar M, Gautier J, Sacco G, Annweiler C. Initial functional disability as a 1-year prognostic factor in geriatric patients hospitalized with SARS-CoV-2 infection. PLoS One 2023; 18:e0289297. [PMID: 37498909 PMCID: PMC10374042 DOI: 10.1371/journal.pone.0289297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/14/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND SARS-CoV2 infection has affected many older people and has required us to adapt our practices to this new pathology. Initial functional capacity is already considered an important prognostic marker in older patients particularly during infections. AIM The objective of this longitudinal study was to determine whether baseline functional disability was associated with mortality risk after 1 year in older patients hospitalized for COVID-19. METHODS All COVID-19 patients admitted to the geriatric acute care unit of Angers University Hospital, France, between March-June 2020 received a group iso-ressource (GIR) assessment upon admission. Disability was defined as a GIR score≤3. All-cause mortality was collected after 1 year of follow-up. Covariables were age, sex, history of malignancies, hypertension, cardiomyopathy, number of acute diseases at baseline, and use of antibiotics or respiratory treatments during COVID-19 acute phase. RESULTS In total, 97 participants (mean±SD 88.0+5.4 years; 49.5% women; 46.4% GIR score≤3) were included. 24 of the 36 patients who did not survive 1 year had a GIR score ≤ 3 (66.7%; P = 0.003). GIR score≤3 was directly associated with 1-year mortality (fully adjusted HR = 2.27 95% CI: 1.07-4.89). Those with GIR≤3 at baseline had shorter survival time than the others (log-rank P = 0.0029). CONCLUSIONS Initial functional disability was associated with poorer survival in hospitalized frail elderly COVID-19 patients. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov: NCT04560608 registered on September 23, 2022.
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Affiliation(s)
- Olivier Brière
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
| | - Marie Otekpo
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
| | - Marine Asfar
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
| | - Jennifer Gautier
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
| | - Guillaume Sacco
- University Côte d'Azur, Nice, France
- Department of Geriatric Medecine and Brain Clinic, Nice, France
| | - Cédric Annweiler
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
- UNIV ANGERS, University of Angers, Angers, France
- Gérontopôle Autonomie Longévité des Pays de la Loire, Nantes, France
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
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Ebong U, Büttner SM, Schmidt SA, Flack F, Korf P, Peters L, Grüner B, Stenger S, Stamminger T, Kestler H, Beer M, Kloth C. Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms. Diagnostics (Basel) 2023; 13:2129. [PMID: 37371024 DOI: 10.3390/diagnostics13122129] [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: 03/25/2023] [Revised: 05/14/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
PURPOSE: To implement the technical feasibility of an AI-based software prototype optimized for the detection of COVID-19 pneumonia in CT datasets of the lung and the differentiation between other etiologies of pneumonia. METHODS: This single-center retrospective case-control-study consecutively yielded 144 patients (58 female, mean age 57.72 ± 18.25 y) with CT datasets of the lung. Subgroups including confirmed bacterial (n = 24, 16.6%), viral (n = 52, 36.1%), or fungal (n = 25, 16.6%) pneumonia and (n = 43, 30.7%) patients without detected pneumonia (comparison group) were evaluated using the AI-based Pneumonia Analysis prototype. Scoring (extent, etiology) was compared to reader assessment. RESULTS: The software achieved an optimal sensitivity of 80.8% with a specificity of 50% for the detection of COVID-19; however, the human radiologist achieved optimal sensitivity of 80.8% and a specificity of 97.2%. The mean postprocessing time was 7.61 ± 4.22 min. The use of a contrast agent did not influence the results of the software (p = 0.81). The mean evaluated COVID-19 probability is 0.80 ± 0.36 significantly higher in COVID-19 patients than in patients with fungal pneumonia (p < 0.05) and bacterial pneumonia (p < 0.001). The mean percentage of opacity (PO) and percentage of high opacity (PHO ≥ -200 HU) were significantly higher in COVID-19 patients than in healthy patients. However, the total mean HU in COVID-19 patients was -679.57 ± 112.72, which is significantly higher than in the healthy control group (p < 0.001). CONCLUSION: The detection and quantification of pneumonia beyond the primarily trained COVID-19 datasets is possible and shows comparable results for COVID-19 pneumonia to an experienced reader. The advantages are the fast, automated segmentation and quantification of the pneumonia foci.
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Affiliation(s)
- Una Ebong
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Susanne Martina Büttner
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stefan A Schmidt
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Franziska Flack
- Scientific Collaborations Siemens Healthcare GmbH Erlangen, 91052 Erlangen, Germany
| | - Patrick Korf
- Scientific Collaborations Siemens Healthcare GmbH Erlangen, 91052 Erlangen, Germany
| | - Lynn Peters
- Division of Infectious Diseases, University Hospital and Medical Centre of Ulm, 89081 Ulm, Germany
| | - Beate Grüner
- Division of Infectious Diseases, University Hospital and Medical Centre of Ulm, 89081 Ulm, Germany
| | - Steffen Stenger
- Institute of Medical Microbiology and Hygiene, Ulm University Medical Center, 89081 Ulm, Germany
| | - Thomas Stamminger
- Institute of Virology, Ulm University Medical Center, 89081 Ulm, Germany
| | - Hans Kestler
- Institute for Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
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Oi Y, Ogawa F, Yamashiro T, Matsushita S, Oguri A, Utada S, Misawa N, Honzawa H, Abe T, Takeuchi I. Prediction of prognosis in patients with severe COVID-19 pneumonia using CT score by emergency physicians: a single-center retrospective study. Sci Rep 2023; 13:4045. [PMID: 36899171 PMCID: PMC10004443 DOI: 10.1038/s41598-023-31312-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/09/2023] [Indexed: 03/12/2023] Open
Abstract
We aimed to develop a method to determine the CT score that can be easily obtained from CT images and examine its prognostic value for severe COVID pneumonia. Patients with COVID pneumonia who required ventilatory management by intubation were included. CT score was based on anatomical information in axial CT images and were divided into three sections of height from the apex to the bottom. The extent of pneumonia in each section was rated from 0 to 5 and summed. The primary outcome was the prediction of patients who died or were managed on extracorporeal membrane oxygenation (ECMO) based on the CT score at admission. Of the 71 patients included, 12 (16.9%) died or required ECMO management, and the CT score predicted death or ECMO management with ROC of 0.718 (0.561-0.875). The death or ECMO versus survival group (median [quartiles]) had a CT score of 17.75 (14.75-20) versus 13 (11-16.5), p = 0.017. In conclusion, a higher score on our generated CT score could predict the likelihood of death or ECMO management. A CT score at the time of admission allows for early preparation and transfer to a hospital that can manage patients who may need ECMO.
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Affiliation(s)
- Yasufumi Oi
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan. .,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan.
| | - Fumihiro Ogawa
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Tsuneo Yamashiro
- Department of Radiology, Yokohama City University School of Medicine, Yokohama, Japan
| | - Shoichiro Matsushita
- Department of Radiology, Yokohama City University School of Medicine, Yokohama, Japan
| | - Ayako Oguri
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Shusuke Utada
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Naho Misawa
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Hiroshi Honzawa
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Takeru Abe
- Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan.,Advanced Critical Care and Emergency Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Ichiro Takeuchi
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan.,Advanced Critical Care and Emergency Center, Yokohama City University Medical Center, Yokohama, Japan
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Wen R, Zhang M, Xu R, Gao Y, Liu L, Chen H, Wang X, Zhu W, Lin H, Liu C, Zeng X. COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19. Eur Radiol 2023; 33:3133-3143. [PMID: 36892649 PMCID: PMC9996554 DOI: 10.1007/s00330-023-09498-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/08/2022] [Accepted: 01/29/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. METHODS This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. RESULTS The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. CONCLUSIONS This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.
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Affiliation(s)
- Ru Wen
- Medical College, Guizhou University, Guizhou, 550000, People's Republic of China.,Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.,Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China
| | - Mudan Zhang
- Guizhou Medical University, Guiyang, Guizhou Province, 550000, People's Republic of China
| | - Rui Xu
- Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China
| | - Yingming Gao
- College of Life Science, Guizhou University, Guiyang, Guizhou Province, 550000, People's Republic of China
| | - Lin Liu
- Department of Respiratory Medicine, Guizhou Provincial People Hospital, Guiyang City, Guizhou Province, 550000, People's Republic of China
| | - Hui Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China
| | - Xingang Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China
| | - Wenyan Zhu
- Medical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China
| | - Huafang Lin
- Medical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.
| | - Xianchun Zeng
- Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China.
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9
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Oliveira MC, Scharan KO, Thomés BI, Bernardelli RS, Reese FB, Kozesinski-Nakatani AC, Martins CC, Lobo SMA, Réa-Neto Á. Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard. BMC Pulm Med 2023; 23:81. [PMID: 36894945 PMCID: PMC9997428 DOI: 10.1186/s12890-023-02369-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND The gold-standard method for establishing a microbiological diagnosis of COVID-19 is reverse-transcriptase polymerase chain reaction (RT-PCR). This study aimed to evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. METHODS Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong versus weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). RESULTS With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5-99.5%) sensitivity, 70% (95% CI 65.8-74.2%) specificity, 85.5% (95% CI 83.4-87.7%) accuracy, PPV of 79.7% (95% CI 76.6-82.7%) and NPV of 97.6% (95% CI 95.9-99.2%). Similar performance was observed when evaluated in the subgroups of patients admitted with mild/moderate respiratory disfunction, and severe respiratory disfunction. CONCLUSION The proposed set of clinical-radiological criteria were accurate in identifying patients with strong versus weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.
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Affiliation(s)
- Mirella Cristine Oliveira
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
- Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná 81050-000 Brazil
| | - Karoleen Oswald Scharan
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
| | - Bruna Isadora Thomés
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
| | - Rafaella Stradiotto Bernardelli
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
- School of Medicine and Life Sciences, Pontifical Catholic University of Paraná, Imaculada Conceição Street, 1155, Curitiba, Paraná 80215-901 Brazil
| | - Fernanda Baeumle Reese
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
- Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná 81050-000 Brazil
| | - Amanda Christina Kozesinski-Nakatani
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
- Hospital Santa Casa de Curitiba, Praça Rui Barbosa, 694, Curitiba, Paraná 80010-030 Brazil
| | - Cintia Cristina Martins
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
- Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná 81050-000 Brazil
| | - Suzana Margareth Ajeje Lobo
- Departament of Medicine, São José do Rio Preto Medical School, Brigadeiro Faria Lima avenue, 5416, São José do Rio Preto, São Paulo 15090-000 Brazil
| | - Álvaro Réa-Neto
- Center for Studies and Research in Intensive Care Medicine – CEPETI, Monte Castelo Street, 366, Curitiba, Paraná 82590-300 Brazil
- Internal Medicine Department, Hospital de Clínicas, Federal University of Paraná, General Carneiro Street, 181, Curitiba, Paraná 80060-900 Brazil
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10
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Prakash J, Kumar N, Saran K, Yadav AK, Kumar A, Bhattacharya PK, Prasad A. Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis. J Med Imaging Radiat Sci 2023; 54:364-375. [PMID: 36907753 PMCID: PMC9933858 DOI: 10.1016/j.jmir.2023.02.003] [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: 07/26/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects. METHODS The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool. RESULTS Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I2 =83), 0.86 (95% CI 0.76-0.92, I2 =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I2 = 41), 0.79 (95% CI 0.72-0.85, I2 = 88), and 0.84 (95% CI 0.81-0.87), respectively. DISCUSSION Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis. CONCLUSION Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients. CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.
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Affiliation(s)
- Jay Prakash
- Department of Critical Care Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Naveen Kumar
- Department of Radiology, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Khushboo Saran
- Department of Pathology, Gandhi Nagar Hospital, Central Coalfields Limited, Kanke, Ranchi, Jharkhand, India.
| | - Arun Kumar Yadav
- Department of Community Medicine, Armed Force Medical College, Pune, Maharashtra, India
| | - Amit Kumar
- Department of Laboratory Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Pradip Kumar Bhattacharya
- Department of Critical Care Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Anupa Prasad
- Department of Biochemistry, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
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Chest computed tomography of suspected COVID-19 pneumonia in the Emergency Department: comparative analysis between patients with different vaccination status. Pol J Radiol 2023; 88:e80-e88. [PMID: 36910888 PMCID: PMC9995244 DOI: 10.5114/pjr.2023.125010] [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: 09/17/2022] [Accepted: 10/25/2022] [Indexed: 03/06/2023] Open
Abstract
Purpose To identify differences in chest computed tomography (CT) of the symptomatic coronavirus disease 2019 (COVID-19) population according to the patients' severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination status (non-vaccinated, vaccinated with incomplete or complete vaccination cycle). Material and methods CT examinations performed in the Emergency Department (ED) in May-November 2021 for suspected COVID-19 pneumonia with a positive SARS-CoV-2 test were retrospectively included. Personal data were compared for vaccination status. One 13-year experienced radiologist and two 4th-year radiology residents independently evaluated chest CT scans according to CO-RADS and ACR COVID classifications. In possible COVID-19 pneumonia cases, defined as CO-RADS 3 to 5 (ACR indeterminate and typical) by each reader, high involvement CT score (≥ 25%) and CT patterns (presence of ground glass opacities, consolidations, crazy paving areas) were compared for vaccination status. Results 184 patients with known vaccination status were included in the analysis: 111 non-vaccinated (60%) for SARS-CoV-2 infection, 21 (11%) with an incomplete vaccination cycle, and 52 (28%) with a complete vaccination cycle (6 different vaccine types). Multivariate logistic regression showed that the only factor predicting the absence of pneumonia (CO-RADS 1 and ACR negative cases) for the 3 readers was a complete vaccination cycle (OR = 12.8-13.1compared to non-vaccinated patients, p ≤ 0.032). Neither CT score nor CT patterns of possible COVID-19 pneumonia showed any statistically significant correlation with vaccination status for the 3 readers. Conclusions Symptomatic SARS-CoV-2-infected patients with a complete vaccination cycle had much higher odds of showing a negative CT chest examination in ED compared to non-vaccinated patients. Neither CT involvement nor CT patterns of interstitial pneumonia showed differences across different vaccination status.
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12
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Kanda J, Wakasugi M, Kondo Y, Ueno S, Kaneko H, Okada Y, Okano Y, Kishihara Y, Hamaguchi J, Ishihara T, Igarashi Y, Nakae R, Miyamoto S, Yamada E, Ikechi D, Yamazaki M, Tanaka D, Sawada Y, Suda C, Yoshimura S, Onodera R, Kano K, Hongo T, Endo K, Iwasaki Y, Kodaira H, Yasuo S, Seki N, Okuda H, Nakajima S, Nagato T, Terazumi K, Nakamura S, Yokobori S. Heat stroke management during the COVID-19 pandemic: Recommendations from the experts in Japan (2nd edition). Acute Med Surg 2023; 10:e827. [PMID: 37056485 PMCID: PMC10086676 DOI: 10.1002/ams2.827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/08/2023] [Indexed: 04/15/2023] Open
Abstract
Both coronavirus disease 2019 (COVID-19) and heat stroke have symptoms of fever or hyperthermia and the difficulty in distinguishing them could lead to a strain on emergency medical care. To mitigate the potential confusion that could arise from actions for preventing both COVID-19 spread and heat stroke, particularly in the context of record-breaking summer season temperatures, this work offers new knowledge and evidence that address concerns regarding indoor ventilation and indoor temperatures, mask wearing and heat stroke risk, and the isolation of older adults. Specifically, the current work is the second edition to the previously published guidance for handling heat stroke during the COVID-19 pandemic, prepared by the "Working group on heat stroke medical care during the COVID-19 epidemic," composed of members from four organizations in different medical and related fields. The group was established by the Japanese Association for Acute Medicine Heatstroke and Hypothermia Surveillance Committee. This second edition includes new knowledge, and conventional evidence gleaned from a primary selection of 60 articles from MEDLINE, one article from Cochrane, 13 articles from Ichushi, and a secondary/final selection of 56 articles. This work summarizes the contents that have been clarified in the prevention and treatment of infectious diseases and heat stroke to provide guidance for the prevention, diagnosis, and treatment of heat stroke during the COVID-19 pandemic.
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Affiliation(s)
- Jun Kanda
- Department of Emergency MedicineTeikyo University School of MedicineItabashiJapan
| | - Masahiro Wakasugi
- Department of Emergency and Disaster MedicineUniversity of ToyamaToyamaJapan
| | - Yutaka Kondo
- Department of Emergency and Critical Care MedicineJuntendo University Urayasu HospitalUrayasuJapan
| | - Satoru Ueno
- Japan Organisation of Occupational Health and SafetyNational Institute of Occupational Safety and HealthKiyoseJapan
| | - Hitoshi Kaneko
- Department of Trauma and Emergency MedicineTokyo Metropolitan Tama Medical CenterFuchuJapan
| | - Yohei Okada
- Department of Public Health Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Yuichi Okano
- Department of Emergency MedicineKumamoto Sekijuji HospitalKumamotoJapan
| | - Yuki Kishihara
- Department of Urology, Emergency Room, Jichi Medical University Saitama Medical CenterJichi Medical UniversityShimotsukeJapan
| | - Jun Hamaguchi
- Department of Emergency and Critical Care MedicineTokyo Metropolitan Tama Medical CenterFuchuJapan
| | - Tadashi Ishihara
- Department of Emergency and Critical Care MedicineJuntendo University Urayasu HospitalUrayasuJapan
| | - Yutaka Igarashi
- Department of Emergency and Critical Care MedicineNippon Medical SchoolBunkyo CityJapan
| | - Ryuta Nakae
- Department of Emergency and Critical Care MedicineNippon Medical SchoolBunkyo CityJapan
| | - Sohma Miyamoto
- Department of Emergency and Critical Care MedicineSt. Luke's International HospitalChuo CityJapan
| | - Eri Yamada
- Advanced Medical Emergency Department and Critical Care CenterMaebashi Red Cross HospitalMaebashiJapan
| | - Daisuke Ikechi
- Department of Emergency and Critical Care MedicineHitachi General HospitalHitachiJapan
| | - Maiko Yamazaki
- Department of Emergency MedicineTeikyo University School of MedicineItabashiJapan
| | - Daiki Tanaka
- Department of Emergency MedicineTeikyo University School of MedicineItabashiJapan
| | - Yusuke Sawada
- Department of Emergency MedicineGunma University Graduate School of MedicineMaebashiJapan
| | - Chiaki Suda
- Department of Emergency and Critical Care MedicineSaku Central Hospital Advanced Care CenterSakuJapan
| | | | - Ryuta Onodera
- Department of Preventive ServicesKyoto University School of Public HealthKyotoJapan
| | - Kenichi Kano
- Emergency and Critical Care MedicineKokuritsu Byoin Kiko Kyoto Iryo CenterKyotoJapan
| | - Takashi Hongo
- Emergency DepartmentOkayama Saiseikai General HospitalOkayamaJapan
| | - Kaori Endo
- Orthopaedic Surgery, Sapporo Tokushukai HospitalHokkaido UniversitySapporoJapan
| | - Yohei Iwasaki
- Trauma and Acute Critical Care CenterTokyo Medical and Dental University HospitalTokyoJapan
| | | | | | - Nozomu Seki
- Department of Emergency and Critical Care MedicineJapanese Red Cross Saitama HospitalSaitamaJapan
| | - Hiroshi Okuda
- Division of Comprehensive MedicineTohoku University Graduate School of MedicineSendaiJapan
| | - Satoshi Nakajima
- Department of Emergency MedicineKyoto Prefectural University of MedicineKyotoJapan
| | - Tadashi Nagato
- Department of Respiratory MedicineJCHO Tokyo Yamate Medical CenterKyotoJapan
| | - Keiko Terazumi
- Trauma and Critical CareJapanese Red Cross Kumamoto HospitalKumamotoJapan
| | - Satoshi Nakamura
- Department of Emergency MedicineAsahi General HospitalAsahiJapan
| | - Shoji Yokobori
- Department of Emergency and Critical Care MedicineNippon Medical SchoolBunkyo CityJapan
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13
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Parental chest computerized tomography examination before IVF/ICSI has no impact on pregnancy and neonatal outcomes: a cohort study of 2680 fresh transfer cycles. BMC Pregnancy Childbirth 2022; 22:965. [PMID: 36572853 PMCID: PMC9791144 DOI: 10.1186/s12884-022-05297-4] [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] [Received: 01/12/2022] [Accepted: 12/08/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Some concern has been expressed regarding the negative effects of low-level ionizing radiation exposure in the context of radiological evaluation prior to IVF/ICSI treatment, but the available evidence is limited and conflicting. The aim of this study is to evaluate pregnancy and neonatal outcomes of couples who did chest computed tomography (CT) prior to IVF/ICSI. METHODS This was a retrospective cohort study of 2680 IVF/ICSI fresh embryo transfer cycles conducted from January 2019 - August 2020. Fertility outcomes were compared between couples that had or had not undergone CT examination within 3 months prior to the date of oocyte retrieval and sperm collection. Miscarriage was the primary study outcome, while secondary outcomes included the number of oocytes collected, oocyte maturation, normal fertilization, number of good quality cleavage stage embryos, blastocyst formation, implantation, clinical pregnancy, ectopic pregnancy, live birth, multiple birth, Cesarean section rates, gestational weeks, maternal obstetric complications, birth weight, newborn sex ratio, and birth defect incidence. Propensity score matching was used to control for potential confounding variables. RESULTS Of the 2680 cycles included in this study, couples underwent CT examination in 731 cycles. After 1:1 propensity score matching, 670 cycles were included in each group. When comparing demographic and fertility-related variables between groups that had and had not undergone CT examination after propensity score matching, we detected no significant differences in miscarriage rates (16.99% vs. 15.77%, OR = 1.10, 95CI% = 0.74 to 1.68). Similarly, both groups exhibited comparable oocyte and embryonic development, implantation rates (41.99% vs. 40.42%, OR = 1.07, 95%CI = 0.87 to 1.31), clinical pregnancy rates (45.67% vs. 44.48%, OR = 1.05, 95%CI = 0.85 to 1.30), ectopic pregnancy rates (2.94% vs. 1.68%, OR = 1.78, 95%CI = 0.59 to 5.36), live birth rates (36.57% vs. 35.67%, OR = 1.04, 95%CI = 0.83 to 1.30), multiple birth rates, Cesarean section rates, gestational weeks, maternal obstetric complication rates, and neonatal outcomes. CONCLUSIONS Chest CT examination before IVF/ICSI has no impact on pregnancy and neonatal outcomes associated with fresh embryo transfer. TRIAL REGISTRATION Not applicable.
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14
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Torres-Ramirez CA, Timaran-Montenegro D, Mateo-Camacho YS, Morales-Jaramillo LM, Tapia-Rangel EA, Fuentes-Badillo KD, Morales-Dominguez V, Punzo-Alcaraz R, Feria-Arroyo GA, Parra-Guerrero LM, Saenz-Castillo PF, Hernandez-Rojas AM, Falla-Trujillo MG, Obando-Bravo DE, Contla-Trejo GS, Jacome-Portilla KI, Chavez-Sastre J, Govea-Palma J, Carrillo-Alvarez S, Bonifacio D, Orozco-Vazquez JDS. CT-based pathological lung opacities volume as a predictor of critical illness and inflammatory response severity in patients with COVID-19. Heliyon 2022; 8:e11908. [PMID: 36447748 PMCID: PMC9694356 DOI: 10.1016/j.heliyon.2022.e11908] [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: 08/27/2020] [Revised: 10/18/2021] [Accepted: 11/18/2022] [Indexed: 11/27/2022] Open
Abstract
Objective The aim of the study was to assess the impact of CT-based lung pathological opacities volume on critical illness and inflammatory response severity of patients with COVID-19. Methods A retrospective, single center, single arm study was performed over a 30-day period. In total, 138 patients (85.2%) met inclusion criteria. All patients were evaluated with non-contrast enhanced chest CT scan at hospital admission. CT-based lung segmentation was performed to calculate pathological lung opacities volume (LOV). At baseline, complete blood count (CBC) and inflammation response biomarkers were obtained. The primary endpoint of the study was the occurrence of critical illness, as defined as, the need of mechanical ventilation and/or ICU admission. Mann-Whitney U test was performed for univariate analysis. Logistic regression analysis was performed to determine independent predictors of critical illness. Spearman analysis was performed to assess the correlation between inflammatory response biomarkers serum concentrations and LOV. Results Median LOV was 28.64% (interquartile range [IQR], 6.33-47.22%). Correlation analysis demonstrated that LOV was correlated with higher levels of D-dimer (r = 0.51, p < 0.01), procalcitonin (r = 0.47, p < 0.01) and IL6 (r = 0.48, p < 0.01). Critical illness occurred in 51 patients (37%). Univariate analysis demonstrated that inflammatory response biomarkers and LOV were associated with critical illness (p < 0.05). However, multivariate analysis demonstrated that only D-dimer and LOV were independent predictors of critical illness. Furthermore, a ROC analysis demonstrated that a LOV equal or greater than 60% had a sensitivity of 82.1% and specificity of 70.2% to determine critical illness with an odds ratio of 19.4 (95% CI, 4.2-88.9). Conclusion Critical illness may occur in up to 37% of the patients with COVID-19. Among patients with critical illness, higher levels of inflammatory response biomarkers with larger LOVs were observed. Furthermore, multivariate analysis demonstrated that pathological lung opacities volume was an independent predictor of critical illness. In fact, patients with a pathological lung opacities volume equal or greater than 60% had 19.4-fold increased risk of critical illness.
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Affiliation(s)
| | - David Timaran-Montenegro
- Department of Diagnostic and Interventional Imaging, McGovern School of Medicine, University of Texas Health Science Center, 6431 Fannin ST, MSB 2.130B, Houston, TX, 77030, USA
| | - Yohana Sarahi Mateo-Camacho
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | | | - Edgar Alonso Tapia-Rangel
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Karla Daniela Fuentes-Badillo
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Valeria Morales-Dominguez
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Rafael Punzo-Alcaraz
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Gustavo Adolfo Feria-Arroyo
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Lina Marcela Parra-Guerrero
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Pedro Fernando Saenz-Castillo
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Ana Milena Hernandez-Rojas
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Manuel Gerardo Falla-Trujillo
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Daniel Ernesto Obando-Bravo
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Giovanni Saul Contla-Trejo
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | | | - Joshua Chavez-Sastre
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Jovanni Govea-Palma
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Santiago Carrillo-Alvarez
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
| | - Dulce Bonifacio
- Department of Radiology, Centro Médico Nacional 20 de Noviembre, Universidad Nacional Autonoma de Mexico (UNAM), México
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Celasin H, Törüner M, Aghayeva S, Bayramov N, Vardanyan A, Nanaeva B, Dardanov D, Podpriatov S, Dorofeyev A, Ethem Geçim İ, Gecim IE. Perception of COVID-19 Pandemic Among IBD Clinicians and IBD Surgeons in Black Sea Region: A Cross-Sectional Questionnaire Study. THE TURKISH JOURNAL OF GASTROENTEROLOGY : THE OFFICIAL JOURNAL OF TURKISH SOCIETY OF GASTROENTEROLOGY 2022; 33:1004-1011. [PMID: 35726844 PMCID: PMC9797775 DOI: 10.5152/tjg.2022.22009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND Since December 2019, the COVID-19 pandemic has created an increasing challenge in managing inflammatory bowel dis- ease patients both medically and surgically. Although several international and national medical/surgical associations published guide- lines in this area, there is still a huge difference between daily practices and these guidelines, especially depending on regional practices and governmental policies. Therefore, we aimed to investigate and define gastroenterologists' and surgeons' fear of COVID-19 and how they have managed inflammatory bowel disease patients during this pandemic in the Black Sea region. METHODS A 20-question survey was administered to 70 gastroenterology specialists and 80 general surgeons who are mainly focused on the management of inflammatory bowel disease in 5 countries in the Black Sea region. RESULTS The majority of respondents (81.3%) mentioned that they have concerns that their inflammatory bowel disease patients were at risk of contracting COVID-19. In addition, the majority of respondents (80.3%) believed that inflammatory bowel disease itself, inde- pendent of medications, might increase the risk of contracting COVID-19. The majority of gastroenterologists told that they did not stop inflammatory bowel disease medications due to the COVID-19 pandemic unless patients had COVID-19 disease. Surgeons overwhelm- ingly reached a consensus on how to test patients for COVID-19 perioperatively and came to a conclusion on which of the patients can- not wait to be operated. Both gastroenterologists and general surgeons, usually have similar perceptions. CONCLUSION Despite the increasing number of definitive studies, it seems that there are still regional differences in the perception of COVID-19 and inflammatory bowel disease patient care during the pandemic.
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Affiliation(s)
- Haydar Celasin
- Department of Surgery, Lokman Hekim University Faculty of Medicine, Ankara, Turkey,Department of Gastroenterology, Ankara University Faculty of Medicine, Ankara, Turkey,Haydar Celasin and Murat Toruner contributed equally.Corresponding author: Murat Törüner, e-mail: ,
| | - Murat Törüner
- Department of Gastroenterology, Ankara University Faculty of Medicine, Ankara, Turkey,Department of Gastroenterology, Baku Medical Plaza, Baku, Azerbaijan,Haydar Celasin and Murat Toruner contributed equally.Corresponding author: Murat Törüner, e-mail: ,
| | - Sevda Aghayeva
- Department of Surgery, Medical University of Azerbaijan, Baku, Azerbaijan
| | - Nuru Bayramov
- Ryzhikh State Scientific Research Center of Coloproctology, Moscow, Russia
| | - Armen Vardanyan
- Department of Surgery, University Hospital Alexandrovska, Sofia, Bulgaria
| | - Bella Nanaeva
- Department of Surgery, University Hospital Alexandrovska, Sofia, Bulgaria
| | | | - Sergii Podpriatov
- National Medical Academy of Postgraduate Education n.a. P.L. Shupic, Ukraine
| | - Andriy Dorofeyev
- Department of Surgery, Ankara University Faculty of Medicine, Ankara, Turkey
| | - İbrahim Ethem Geçim
- Department of Surgery, Ankara University Faculty of Medicine, Ankara, Turkey
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Mehta V, Jyoti D, Guria RT, Sharma CB. Correlation between chest CT and RT-PCR testing in India's second COVID-19 wave: a retrospective cohort study. BMJ Evid Based Med 2022; 27:305-312. [PMID: 35058302 DOI: 10.1136/bmjebm-2021-111801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVES To assess the diagnostic accuracy of chest CT in clinically suspected patients with COVID-19 using reverse transcriptase PCR (RT-PCR) as the reference standard and establish the correlation between CT Severity Score (CTSS) and RT-PCR results. DESIGN AND SETTING Retrospective cohort study. Single-centre tertiary care hospital-based study. PARTICIPANTS We enrolled 112 clinically suspected patients with COVID-19 between 1 April 2021 and 31 May 2021. Chest CT and RT-PCR tests were performed for all patients at a time interval of no longer than 7 days between the two tests. Patients with prior chronic respiratory illnesses were excluded. The diagnostic performance of chest CT was evaluated using RT-PCR as the reference standard. The CTSS was calculated for all patients with positive chest CT findings, and it was correlated with results of the RT-PCR assay. MAIN OUTCOME MEASURES The primary outcome measures were determination of the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of chest CT using RT-PCR as the standard of reference. The correlation between CTSS and RT-PCR was the secondary outcome. RESULTS 85/112 (76%) patients tested positive on the RT-PCR whereas 91/112 (81%) had chest CT findings typical of SARS-CoV-2 infection. Chest CT had a sensitivity of 90.6% (95% CI 82.3% to 95.8%), a specificity of 48.1% (95% CI 28.7% to 68.0%), a PPV of 84.6% (95% CI 79.2% to 88.8%), an NPV of 61.9% (95% CI 43.0% to 77.8%) and an accuracy of 80.4% (95% CI 71.8% to 87.3%). There was a significant correlation between the CTSS and RT-PCR positivity (p value=0.003). CONCLUSION In our experience, chest CT has a good sensitivity and provides a reliable diagnostic tool for moderate-to-severe COVID-19 cases in resource limited settings.
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Affiliation(s)
- Vishal Mehta
- Department of Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
| | - Divya Jyoti
- Department of Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
| | - Rishi Tuhin Guria
- Department of Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
| | - Chandra Bhushan Sharma
- Department of Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India
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17
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Ozery-Flato M, Ein-Dor L, Pinchasov O, Dabush Kasa M, Hexter E, Chodick G, Rosen-Zvi M, Guindy M. The Impact of COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams: An Observational Study in a Nationwide Israeli Health Organization (Preprint). JMIR Form Res 2022; 7:e42930. [PMID: 36989460 PMCID: PMC10156149 DOI: 10.2196/42930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND The outbreak of the COVID-19 pandemic had a major effect on consumption of healthcare services. Changes in the use of routine diagnostic exams, increased incidences of post-acute COVID-19 syndrome (PCS), and other pandemic-related factors, may have influenced detected clinical conditions. OBJECTIVE The study aimed to analyze the impact of COVID-19 on the use of outpatient medical imaging services and clinical findings therein, specifically focusing on the time period after the launch of the Israeli COVID-19 vaccination campaign. In addition, the study tested whether the observed gains in abnormal findings may be linked to PCS or COVID-19 vaccination. METHODS Our dataset included 572,480 ambulatory medical imaging patients in a national health organization, from January 1, 2019 to August 31, 2021. We compared different measures of medical imaging utilization and clinical findings therein, before and after the surge of the pandemic, to identify significant changes. We also inspected the changes in the rate of abnormal findings during the pandemic after adjusting for changes in medical imaging utilization. Finally, for imaging classes that showed increased rates of abnormal findings, we measured the causal associations between COVID-19 infection, hospitalization (indicative of COVID-19 complications), and vaccination and future risk for abnormal finding. To allow adjustment for a multitude of confounding factors, we used causal inference methodologies. RESULTS After the initial drop in the utilization of routine medical imaging due to the first COVID-19 wave, the number of these exams has increased, but with lower proportions of older patients, patients with comorbidities, women, and vaccine-hesitant patients. Furthermore, we observed significant gains in the rate of abnormal findings, specifically in musculoskeletal magnetic resonance (MR-MSK) and brain computed tomography (CT-brain) exams. These results also persisted after adjusting for the changes in medical imaging utilization. Demonstrated causal associations included: COVID-19 infection increasing the risk for an abnormal finding in a CT-brain exams (odds ratio [OR] of 1.4, with 95% confidence interval [CI] 1.1 to 1.7); and COVID-19-related hospitalization increasing the risk for abnormal findings in an MR-MSK exam (OR 3.1, 95% CI 1.9 to 5.3). CONCLUSIONS COVID-19 impacted the use of ambulatory imaging exams, with greater avoidance among patients at higher risk for COVID-19 complications: older patients, patients with comorbidities, and non-vaccinated patients. Causal analysis results imply that PCS may have contributed to the observed gains in abnormal findings in MR-MSK and CT-brain exams, respectively. CLINICALTRIAL
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Affiliation(s)
| | | | | | | | | | - Gabriel Chodick
- Maccabi Healthcare Services, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michal Rosen-Zvi
- IBM Research - Israel, Haifa, Israel
- Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Michal Guindy
- Assuta Medical Centers, Tel Aviv, Israel
- Goldman Medical School, Ben Gurion University of the Negev, Beer Sheva, Israel
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18
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Dori G, Bachner-Hinenzon N, Kasim N, Zaidani H, Perl SH, Maayan S, Shneifi A, Kian Y, Tiosano T, Adler D, Adir Y. A novel infrasound and audible machine-learning approach for the diagnosis of COVID-19. ERJ Open Res 2022; 8:00152-2022. [PMID: 36284830 PMCID: PMC9501643 DOI: 10.1183/23120541.00152-2022] [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: 03/28/2022] [Accepted: 07/29/2022] [Indexed: 12/15/2022] Open
Abstract
The COVID-19 outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19 related pneumonia is that it is often manifested as a “silent pneumonia”, i.e., pulmonary auscultation, using a standard stethoscope, sounds "normal". Chest CT is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilization as a screening tool for COVID-19 pneumonia. In this study we hypothesized that COVID-19 pneumonia, “silent” to the human ear using a standard stethoscope, is detectable using a full spectrum auscultation device that contains a machine-learning analysis.Lung sounds signals were acquired, using a novel full spectrum (3–2,000Hz) stethoscope, from 164 patients with COVID-19 pneumonia, 61 non-COVID-19 pneumonia and 141 healthy subjects. A machine-learning classifier was constructed, and the data was classified into 3 groups: 1. Normal lung sounds 2. COVID-19 pneumonia 3. Non-COVID-19 pneumonia.Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds, compared to only 25% for the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analyzing the sound and infrasound data, and they were reduced to 93% and 80% without the infrasound data (p<0.01 difference in ROC with and without infrasound).This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19 related pneumonia, and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.
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19
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Evaluation of the appropriate use of chest CT-Scans in the diagnosis of hospitalized patients in shiraz teaching hospitals, Southern Iran. Cost Eff Resour Alloc 2022; 20:44. [PMID: 35999543 PMCID: PMC9395783 DOI: 10.1186/s12962-022-00381-0] [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] [Received: 02/23/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE During recent years, overuse of medical imaging especially computed tomography has become a serious concern. We evaluated the suitable usage of chest computed tomography (CT)-scan, in patients hospitalized in emergency and medical wards of two teaching hospitals of Shiraz University of Medical Science. METHODS Medical records of 216 patients admitted in two major teaching hospitals (Namazi and Shahid Faghihi), who had undergone chest radiography and at least one type of chest CT were investigated. The clinical and paraclinical manifestations were independently presented to three pulmonologists and their opinion regarding the necessity and type of CT prescription were documented. Also, the patient's history was presented to an expert chest radiologist and asked to rate the appropriateness of chest CT according to American colleague of radiologist (ACR) criteria. RESULTS In 127 cases (59%), at least 2 out of 3 pulmonologists had the same opinion on the necessity of performing CT scan regardless of CT scan type, in 89 cases (41%) the same CT type and in 38 (17.5%) cases other CT type was supposed. Based on ACR criteria, of total prescribed CTs, 49.5% were "usually not appropriate" and 31.5% of cases were "usually appropriate". Among 109 pulmonary CT angiography, 54 (49.5%) was usually not appropriate base on ACR criteria, which was the most frequent inappropriate requested CT type. CONCLUSION Considering the high rates of inappropriate utilization of chest CT scan in our teaching hospitals, implementation of the standard guideline at a different level and consulting with a pulmonologist, may prevent unnecessary chest CTs prescription and reduce harm to patients and the health system.
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20
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Gempeler A, Griswold DP, Rosseau G, Johnson WD, Kaseje N, Kolias A, Hutchinson PJ, Rubiano AM. An Umbrella Review With Meta-Analysis of Chest Computed Tomography for Diagnosis of COVID-19: Considerations for Trauma Patient Management. Front Med (Lausanne) 2022; 9:900721. [PMID: 35957847 PMCID: PMC9360488 DOI: 10.3389/fmed.2022.900721] [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] [Received: 03/21/2022] [Accepted: 06/06/2022] [Indexed: 12/01/2022] Open
Abstract
Background RT-PCR testing is the standard for diagnosis of COVID-19, although it has its suboptimal sensitivity. Chest computed tomography (CT) has been proposed as an additional tool with diagnostic value, and several reports from primary and secondary studies that assessed its diagnostic accuracy are already available. To inform recommendations and practice regarding the use of chest CT in the in the trauma setting, we sought to identify, appraise, and summarize the available evidence on the diagnostic accuracy of chest CT for diagnosis of COVID-19, and its application in emergency trauma surgery patients; overcoming limitations of previous reports regarding chest CT accuracy and discussing important considerations regarding its role in this setting. Methods We conducted an umbrella review using Living Overview of Evidence platform for COVID-19, which performs regular automated searches in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and more than 30 other sources. The review was conducted following the JBI methodology for systematic reviews. The Grading of Recommendations, Assessment, Development, and Evaluation approach for grading the certainty of the evidence is reported (registered in International Prospective Register of Systematic Reviews, CRD42020198267). Results Thirty studies that fulfilled selection criteria were included; 19 primary studies provided estimates of sensitivity (0.91, 95%CI = [0.88-0.93]) and specificity (0.73, 95%CI = [0.61; 0.82]) of chest CT for COVID-19. No correlation was found between sensitivities and specificities (ρ = 0.22, IC95% [-0.33; 0.66]). Diagnostic odds ratio was estimated at: DOR = 27.5, 95%CI (14.7; 48.5). Evidence for sensitivity estimates was graded as MODERATE, and for specificity estimates it was graded as LOW. Conclusion The value of chest CT appears to be that of an additional screening tool that can easily detect PCR false negatives, which are reportedly highly frequent. Upon the absence of PCR testing and impossibility to perform RT-PCR in trauma patients, chest CT can serve as a substitute with increased value and easy implementation. Systematic Review Registration [www.crd.york.ac.uk/prospero], identifier [CRD42020198267].
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Affiliation(s)
- Andrés Gempeler
- Centro de Investigaciones Clínicas, Fundación Valle del Lili, Cali, Colombia
| | - Dylan P. Griswold
- NIHR Global Health Research Group on Neurotrauma, University of Cambridge, Cambridge, United Kingdom
- Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, United Kingdom
| | - Gail Rosseau
- Department of Neurosurgery, George Washington University School of Medicine and Health Sciences, Washington, DC, United States
| | - Walter D. Johnson
- School of Medicine and Public Health, Loma Linda University, Loma Linda, CA, United States
| | | | - Angelos Kolias
- NIHR Global Health Research Group on Neurotrauma, University of Cambridge, Cambridge, United Kingdom
- Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, United Kingdom
| | - Peter J. Hutchinson
- NIHR Global Health Research Group on Neurotrauma, University of Cambridge, Cambridge, United Kingdom
- Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, United Kingdom
| | - Andres M. Rubiano
- NIHR Global Health Research Group on Neurotrauma, University of Cambridge, Cambridge, United Kingdom
- Neuroscience Institute, INUB-MEDITECH Research Group, El Bosque University, Bogotá, Colombia
- Neurological Surgery Service, Vallesalud Clinic, Cali, Colombia
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21
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Ismail A, Riachy M, Awali M, Farah F, Haddad S, Kerbage A, Aoun N, Sleilaty G. Pulmonary artery enlargement: an independent risk factor for mortality in hospitalized COVID-19 patients. Mayo Clin Proc Innov Qual Outcomes 2022; 6:399-408. [PMID: 35880237 PMCID: PMC9300717 DOI: 10.1016/j.mayocpiqo.2022.07.001] [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] [Indexed: 10/26/2022] Open
Abstract
Objective To assess whether baseline pulmonary artery diameter (PAD), obtained from Non-contrast non-gated computed tomography (NCCT), can be associated with COVID-19 outcomes. Patients and Methods This is a retrospective study of hospitalized COVID-19 patients admitted to Hôtel-Dieu de France university hospital (Beirut, Lebanon) between March 2020 and March 2021. PAD was measured on baseline NCCT. Various outcomes were assessed, including hospital length of stay, ICU admission, invasive mechanical ventilation, mortality, and post-covid functional scale (PCFS) status at discharge and at 2-month follow-up. Results 465 patients had a baseline NCCT, including 315 males (67.7%) with a mean age of 63.7±16 years. Baseline PAD was higher in critically ill patients admitted to the ICU (mean difference 0.8 mm [95% CI 0.4-1.59 mm]) and those receiving invasive mechanical ventilation (mean difference 1.1 mm [95% CI 0.11-2.04 mm]). PAD at baseline correlated significantly with hospital length of stay (r = 0.130, p=0.005), discharge status (r=0.117, p=0.023) and with PCFS at 2-month follow-up (r=0.121, p=0.021). Moreover, multivariable logistic regression showed that a PAD ≥ 24.5 mm independently predicted in-hospital all-cause mortality remained unaffected in COVID-19 patients (OR 2.07 (95% CI 1.05 - 4.09)). Conclusion Baseline PAD measurement using NCCT can be a useful prognostic parameter. Its measurement can help identify early severe cases and adapt the initial management of hospitalized Covid-19 patients.
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Affiliation(s)
- Anis Ismail
- Faculty of Medicine, Saint Joseph University of Beirut, Lebanon
| | - Moussa Riachy
- Division of Pulmonary and critical care medicine, Hotel Dieu de France Hospital, Beirut, Lebanon
| | - Mohamad Awali
- Division of Radiology, Faculty of Medicine, Hotel Dieu de France Hospital, Beirut, Lebanon
| | - Fadi Farah
- Division of Radiology, Faculty of Medicine, Hotel Dieu de France Hospital, Beirut, Lebanon
| | - Sarah Haddad
- Faculty of Medicine, Saint Joseph University of Beirut, Lebanon
| | - Anthony Kerbage
- Faculty of Medicine, Saint Joseph University of Beirut, Lebanon
| | - Noel Aoun
- Division of Radiology, Faculty of Medicine, Hotel Dieu de France Hospital, Beirut, Lebanon
| | - Ghassan Sleilaty
- Division of Cardiovascular Surgery, Hotel Dieu de France Hospital, Beirut, Lebanon
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22
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Pant S, Basnet B, Panta S, Tulachan NB, Rai K, Shrestha MS. Abnormal Chest Computed Tomography Findings among Admitted Symptomatic COVID-19 Patients in a Tertiary Care Centre: A Descriptive Cross-sectional Study. JNMA J Nepal Med Assoc 2022; 60:608-611. [PMID: 36705199 PMCID: PMC9297348 DOI: 10.31729/jnma.7529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/25/2022] [Indexed: 01/31/2023] Open
Abstract
Introduction COVID-19 has emerged as a pandemic and has varied clinical presentation. Computed Tomography scans of the chest play an important role in evaluating the lung parenchymal changes and aids in better planning the management of COVID-19 patients. The purpose of this study was to find the prevalence of abnormal chest computed tomography findings among admitted symptomatic COVID-19 patients in a tertiary care centre. Methods This descriptive cross-sectional study was conducted from 25 October 2020 to January 2021 in a tertiary care hospital. Ethical approval was taken from the Institutional Review Committee (Registration number: 348). Convenience sampling method was used. Chest computed tomography findings of the admitted symptomatic COVID-19 patients were evaluated for abnormal findings. Point estimate and 95% Confidence Interval were calculated. Results Among 153 patients, abnormal chest computed tomography findings were seen in 147 (96.07%) (92.99-99.15, 95% Confidence Interval). The findings of ground-glass opacities with consolidations were seen in 78 (53.06%) patients. Conclusions The prevalence of abnormal chest findings among symptomatic COVID-19 patients in our study was similar to the studies done in other countries in similar settings. Majority of the symptomatic COVID-19 patients showed abnormal chest computed tomography scan findings in the form of ground glass opacities and consolidations. Keywords COVID-19; Nepal; pneumonia; prevalence.
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Affiliation(s)
- Sujit Pant
- Department of Radiology, Nepalese Army Institute of Health Sciences, Syanobharyang, Kathmandu, Nepal
| | - Bina Basnet
- Department of Radiology, Nepalese Army Institute of Health Sciences, Syanobharyang, Kathmandu, Nepal
| | - Sujata Panta
- Department of Radiology, Nepalese Army Institute of Health Sciences, Syanobharyang, Kathmandu, Nepal
| | - Neeraj Basanta Tulachan
- Department of Radiology, Nepalese Army Institute of Health Sciences, Syanobharyang, Kathmandu, Nepal
| | - Kalpana Rai
- Department of Radiology, Nepalese Army Institute of Health Sciences, Syanobharyang, Kathmandu, Nepal
| | - Mukunda Singh Shrestha
- Department of Radiology, Nepalese Army Institute of Health Sciences, Syanobharyang, Kathmandu, Nepal
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23
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Impact of the COVID-19 Pandemic on Trends in Cardiothoracic Imaging. Radiol Res Pract 2022; 2022:7923228. [PMID: 35756751 PMCID: PMC9225849 DOI: 10.1155/2022/7923228] [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: 09/15/2021] [Revised: 01/06/2022] [Accepted: 01/09/2022] [Indexed: 01/28/2023] Open
Abstract
Introduction Here, we evaluate the effect of the COVID-19 pandemic on utilization of cardiothoracic imaging studies. Methods We queried our radiology record system to retrospectively identify numbers of specific key cardiothoracic imaging studies for five years prior and during the COVID-19 pandemic. Statistical analysis was performed to evaluate changes in the number of exams in 2020 and 2021 compared to 2019. Results Five-year retrospective analysis demonstrated progressive increases in nearly all cross-sectional studies. In 2020, daily chest radiograph utilization decreased with an overall number of daily radiographs of 406 (SD = 73.1) compared to 480 per day in 2019 (SD = 82.6) (p < 0.0001). Portable radiograph utilization was increased in 2020 averaging 320 (SD = 68.2) films daily in 2020 compared to 266 (SD = 29.1) in 2019 (p < 0.0001). Utilization of thoracic CT was decreased during the pandemic, with 21.8 (SD = 12.9) studies daily compared to 52.0 (SD = 21.4) (p < 0.0001) studies daily in 2019. Cardiac imaging utilization was also substantially decreased in 2020 compared to 2019, averaging a total of 3.8 (SD = 3.2) versus 10.8 (SD = 6.6) studies daily and 0.88 (SD = 1.7) versus 2.5 (SD = 2.3) studies daily for CT and MRI, respectively. Evaluation of cardiothoracic imaging for the subsequent 18 months after New York's entry to phase I recovery in June 2020 demonstrated that by one year after the emergence of COVID-19 imaging utilization had recovered to prepandemic levels. Cardiac imaging continued to increase throughout the chronic phase of the COVID-19 pandemic, reaching almost twice the prepandemic levels by the end of 2021. Conclusion COVID-19 has had far-reaching effects on medicine and public health. Here, we demonstrate decreases in all cross-sectional cardiothoracic imaging studies, closely mirroring findings in other fields during the height of the pandemic, which have since rebounded.
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Shim SR, Kim SJ, Hong M, Lee J, Kang MG, Han HW. Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis. Diagnostics (Basel) 2022; 12:1302. [PMID: 35741112 PMCID: PMC9222155 DOI: 10.3390/diagnostics12061302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 12/10/2022] Open
Abstract
(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791-0.914) and 0.879 (95% CrI, 0.784-0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960-0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.
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Affiliation(s)
- Sung Ryul Shim
- Department of Health and Medical Informatics, Kyungnam University College of Health Sciences, Changwon 51767, Korea;
| | - Seong-Jang Kim
- Department of Nuclear Medicine, Pusan National University Yangsan Hospital, Yangsan 50615, Korea;
- Department of Nuclear Medicine, College of Medicine, Pusan National University, Yangsan 50615, Korea
- BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50615, Korea
| | - Myunghee Hong
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea;
- Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam 13488, Korea
| | - Jonghoo Lee
- Department of Internal Medicine, Jeju National University Hospital, Jeju National University School of Medicine, Jeju 63241, Korea;
| | - Min-Gyu Kang
- Department of Internal Medicine, Chungbuk National University College of Medicine, Chungbuk National University Hospital, Cheongju 28644, Korea;
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea;
- Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam 13488, Korea
- Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam 13488, Korea
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25
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Li CF, Xu YD, Ding XH, Zhao JJ, Du RQ, Wu LZ, Sun WP. MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification. Comput Biol Med 2022; 144:105340. [PMID: 35305504 PMCID: PMC8912982 DOI: 10.1016/j.compbiomed.2022.105340] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/18/2022] [Accepted: 02/20/2022] [Indexed: 12/16/2022]
Abstract
The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.
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Affiliation(s)
- Cheng-Fan Li
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Yi-Duo Xu
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Xue-Hai Ding
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
| | - Jun-Juan Zhao
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Rui-Qi Du
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Li-Zhong Wu
- Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Rd, Shanghai, 200111, China
| | - Wen-Ping Sun
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Rd, Shanghai, 200233, China.
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Ibrahim DA, Zebari DA, Mohammed HJ, Mohammed MA. Effective hybrid deep learning model for COVID-19 patterns identification using CT images. EXPERT SYSTEMS 2022; 39:e13010. [PMID: 35942177 PMCID: PMC9348188 DOI: 10.1111/exsy.13010] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 05/31/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over-and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. These three described pre-trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID-19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state-of-the-art studies. Because of its effectiveness in accurately screening COVID-19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals.
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Affiliation(s)
- Dheyaa Ahmed Ibrahim
- Communications Engineering Techniques Department, Information Technology CollageImam Ja'afar Al‐Sadiq UniversityBaghdadIraq
| | - Dilovan Asaad Zebari
- Department of Computer Science, College of ScienceNawroz UniversityDuhok Kurdistan RegionIraq
| | | | - Mazin Abed Mohammed
- Information systems Department, College of Computer Science and Information TechnologyUniversity of AnbarAl AnbarIraq
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Kuo KM, Talley PC, Chang CS. The Accuracy of Machine Learning Approaches Using Non-image Data for the Prediction of COVID-19: A Meta-Analysis. Int J Med Inform 2022; 164:104791. [PMID: 35594810 PMCID: PMC9098530 DOI: 10.1016/j.ijmedinf.2022.104791] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 04/08/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022]
Abstract
Objective COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. Materials and methods A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. Results A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. Conclusions Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.
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De Rosa L, L'Abbate S, Kusmic C, Faita F. Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting. Artif Intell Med Imaging 2022; 3:42-54. [DOI: 10.35711/aimi.v3.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/22/2022] [Accepted: 04/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.
AIM To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.
METHODS A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.
RESULTS As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.
CONCLUSION Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.
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Affiliation(s)
- Laura De Rosa
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| | - Serena L'Abbate
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
- Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa 56124, Italy
| | - Claudia Kusmic
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| | - Francesco Faita
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
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Ravindra Naik B, Sakalecha AK, B N S, A C, Kale R M, Uhasai K. Computed Tomography Severity Scoring on High-Resolution Computed Tomography Thorax and Inflammatory Markers With COVID-19 Related Mortality in a Designated COVID Hospital. Cureus 2022; 14:e24190. [PMID: 35592193 PMCID: PMC9110092 DOI: 10.7759/cureus.24190] [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] [Accepted: 04/16/2022] [Indexed: 11/05/2022] Open
Abstract
Introduction Radiological Society of the Netherlands introduced the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) to diagnose COVID-19 severity. However, data regarding the same is very limited. Objectives The objective of this study was to correlate the computed tomography severity scoring (CTSS) on high-resolution computed tomography (HRCT) thorax and inflammatory markers with COVID-19 related mortality. Methods A retrospective observational study was conducted in a tertiary center between June 2020 to May 2021 among 2343 adult patients at the department of radio-diagnosis with suspected and confirmed COVID-19 cases who received an HRCT thorax. Data was collected retrospectively from the records regarding age, sex, and information regarding inflammatory markers such as C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), D-dimer, and neutrophil-to-lymphocyte ratio. Information on HRCT thorax of patients was reviewed for radiological suspicion of COVID-19 related lung changes using CO-RADS scoring and severity of lung involvement using CT-severity scoring. Data was analyzed using SPSS version 22 (IBM Inc., Armonk, New York). Results The mean age was 51.69 ± 16.02 years, and most of the study participants were male (1592, 67.95%). The majority (999, 42.64%) had diabetes as a comorbidity. The reverse transcription polymerase chain reaction (RT-PCR) test was positive in 1571 (67.05%) participants. The majority (1571, 67.05%) had a CO-RADS score of six, and only 150 (6.40%) had CO-RADS score of four. The CT severity score was normal in 147 (6.27%), mild in 724 (30.90%), moderate in 903 (38.54%), and severe in 569 (24.29%) participants. The CRP levels were moderate in 1200 (51.22%) and severe in 428 (18.27%) participants. The mean ferritin, D-dimer and interleukin-6 (IL-6) levels were 321.83 ± 266.42 ng/ml, 1.51 ± 0.85mg/l, and 323.05 ± 95.52pg/ml, respectively. The mean length of hospital stay was 10.25 ± 6.52 days. Most of them (1926 out of 2343, 82.20%) survived, and nearly 417 out of 2343 (17.80%) died. Out of 2343, 569 participants had severe CT severity scores, out of which 205 (36.03%) died, and 364 (63.97%) participants survived. Conclusion A positive correlation was found between CT severity scoring on HRCT thorax and inflammatory markers with COVID-19 related mortality and can be used in early diagnosis and timely management of COVID-19 positive patients.
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Affiliation(s)
| | | | - Sunil B N
- Community Medicine, Sri Devaraj Urs Medical College, Kolar, IND
| | - Chaithanya A
- Radio-Diagnosis, Sri Devaraj Urs Medical College, Kolar, IND
| | - Mahima Kale R
- Radio-Diagnosis, Sri Devaraj Urs Medical College, Kolar, IND
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Should thorax thin section computed tomography (TSCT) be a standard diagnostic procedure in the evaluation of potential kidney transplant recipients - lessons learnt from COVID-19 pandemia. Transplant Proc 2022; 54:890-896. [PMID: 35752505 PMCID: PMC9023338 DOI: 10.1016/j.transproceed.2022.03.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 11/23/2022]
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Liu G, Chen Y, Runa A, Liu J. Diagnostic performance of CO-RADS for COVID-19: a systematic review and meta-analysis. Eur Radiol 2022; 32:4414-4426. [PMID: 35348865 PMCID: PMC8961267 DOI: 10.1007/s00330-022-08576-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/12/2021] [Accepted: 01/08/2022] [Indexed: 12/13/2022]
Abstract
Objectives To investigate the diagnostic performance of the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) for detecting COVID-19. Methods We searched PubMed, EMBASE, MEDLINE, Web of Science, Cochrane Library, and Scopus database until September 21, 2021. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses. Results We included 24 studies with 8382 patients. The pooled sensitivity and specificity and the area under the curve (AUC) of CO-RADS ≥ 3 for detecting COVID-19 were 0.89 (95% confidence interval (CI) 0.85–0.93), 0.68 (95% CI 0.60–0.75), and 0.87 (95% CI 0.84–0.90), respectively. The pooled sensitivity and specificity and AUC of CO-RADS ≥ 4 were 0.83 (95% CI 0.79–0.87), 0.84 (95% CI 0.78–0.88), and 0.90 (95% CI 0.87–0.92), respectively. Cochran’s Q test (p < 0.01) and Higgins I2 heterogeneity index revealed considerable heterogeneity. Studies with both symptomatic and asymptomatic patients had higher specificity than those with only symptomatic patients using CO-RADS ≥ 3 and CO-RADS ≥ 4. Using CO-RADS ≥ 4, studies with participants aged < 60 years had higher sensitivity (0.88 vs. 0.80, p = 0.02) and lower specificity (0.77 vs. 0.87, p = 0.01) than studies with participants aged > 60 years. Conclusions CO-RADS has favorable performance in detecting COVID-19. CO-RADS ≥ 3/4 might be applied as cutoff values given their high sensitivity and specificity. However, there is a need for more well-designed studies on CO-RADS. Key Points • CO-RADS shows a favorable performance in detecting COVID-19. • CO-RADS ≥ 3 had a high sensitivity 0.89 (95% CI 0.85–0.93), and it may prove advantageous in screening the potentially infected people to prevent the spread of COVID-19. • CO-RADS ≥ 4 had high specificity 0.84 (95% CI 0.78–0.88) and may be more suitable for definite diagnosis of COVID-19.
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Monday HN, Li J, Nneji GU, Nahar S, Hossin MA, Jackson J, Ejiyi CJ. COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12030741. [PMID: 35328294 PMCID: PMC8946937 DOI: 10.3390/diagnostics12030741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/07/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.
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Affiliation(s)
- Happy Nkanta Monday
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Jianping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
- Correspondence:
| | - Grace Ugochi Nneji
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (C.J.E.)
| | - Saifun Nahar
- Department of Information System and Technology, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Md Altab Hossin
- School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Jehoiada Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (C.J.E.)
| | - Chukwuebuka Joseph Ejiyi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (C.J.E.)
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Chernikova NA, Shelesko EV, Sharipov OI, Ershova ON, Kalinin PL, Kutin MA, Fomichev DV. Differential diagnosis of pneumonia as a complication of nasal liquorrhea in the context of the COVID-19 pandemic: Case report. TERAPEVT ARKH 2022; 94:420-426. [DOI: 10.26442/00403660.2022.03.201404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Indexed: 11/22/2022]
Abstract
Nasal liquorrhea the outflow of cerebrospinal fluid from the cerebrospinal fluid spaces of the cranial cavity into the nasal cavity or paranasal sinuses due to the presence of a congenital or acquired defect in the bones of the skull base and meninges of various etiologies. Nasal liquorrhea leads to potentially fatal complications: meningitis, meningoencephalitis, pneumocephalus, brain abscess. Also, with nasal liquorrhea, less dangerous complications may occur: aspiration bronchopneumonia and gastritis. The article presents a case of aspiration pneumonia in two patients with nasal liquorrhea treated at the Burdenko National Medical Research Center for Neurosurgery during the COVID-19 pandemic. Both patients noted the profuse nature of the nasal liquorrhea, complained of coughing in a horizontal position. In both cases, no RNA virus (SARS-CoV-2) was detected during the polymerase chain reaction. Antibodies (IgG, M) to coronavirus were not detected. Computed tomography of the chest organs in both cases revealed areas of frosted glass darkening. Since no data was obtained for coronavirus infection (negative tests for coronavirus, lack of antibodies), changes in the lungs were interpreted as a consequence of constant aspiration of CSF. The patients were admitted to a separate ward. Both patients underwent endoscopic endonasal plasty of the skull base defect. The postoperative period in both cases was uneventful. In both cases, the patients underwent computer tomography scan of the chest organs one month later. On the photographs, the signs of pneumonia completely regressed.
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Aghamirza Moghim Aliabadi H, Eivazzadeh‐Keihan R, Beig Parikhani A, Fattahi Mehraban S, Maleki A, Fereshteh S, Bazaz M, Zolriasatein A, Bozorgnia B, Rahmati S, Saberi F, Yousefi Najafabadi Z, Damough S, Mohseni S, Salehzadeh H, Khakyzadeh V, Madanchi H, Kardar GA, Zarrintaj P, Saeb MR, Mozafari M. COVID-19: A systematic review and update on prevention, diagnosis, and treatment. MedComm (Beijing) 2022; 3:e115. [PMID: 35281790 PMCID: PMC8906461 DOI: 10.1002/mco2.115] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 01/09/2023] Open
Abstract
Since the rapid onset of the COVID-19 or SARS-CoV-2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the virus in order to determine the best ways to diagnosis of virus and thereof formulate effective drugs or vaccines to combat the disease. The emergence of novel diagnostic and therapeutic techniques considering the multiplicity of reports from one side and contradictions in assessments from the other side necessitates instantaneous updates on the progress of clinical investigations. There is also growing public anxiety from time to time mutation of COVID-19, as reflected in considerable mortality and transmission, respectively, from delta and Omicron variants. We comprehensively review and summarize different aspects of prevention, diagnosis, and treatment of COVID-19. First, biological characteristics of COVID-19 were explained from diagnosis standpoint. Thereafter, the preclinical animal models of COVID-19 were discussed to frame the symptoms and clinical effects of COVID-19 from patient to patient with treatment strategies and in-silico/computational biology. Finally, the opportunities and challenges of nanoscience/nanotechnology in identification, diagnosis, and treatment of COVID-19 were discussed. This review covers almost all SARS-CoV-2-related topics extensively to deepen the understanding of the latest achievements (last updated on January 11, 2022).
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Affiliation(s)
- Hooman Aghamirza Moghim Aliabadi
- Protein Chemistry LaboratoryDepartment of Medical BiotechnologyBiotechnology Research CenterPasteur Institute of IranTehranIran
- Advance Chemical Studies LaboratoryFaculty of ChemistryK. N. Toosi UniversityTehranIran
| | | | - Arezoo Beig Parikhani
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | | | - Ali Maleki
- Department of ChemistryIran University of Science and TechnologyTehranIran
| | | | - Masoume Bazaz
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | | | | | - Saman Rahmati
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | - Fatemeh Saberi
- Department of Medical BiotechnologySchool of Advanced Technologies in MedicineShahid Beheshti University of Medical SciencesTehranIran
| | - Zeinab Yousefi Najafabadi
- Department of Medical BiotechnologySchool of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
- ImmunologyAsthma & Allergy Research InstituteTehran University of Medical SciencesTehranIran
| | - Shadi Damough
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | - Sara Mohseni
- Non‐metallic Materials Research GroupNiroo Research InstituteTehranIran
| | | | - Vahid Khakyzadeh
- Department of ChemistryK. N. Toosi University of TechnologyTehranIran
| | - Hamid Madanchi
- School of MedicineSemnan University of Medical SciencesSemnanIran
- Drug Design and Bioinformatics UnitDepartment of Medical BiotechnologyBiotechnology Research CenterPasteur Institute of IranTehranIran
| | - Gholam Ali Kardar
- Department of Medical BiotechnologySchool of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
- ImmunologyAsthma & Allergy Research InstituteTehran University of Medical SciencesTehranIran
| | - Payam Zarrintaj
- School of Chemical EngineeringOklahoma State UniversityStillwaterOklahomaUSA
| | - Mohammad Reza Saeb
- Department of Polymer TechnologyFaculty of ChemistryGdańsk University of TechnologyGdańskPoland
| | - Masoud Mozafari
- Department of Tissue Engineering & Regenerative MedicineIran University of Medical SciencesTehranIran
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Wong PK, Yan T, Wang H, Chan IN, Wang J, Li Y, Ren H, Wong CH. Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network. Biomed Signal Process Control 2022; 73:103415. [PMID: 34909050 PMCID: PMC8660060 DOI: 10.1016/j.bspc.2021.103415] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/31/2021] [Accepted: 11/28/2021] [Indexed: 12/13/2022]
Abstract
The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China
| | - Huaqiao Wang
- Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau
| | - Jiangtao Wang
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Yang Li
- Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Hao Ren
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau
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Di Gioia CC, Artusi N, Xotta G, Bonsano M, Sisto UG, Tecchiolli M, Orso D, Cominotto F, Amore G, Meduri S, Copetti R. Lung ultrasound in ruling out COVID-19 pneumonia in the ED: a multicentre prospective sensitivity study. Emerg Med J 2022; 39:199-205. [PMID: 34937709 PMCID: PMC8704061 DOI: 10.1136/emermed-2020-210973] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 12/13/2021] [Indexed: 01/11/2023]
Abstract
PURPOSE Early diagnosis of COVID-19 has a crucial role in confining the spread among the population. Lung ultrasound (LUS) was included in the diagnostic pathway for its high sensitivity, low costs, non-invasiveness and safety. We aimed to test the sensitivity of LUS to rule out COVID-19 pneumonia (COVIDp) in a population of patients with suggestive symptoms. METHODS Multicentre prospective observational study in three EDs in Northeastern Italy during the first COVID-19 outbreak. A convenience sample of 235 patients admitted to the ED for symptoms suggestive COVIDp (fever, cough or shortness of breath) from 17 March 2020 to 26 April 2020 was enrolled. All patients underwent a sequential assessment involving: clinical examination, LUS, CXR and arterial blood gas. The index test under investigation was a standardised protocol of LUS compared with a pragmatic composite reference standard constituted by: clinical gestalt, real-time PCR test, radiological and blood gas results. Of the 235 enrolled patients, 90 were diagnosed with COVIDp according to the reference standard. RESULTS Among the patients with suspected COVIDp, the prevalence of SARS-CoV-2 was 38.3%. The sensitivity of LUS for diagnosing COVIDp was 85.6% (95% CI 76.6% to 92.1%); the specificity was 91.7% (95% CI 86.0% to 95.7%). The positive predictive value and the negative predictive value were 86.5% (95%CI 78.8% to 91.7%) and 91.1% (95% CI 86.1% to 94.4%) respectively. The diagnostic accuracy of LUS for COVIDp was 89.4% (95% CI 84.7% to 93.0%). The positive likelihood ratio was 10.3 (95% CI 6.0 to 17.9), and the negative likelihood ratio was 0.16 (95% CI 0.1 to 0.3). CONCLUSION In a population with high SARS-CoV-2 prevalence, LUS has a high sensitivity (and negative predictive value) enough to rule out COVIDp in patients with suggestive symptoms. The role of LUS in diagnosing patients with COVIDp is perhaps even more promising. Nevertheless, further research with adequately powered studies is needed. TRIAL REGISTRATION NUMBER NCT04370275.
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Affiliation(s)
- Carmine Cristiano Di Gioia
- Department of Emergency Medicine, Trieste University Integrated Healthcare Company, Trieste, Friuli-Venezia Giulia, Italy
| | - Nicola Artusi
- Department of Emergency Medicine, Trieste University Integrated Healthcare Company, Trieste, Friuli-Venezia Giulia, Italy
| | - Giovanni Xotta
- Department of Emergency Medicine, University of Verona, Verona, Veneto, Italy
| | - Marco Bonsano
- Department of Emergency Medicine, Barts Health NHS Trust, London, UK
| | - Ugo Giulio Sisto
- Department of Emergency Medicine, Trieste University Integrated Healthcare Company, Trieste, Friuli-Venezia Giulia, Italy
| | - Marzia Tecchiolli
- Department of Emergency Medicine, Trieste University Integrated Healthcare Company, Trieste, Friuli-Venezia Giulia, Italy
| | - Daniele Orso
- Department of Medicine (DAME), University of Udine, Udine, Friuli-Venezia Giulia, Italy
| | - Franco Cominotto
- Department of Emergency Medicine, Trieste University Integrated Healthcare Company, Trieste, Friuli-Venezia Giulia, Italy
| | - Giulia Amore
- Department of Emergency Medicine, Ospedale Civile di Latisana, Latisana, Friuli-Venezia Giulia, Italy
| | - Stefano Meduri
- Department of Radiology, Ospedale Civile di Latisana, Latisana, Friuli-Venezia Giulia, Italy
| | - Roberto Copetti
- Department of Emergency Medicine, Ospedale Civile di Latisana, Latisana, Friuli-Venezia Giulia, Italy
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COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network. Healthcare (Basel) 2022; 10:healthcare10030422. [PMID: 35326900 PMCID: PMC8949056 DOI: 10.3390/healthcare10030422] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/16/2022] [Accepted: 02/20/2022] [Indexed: 12/23/2022] Open
Abstract
Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.
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Liu J, Wang Y, He G, Wang X, Sun M. Quantitative CT comparison between COVID-19 and mycoplasma pneumonia suspected as COVID-19: a longitudinal study. BMC Med Imaging 2022; 22:21. [PMID: 35125096 PMCID: PMC8818096 DOI: 10.1186/s12880-022-00750-4] [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: 10/05/2021] [Accepted: 02/01/2022] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The purpose of this study was to compare imaging features between COVID-19 and mycoplasma pneumonia (MP). MATERIALS AND METHODS The data of patients with mild COVID-19 and MP who underwent chest computed tomography (CT) examination from February 1, 2020 to April 17, 2020 were retrospectively analyzed. The Pneumonia-CT-LKM-PP model based on a deep learning algorithm was used to automatically quantify the number, volume, and involved lobes of pulmonary lesions, and longitudinal changes in quantitative parameters were assessed in three CT follow-ups. RESULTS A total of 10 patients with mild COVID-19 and 13 patients with MP were included in this study. There was no difference in lymphocyte counts at baseline between the two groups (1.43 ± 0.45 vs. 1.44 ± 0.50, p = 0.279). C-reactive protein levels were significantly higher in MP group than in COVID-19 group (p < 0.05). The number, volume, and involved lobes of pulmonary lesions reached a peak in 7-14 days in the COVID-19 group, but there was no peak or declining trend over time in the MP group (p < 0.05). CONCLUSION Based on the longitudinal changes of quantitative CT, pulmonary lesions peaked at 7-14 days in patients with COVID-19, and this may be useful to distinguish COVID-19 from MP and evaluate curative effects and prognosis.
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Affiliation(s)
- Junzhong Liu
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China.
- Department of Medical Imaging, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, 7 Yuanxiao Street, Weifang City, 261041, Shandong Province, People's Republic of China.
| | - Yuzhen Wang
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
| | - Guanghui He
- Department of Interventional Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
| | - Xinhua Wang
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
| | - Minfeng Sun
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
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Stammes MA, Lee JH, Meijer L, Naninck T, Doyle-Meyers LA, White AG, Borish HJ, Hartman AL, Alvarez X, Ganatra S, Kaushal D, Bohm RP, le Grand R, Scanga CA, Langermans JAM, Bontrop RE, Finch CL, Flynn JL, Calcagno C, Crozier I, Kuhn JH. Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates. Trends Mol Med 2022; 28:123-142. [PMID: 34955425 PMCID: PMC8648672 DOI: 10.1016/j.molmed.2021.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/01/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022]
Abstract
Chest X-ray (CXR), computed tomography (CT), and positron emission tomography-computed tomography (PET-CT) are noninvasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed non-human primates (NHPs) to complement virological assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures (MCMs). However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.
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Affiliation(s)
- Marieke A Stammes
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands.
| | - Ji Hyun Lee
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - Lisette Meijer
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands
| | - Thibaut Naninck
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, 92260 Fontenay-aux-Roses, France
| | - Lara A Doyle-Meyers
- Tulane National Primate Research Center, Covington, LA 70433, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Alexander G White
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - H Jacob Borish
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Amy L Hartman
- Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Infectious Diseases and Microbiology, School of Public Health, University of Pittsburgh, Pitt Public Health, Pittsburgh, PA 15261, USA
| | - Xavier Alvarez
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | | | - Deepak Kaushal
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | - Rudolf P Bohm
- Tulane National Primate Research Center, Covington, LA 70433, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Roger le Grand
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, 92260 Fontenay-aux-Roses, France
| | - Charles A Scanga
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jan A M Langermans
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands; Department Population Health Sciences, Division of Animals in Science and Society, Faculty of Veterinary Medicine, Utrecht University, 3584 CL, Utrecht, The Netherlands
| | - Ronald E Bontrop
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands; Department of Biology, Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH, Utrecht, The Netherlands
| | - Courtney L Finch
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - JoAnne L Flynn
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Claudia Calcagno
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - Ian Crozier
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
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Calvi C, Ferreira FF, Lyrio L, Baptista RDM, Zanoni BB, Junger YO, Barros WH, Volpato R, Mule Júnior L, Rosa Júnior M. COVID-19 findings in chest computed tomography. Rev Assoc Med Bras (1992) 2022; 67:1409-1414. [PMID: 35018967 DOI: 10.1590/1806-9282.20210414] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/16/2021] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE The aim of this study was to describe chest computed tomography image findings in patients with COVID-19. METHODS The chest computed tomography scans of 453 hospitalized patients with confirmed COVID-19 were collected at two tertiary care Brazilian hospitals. Demographics and clinical data were extracted from the electronic record medical system. RESULTS The main chest computed tomography findings were ground-glass opacities (92.5%), consolidation (79.2%), crazy-paving pattern (23.9%), parenchymal bands (50%), septal thickening (43.5%), and inverted halo sign (3.5%). Of the 453 hospitalized patients, 136 (30%) died. In this group, ground-glass opacities (94.1%), consolidation (89.7%), septal thickening (58.1%), crazy-paving pattern (52.2%), and parenchymal bands (39.7%) were the most common imaging findings. CONCLUSIONS In a dynamic disease with a broad clinical spectrum such as COVID-19, radiologists can cooperate in a better patient management. On wisely indicated chest computed tomography scans, the fast identification of poor prognosis findings could advise patient management through hospital care facilities and clinical team decisions.
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Affiliation(s)
- Camila Calvi
- Universidade Federal do Espírito Santo, Hospital Universitário Cassiano Antônio Moraes, Division of Radiology - Vitória (ES), Brazil
| | - Fernanda Filetti Ferreira
- Universidade Federal do Espírito Santo, Hospital Universitário Cassiano Antônio Moraes, Division of Radiology - Vitória (ES), Brazil
| | - Lucas Lyrio
- Universidade Federal do Espírito Santo, Hospital Universitário Cassiano Antônio Moraes, Division of Radiology - Vitória (ES), Brazil
| | - Rodrigo de Melo Baptista
- Universidade Federal do Espírito Santo, Hospital Universitário Cassiano Antônio Moraes, Division of Radiology - Vitória (ES), Brazil
| | - Barbara Binda Zanoni
- Hospital Estadual Jayme Santos Neves, Division of Radiology - Serra (ES), Brazil
| | - Ynara Olivier Junger
- Hospital Estadual Jayme Santos Neves, Division of Radiology - Serra (ES), Brazil
| | - Wagner Haese Barros
- Hospital Estadual Jayme Santos Neves, Division of Radiology - Serra (ES), Brazil
| | - Ricardo Volpato
- Hospital Estadual Jayme Santos Neves, Division of Radiology - Serra (ES), Brazil
| | - Libório Mule Júnior
- Hospital Estadual Jayme Santos Neves, Division of Radiology - Serra (ES), Brazil
| | - Marcos Rosa Júnior
- Universidade Federal do Espírito Santo, Hospital Universitário Cassiano Antônio Moraes, Division of Radiology - Vitória (ES), Brazil
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Lonikar A, Diwan S, Sancheti P. Low-volume ultrasound-guided supraclavicular block in a multicomorbid patient for emergency vascular surgery – In COVID-19 era. J Anaesthesiol Clin Pharmacol 2022; 38:S125-S127. [PMID: 36060162 PMCID: PMC9438842 DOI: 10.4103/joacp.joacp_545_20] [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: 09/16/2020] [Accepted: 11/29/2020] [Indexed: 11/04/2022] Open
Abstract
Supraclavicular block is the most commonly used block in upper limb surgeries, right from the day it was introduced into clinical practice in Germany by Kulenkampff in 1911. The block underwent many changes in its application due to the advent of peripheral nerve stimulator and ultrasonographic application in regional anesthesia. This case report focuses on supraclavicular block’s application in a multicomorbid patient, the drug dose required, and how the scope of regional anesthesia can be extended in times of pandemic, like coronavirus disease 2019 (COVID-19), in coming future.
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Ghaemian N, Ghadimi R, Soraya S, Mouodi S. Chest computed tomography findings in more than 4,000 non-hospitalized suspected COVID-19 patients. CASPIAN JOURNAL OF INTERNAL MEDICINE 2022; 13:187-192. [PMID: 35872675 PMCID: PMC9272952 DOI: 10.22088/cjim.13.0.187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/31/2021] [Accepted: 04/06/2021] [Indexed: 10/31/2022]
Abstract
Background When the first wave of COVID-19 outbreak occurred, the infrastructure for definitive detection of the disease through real-time polymerase chain reaction (RT-PCR) was not yet available in many regions, and a large proportion of suspected patients were inevitably referred to radiology centers to provide a chest CT scan. This research was conducted to describe chest CT characteristics in patients who underwent chest CT during the first weeks of COVID-19 outbreak in Babol, Iran. Methods All non-hospitalized suspected COVID-19 patients referred to the state radiologic clinic to perform chest CT from March 8, 2020 to March 28, 2020 have been enrolled in this observational study. All CT scans were reviewed by a faculty member radiologist with approximately 20 years of experience. Results Totally, 2,207 (52.3%) men and 2016 (47.7%) women have been examined. Imaging characteristics in 2292 (54.3%) individuals illustrated a highly suggestive sign of COVID-19 infection while 1869 (44.3%) had a normal chest CT scan. 1813 cases (77.00%) had bilateral involvement and 541 cases (23.00%) were infected unilaterally; Also, 1727 (73.36%) patients had left-sided involvement. Lung field involvement in 2036 (86.49%) patients was less than 20%. Ground glass opacity had a sensitivity, specificity, PPV, NPV, LR+ and LR- of 99%, 96%, 96%, 98%, 22 and 0.01, respectively, for categorization of a patient as a COVID-19 case. These values were 99%, 73%, 70%, 99%, 3.72% and 0.01%, respectively for consolidations. Conclusion Although, RT-PCR is still introduced as the gold standard method for definite diagnosis, diagnostic accuracy of chest CT in COVID-19 detection is considerable.
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Affiliation(s)
- Naser Ghaemian
- Cancer Research Center, Babol University of Medical Sciences, Babol, Iran
| | - Reza Ghadimi
- Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Soraya Soraya
- Department of Epidemiology and Biostatistics, School of Public Health, Babol University of Medical Sciences, Babol, Iran
| | - Simin Mouodi
- Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran,Correspondence: Simin Mouodi, Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran. E-mail: , Tel: 0098 1132190624, Fax: 0098 1132190624
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Alathari MJA, Al Mashhadany Y, Mokhtar MHH, Burham N, Bin Zan MSD, A Bakar AA, Arsad N. Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques. SENSORS (BASEL, SWITZERLAND) 2021; 21:8362. [PMID: 34960456 PMCID: PMC8704003 DOI: 10.3390/s21248362] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022]
Abstract
Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.
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Affiliation(s)
- Mohammed Jawad Ahmed Alathari
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq;
| | - Mohd Hadri Hafiz Mokhtar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Norhafizah Burham
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
| | - Mohd Saiful Dzulkefly Bin Zan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
| | - Norhana Arsad
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia; (M.J.A.A.); (M.H.H.M.); (N.B.); (M.S.D.B.Z.); (A.A.A.B.)
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Patrucco F, Zeppegno P, Baricich A, Gramaglia CM, Balbo PE, Falaschi Z, Carriero A, Cuneo D, Pirisi M, Bellan M. Long-lasting consequences of Coronavirus disease 19 pneumonia: a systematic review. Minerva Med 2021; 113:158-171. [PMID: 34856780 DOI: 10.23736/s0026-4806.21.07594-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
INTRODUCTION Coronavirus Disease 19 (Covid-19) is an infectious disease caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have plenty of data about the clinical features of the disease's acute phase, while little is known about the long-term consequences on survivors. EVIDENCE ACQUISITION We aimed to review systematically emerging evidence about clinical and functional consequences of Covid-19 pneumonia months after hospital discharge. EVIDENCE SYNTHESIS Current evidence supports the idea that a high proportion of Covid-19 survivors complain of symptoms months after the acute illness phase, being fatigue and reduced tolerance to physical effort the most frequently reported symptom. The strongest association for these symptoms is with the female gender, while disease severity seems less relevant. Respiratory symptoms are associated with a decline in respiratory function and, conversely, seem to be more frequent in those who experienced a more severe acute pneumonia. Current evidence highlighted a persistent motor impairment which is, again, more prevalent among those survivors who experienced a more severe acute phase of the disease. Additionally, the persistence of symptoms is a primary determinant of mental health outcome, with anxiety, depression, sleep disturbances, and post-traumatic stress symptoms being commonly reported in Covid-19 survivors. CONCLUSIONS Current literature highlights the importance of a multidisciplinary approach to Coronavirus Disease 19 since the sequelae appear to involve different organs and systems. Given the pandemic outbreak's size, this is a critical public health issue: a better insight on this topic should inform clinical decisions about the modalities of follow-up for Covid-19 survivors.
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Affiliation(s)
- Filippo Patrucco
- Pneumology Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Patrizia Zeppegno
- Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.,Psychiatry Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Alessio Baricich
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy - .,Physical and Rehabilitation Medicine, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Carla M Gramaglia
- Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.,Psychiatry Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Piero E Balbo
- Pneumology Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Zeno Falaschi
- Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.,Radiology Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Alessandro Carriero
- Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.,Radiology Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Daria Cuneo
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy.,Physical and Rehabilitation Medicine, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Mario Pirisi
- Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.,Internal Medicine Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
| | - Mattia Bellan
- Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.,Internal Medicine Department, Ospedale Maggiore della Carità University Hospital, Novara, Italy
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Korkmaz I, Dikmen N, Keleş FO, Bal T. Chest CT in COVID-19 pneumonia: correlations of imaging findings in clinically suspected but repeatedly RT-PCR test-negative patients. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [PMCID: PMC8022619 DOI: 10.1186/s43055-021-00481-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background To emphasize the importance of CT in the diagnosis of COVID-19 disease by comparing the thoracic CT findings of COVID-19 patients with positive RT-PCR results and patients with clinical suspicion of COVID-19 but with negative RT-PCR results. Results In our study, COVID-19 patients with positive RT-PCR results (RT-PCR (+) group) and patients with clinical suspicion of COVID-19 but negative RT-PCR results (RT-PCR (−) group) were compared in terms of CT findings. In CT images, ground-glass opacity and ground-glass opacity + patchy consolidation were the most common lesion patterns in both groups. No statistically significant differences in the rates and types of lesion patterns were observed between the two groups. In both groups, lesion distributions and distribution patterns were similarly frequent in the bilateral, peripheral, and lower lobe distributions. Among the 39 patients who underwent follow-up CT imaging in the first or second month, a regression in lesion number and density was detected in 18 patients from both groups. Consolidations were completely resorbed in 16 of these patients, and five patients had newly developed fibrotic changes. The follow-up CT examination of 16 patients was normal. Conclusions Due to the false-negative rate of RT-PCR tests caused by various reasons, clinically suspected COVID-19 patients with a contact history should be examined with CT scans, even if RT-PCR tests are negative. If the CT findings are positive, these patients should not be removed from isolation.
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Stessel B, Callebaut I, Polus F, Geebelen L, Evers S, Ory JP, Magerman K, Souverijns G, Braeken G, Ramaekers D, Cox J. Evaluation of a comprehensive pre-procedural screening protocol for COVID-19 in times of a high SARS CoV-2 prevalence: a prospective cross-sectional study. Ann Med 2021; 53:337-344. [PMID: 33583292 PMCID: PMC7889170 DOI: 10.1080/07853890.2021.1878272] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 01/13/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND To minimise the risk of COVID-19 transmission, an ambulant screening protocol for COVID-19 in patients before admission to the hospital was implemented, combining the SARS CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR) on a nasopharyngeal swab, a chest computed tomography (CT) and assessment of clinical symptoms. The aim of this study was to evaluatethe diagnostic yield and the proportionality of this pre-procedural screeningprotocol. METHODS In this mono-centre, prospective, cross-sectional study, all patients admitted to the hospital between 22nd April 2020 until 14th May 2020 for semi-urgent surgery, haematological or oncological treatment, or electrophysiological investigationunderwent a COVID-19 screening 2 days before their procedure. At a 2-week follow-up, the presence of clinical symptoms was evaluated by telephone as a post-hoc evaluation of the screening approach.Combined positive RT-PCR assay and/or positive chest CT was used as gold standard. Post-procedural outcomes of all patients diagnosed positive for COVID-19 were assessed. RESULTS In total,528 patients were included of which 20 (3.8%) were diagnosed as COVID-19 positive and 508 (96.2%) as COVID-19 negative. 11 (55.0%) of COVID-19 positive patients had only a positive RT-PCR assay, 3 (15.0%) had only a positive chest CT and 6 (30%) had both a positive RT-PCR assay and chest CT. 10 out of 20 (50.0%) COVID-19 positive patients reported no single clinical symptom at the screening. At 2 week follow-up, 50% of these patients were still asymptomatic. 37.5% of all COVID-19 negative patients were symptomatic at screening. In the COVID-19 negative group without symptoms at screening, 78 (29.3%) patients developed clinical symptoms at a 2-week follow-up. CONCLUSION This study suggests that routine chest CT and assessment of self-reported symptoms have limited value in the preprocedural COVID-19 screening due to low sensitivity and/or specificity.
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Affiliation(s)
- Björn Stessel
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
- UHasselt, Faculty of Medicine and Life Sciences, LCRC, Diepenbeek, Belgium
| | - Ina Callebaut
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
- UHasselt, Faculty of Medicine and Life Sciences, LCRC, Diepenbeek, Belgium
| | - Fréderic Polus
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
| | - Laurien Geebelen
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
| | - Stefan Evers
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
| | - Jean-Paul Ory
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
| | - Koen Magerman
- UHasselt, Faculty of Medicine and Life Sciences, LCRC, Diepenbeek, Belgium
- Clinical Laboratory, Jessa Hospital, Hasselt, Belgium
| | | | - Geert Braeken
- Department of Intensive Care and Anaesthesiology, Jessa Hospital, Hasselt, Belgium
| | - Dirk Ramaekers
- Jessa Hospital, Hasselt, Belgium
- Leuven Institute for Healthcare Policy (LIHP), University of Leuven, Leuven, Belgium
| | - Janneke Cox
- UHasselt, Faculty of Medicine and Life Sciences, LCRC, Diepenbeek, Belgium
- Department of Infectious Diseases and Immunity, Jessa Hospital, Hasselt, Belgium
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Nava-Muñoz Á, Gómez-Peña S, Fuentes-Ferrer ME, Cabeza B, Victoria A, Bustos A. COVID-19 pneumonia: Relationship between initial chest X-rays and laboratory findings. RADIOLOGIA 2021; 63:484-494. [PMID: 34801181 PMCID: PMC8549399 DOI: 10.1016/j.rxeng.2021.06.003] [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: 02/01/2021] [Accepted: 06/07/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To analyze the initial findings in chest X-rays of patients with RT-PCR positive for SARS-CoV-2, and to determine whether there is a relationship between the severity of these findings and the clinical and laboratory findings. MATERIALS AND METHODS This retrospective study analyzed the relationship between initial chest X-rays and initial laboratory tests in symptomatic adults with nasopharyngeal RT-PCR results positive for SARS-CoV-2 seen at our center between February 29 and March 23, 2020. Among other radiologic findings, we analyzed ground-glass opacities, consolidations, linear opacities, and pleural effusion. We also used a scale of radiologic severity to assess the distribution and extent of these findings. Among initial laboratory findings, we analyzed leukocytes, lymphocytes, platelets, neutrophil-to-lymphocyte ratio, and C-reactive protein. RESULTS Of 761 symptomatic patients, 639 (84%) required hospitalization and 122 were discharged to their homes. The need for admission increased with increasing scores on the scale of radiologic severity. The extent of initial lung involvement was significantly associated with the laboratory parameters analyzed (P<.05 for platelets, P<.01 for lymphocytes, and P<.001 for the remaining parameters), as well as with the time from the onset of symptoms (P<.001). CONCLUSION It can be useful to use a scale of radiologic severity to classify chest X-ray findings in diagnosing patients with COVID-19, because the greater the radiologic severity, the greater the need for hospitalization and the greater the alteration in laboratory parameters.
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Affiliation(s)
- Á Nava-Muñoz
- Servicio de Radiología, Hospital Clínico Universitario San Carlos, Madrid, Spain.
| | - S Gómez-Peña
- Servicio de Radiología, Hospital Clínico Universitario San Carlos, Madrid, Spain
| | - M E Fuentes-Ferrer
- Servicio de Medicina Preventiva, Hospital Clínico Universitario San Carlos, Madrid, Spain
| | - B Cabeza
- Servicio de Radiología, Hospital Clínico Universitario San Carlos, Madrid, Spain
| | - A Victoria
- Servicio de Radiología, Hospital Clínico Universitario San Carlos, Madrid, Spain
| | - A Bustos
- Jefe de Sección de Radiología de Tórax Hospital Clínico Universitario San Carlos, Madrid, Spain
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Alhasan M, Hasaneen M. The Role and Challenges of Clinical Imaging During COVID-19 Outbreak. JOURNAL OF DIAGNOSTIC MEDICAL SONOGRAPHY 2021. [DOI: 10.1177/87564793211056903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Objective: The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them. Materials and Methods: A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles. Results: A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students. Conclusion: The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.
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Affiliation(s)
- Mustafa Alhasan
- Department of Radiography and Medical Imaging, Fatima College of Health Sciences, Abu Dhabi, United Arab Emirates
- Radiologic Technology Program, Applied Medical Sciences College, Jordan University of Science and Technology, Irbid, Jordan
| | - Mohamed Hasaneen
- Department of Radiography and Medical Imaging, Fatima College of Health Sciences, Abu Dhabi, United Arab Emirates
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Palumbo P, Palumbo MM, Bruno F, Picchi G, Iacopino A, Acanfora C, Sgalambro F, Arrigoni F, Ciccullo A, Cosimini B, Splendiani A, Barile A, Masedu F, Grimaldi A, Di Cesare E, Masciocchi C. Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease. Diagnostics (Basel) 2021; 11:2125. [PMID: 34829472 PMCID: PMC8624922 DOI: 10.3390/diagnostics11112125] [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: 10/14/2021] [Revised: 11/08/2021] [Accepted: 11/12/2021] [Indexed: 12/22/2022] Open
Abstract
(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients' prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; p-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; p-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.
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Affiliation(s)
- Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy;
| | - Maria Michela Palumbo
- Department of Anesthesiology and Intensive Care Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University of The Sacred Heart, 00168 Rome, Italy;
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy;
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Giovanna Picchi
- Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy; (G.P.); (A.C.); (A.G.)
| | - Antonio Iacopino
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Chiara Acanfora
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Ferruccio Sgalambro
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Francesco Arrigoni
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy;
| | - Arturo Ciccullo
- Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy; (G.P.); (A.C.); (A.G.)
| | - Benedetta Cosimini
- Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100 L’Aquila, Italy; (B.C.); (E.D.C.)
| | - Alessandra Splendiani
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Francesco Masedu
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Alessandro Grimaldi
- Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy; (G.P.); (A.C.); (A.G.)
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100 L’Aquila, Italy; (B.C.); (E.D.C.)
| | - Carlo Masciocchi
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
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Watkinson N, Givargis T, Joe V, Nicolau A, Veidenbaum A. Detecting COVID-19 Related Pneumonia On CT Scans Using Hyperdimensional Computing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3970-3973. [PMID: 34892100 DOI: 10.1109/embc46164.2021.9630898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Pneumonia is a common complication associated with COVID-19 infections. Unlike common versions of pneumonia that spread quickly through large lung regions, COVID-19 related pneumonia starts in small localized pockets before spreading over the course of several days. This makes the infection more resilient and with a high probability of developing acute respiratory distress syndrome. Because of the peculiar spread pattern, the use of pulmonary computerized tomography (CT) scans was key in identifying COVID-19 infections. Identifying uncommon pulmonary diseases could be a strong line of defense in early detection of new respiratory infection-causing viruses. In this paper we describe a classification algorithm based on hyperdimensional computing for the detection of COVID-19 pneumonia in CT scans. We test our algorithm using three different datasets. The highest reported accuracy is 95.2% with an F1 score of 0.90, and all three models had a precision of 1 (0 false positives).
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