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Cama-Olivares A, Braun C, Takeuchi T, O'Hagan EC, Kaiser KA, Ghazi L, Chen J, Forni LG, Kane-Gill SL, Ostermann M, Shickel B, Ninan J, Neyra JA. Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification. J Am Soc Nephrol 2025:00001751-990000000-00603. [PMID: 40152939 DOI: 10.1681/asn.0000000702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 03/25/2025] [Indexed: 03/30/2025] Open
Abstract
Key Points
Pooled discrimination metrics were acceptable (area under the receiver operating characteristic curve >0.70) for all AKI risk classification categories in both internal and external validation.Better performance was observed in most recently published studies and those with a low or unclear risk of bias.Significant heterogeneity in patient populations, definitions, clinical predictors, and methods limit implementation in real-world clinical scenarios.
Background
Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established.
Methods
PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model.
Results
Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I2>90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool.
Conclusions
Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.
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Affiliation(s)
- Augusto Cama-Olivares
- Division of Nephrology, Department of Internal Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Chloe Braun
- Division of Critical Care, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama
| | - Tomonori Takeuchi
- Division of Nephrology, Department of Internal Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Health Policy and Informatics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Emma C O'Hagan
- UAB Libraries University of Alabama at Birmingham, Birmingham, Alabama
| | - Kathryn A Kaiser
- Department of Health Behavior, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Lama Ghazi
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jin Chen
- Division of Nephrology, Department of Internal Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Lui G Forni
- Department of Clinical and Experimental Medicine, Faculty of Health Sciences, University of Surrey and Intensive Care Unit, Royal Surrey County Hospital NHS Foundation Trust, Guildford, United Kingdom
| | - Sandra L Kane-Gill
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Benjamin Shickel
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida
| | - Jacob Ninan
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota
| | - Javier A Neyra
- Division of Nephrology, Department of Internal Medicine, University of Alabama at Birmingham, Birmingham, Alabama
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Jia X, Ma J, Qi Z, Zhang D, Gao J. Development and validation of a prediction model for acute kidney injury following cardiac valve surgery. Front Med (Lausanne) 2025; 12:1528147. [PMID: 39958823 PMCID: PMC11825392 DOI: 10.3389/fmed.2025.1528147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Accepted: 01/20/2025] [Indexed: 02/18/2025] Open
Abstract
Background Acute kidney injury (AKI) often accompanies cardiac valve surgery, and worsens patient outcome. The aim of our study is to identify preoperative and intraoperative independent risk factors for AKI in patients undergoing cardiac valve surgery. Using these factors, we developed a risk prediction model for AKI after cardiac valve surgery and conducted external validation. Methods Our retrospective study recruited 497 adult patients undergoing cardiac valve surgery as a derivation cohort between February and August 2023. Patient demographics, including medical history and perioperative clinical information, were acquired, and patients were classified into one of two cohorts, AKI and non-AKI, according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Using binary logistic stepwise regression analysis, we identified independent AKI risk factors after cardiac valve surgery. Lastly, we constructed a nomogram and conducted external validation in a validation cohort comprising 200 patients. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). Results In the derivation cohort, 172 developed AKI (34.6%). Relative to non-AKI patients, the AKI patients exhibited elevated postoperative complication incidences and worse outcome. Based on multivariate analysis, advanced age (OR: 1.855; p = 0.011), preoperative hypertension (OR: 1.91; p = 0.017), coronary heart disease (OR: 6.773; p < 0.001), preoperative albumin (OR: 0.924; p = 0.015), D-Dimer (OR: 1.001; p = 0.038), plasma creatinine (OR: 1.025; p = 0.001), cardiopulmonary bypass (CPB) duration (OR: 1.011; p = 0.001), repeat CPB (OR: 6.195; p = 0.010), intraoperative red blood cell transfusion (OR: 2.560; p < 0.001), urine volume (OR: 0.406 p < 0.001) and vasoactive-inotropic score (OR: 1.135; p = 0.009) were independent risk factors for AKI. The AUC of the nomogram in the derivation and validation cohorts were 0.814 (95%CI: 0.775-0.854) and 0.798 (95%CI: 0.726-0.871), respectively. Furthermore, the calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value. Conclusion Several perioperative factors modulate AKI development following cardiac valve surgery, resulting in poor patient prognosis. The proposed AKI predictive model is both sensitive and precise, and can assist in high-risk patient screening in the clinics.
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Affiliation(s)
| | - Jun Ma
- Department of Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Dai A, Zhou Z, Jiang F, Guo Y, Asante DO, Feng Y, Huang K, Chen C, Shi H, Si Y, Zou J. Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model. Ann Med 2023; 55:2266458. [PMID: 37813109 PMCID: PMC10563625 DOI: 10.1080/07853890.2023.2266458] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/24/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk. METHODS Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models. RESULTS Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683-0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model. CONCLUSION With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.
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Affiliation(s)
- Anran Dai
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhou Zhou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fan Jiang
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yaoyi Guo
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Dorothy O. Asante
- Department of Preventive Medicine and Public Health Laboratory Science, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Yue Feng
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Kaizong Huang
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chen Chen
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Hongwei Shi
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yanna Si
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Shao J, Liu F, Ji S, Song C, Ma Y, Shen M, Sun Y, Zhu S, Guo Y, Liu B, Wu Y, Qin H, Lai S, Fan Y. Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery. Rev Cardiovasc Med 2023; 24:229. [PMID: 39076716 PMCID: PMC11266781 DOI: 10.31083/j.rcm2408229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/06/2023] [Accepted: 02/17/2023] [Indexed: 07/31/2024] Open
Abstract
Background Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in short- and long-term mortality among patients. Here, we adopted machine learning algorithms to build prediction models with the overarching goal of identifying patients who are at a high risk of such unfavorable kidney outcomes. Methods A total of 1686 patients (development cohort) and 422 patients (validation cohort), with 126 pre- and intra-operative variables, were recruited from the First Medical Centre and the Sixth Medical Centre of Chinese PLA General Hospital in Beijing, China, respectively. Analyses were performed using six machine learning techniques, namely K-nearest neighbor, logistic regression, decision tree, random forest (RF), support vector machine, and neural network, and the APPROACH score, a previously established risk score for CSA-AKI. For model tuning, optimal hyperparameter was achieved by using GridSearch with 5-fold cross-validation from the scikit-learn library. Model performance was externally assessed via the receiver operating characteristic (ROC) and decision curve analysis (DCA). Explainable machine learning was performed using the Python SHapley Additive exPlanation (SHAP) package and Seaborn library, which allow the calculation of marginal contributory SHAP value. Results 637 patients (30.2%) developed CSA-AKI within seven days after surgery. In the external validation, the RF classifier exhibited the best performance among the six machine learning techniques, as shown by the ROC curve and DCA, while the traditional APPROACH risk score showed a relatively poor performance. Further analysis found no specific causative factor contributing to the development of CSA-AKI; rather, the development of CSA-AKI appeared to be a complex process resulting from a complex interplay of multiple risk factors. The SHAP summary plot illustrated the positive or negative contribution of RF-top 20 variables and extrapolated risk of developing CSA-AKI at individual levels. The Seaborn library showed the effect of each single feature on the model output of the RF prediction. Conclusions Efficient machine learning approaches were successfully established to predict patients with a high probability of developing acute kidney injury after cardiac surgery. These findings are expected to help clinicians to optimize treatment strategies and minimize postoperative complications. Clinical Trial Registration The study protocol was registered at the ClinicalTrials Registration System (https://www.clinicaltrials.gov/, #NCT04966598) on July 26, 2021.
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Affiliation(s)
- Jiakang Shao
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Feng Liu
- Department of Vascular and Endovascular Surgery, The First Medical Center
of Chinese PLA General Hospital, 100853 Beijing, China
| | - Shuaifei Ji
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Chao Song
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Yan Ma
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Ming Shen
- Department of Cardiovascular Medicine, The First Hospital of Hebei Medical
University, 050000 Shijiazhuang, Hebei, China
| | - Yuntian Sun
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Siming Zhu
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Yilong Guo
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Bing Liu
- Department of Cardiovascular Surgery, the Sixth Medical Centre of Chinese
PLA General Hospital, 100048 Beijing, China
| | - Yuanbin Wu
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
| | - Handai Qin
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Shengwei Lai
- Medical School of Chinese PLA, 100853 Beijing, China
| | - Yunlong Fan
- Medical School of Chinese PLA, 100853 Beijing, China
- Department of Cardiovascular Surgery, the First Medical Centre of Chinese
PLA General Hospital, 100853 Beijing, China
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Feng Y, Wang AY, Jun M, Pu L, Weisbord SD, Bellomo R, Hong D, Gallagher M. Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6:e2313359. [PMID: 37184837 PMCID: PMC12011341 DOI: 10.1001/jamanetworkopen.2023.13359] [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: 01/24/2023] [Accepted: 03/30/2023] [Indexed: 05/16/2023] Open
Abstract
Importance Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes. Objective To systematically review published AKI prediction models across all clinical subsettings. Data Sources MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models. Study Selection All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs. Data Extraction and Synthesis Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias. Main Outcomes and Measures C statistic was used to measure the discrimination of prediction models. Results Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool. Conclusions and Relevance In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.
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Affiliation(s)
- Yunlin Feng
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Amanda Y. Wang
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- Concord Clinical School, University of Sydney, Sydney, Australia
- The Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
| | - Min Jun
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Lei Pu
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Steven D. Weisbord
- Renal Section, Medicine Service, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Renal-Electrolyte Division, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rinaldo Bellomo
- Department of Critical Care, University of Melbourne, Melbourne, Australia
| | - Daqing Hong
- Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, Australia
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Yan Y, Gong H, Hu J, Wu D, Zheng Z, Wang L, Lei C. Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery. Front Cardiovasc Med 2023; 10:1094997. [PMID: 36960471 PMCID: PMC10028074 DOI: 10.3389/fcvm.2023.1094997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
Background Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population. Methods Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score. Results A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score. Conclusion Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery. Clinical trial registration Trial registration: Clinicaltrials.gov, NCT04237636.
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Affiliation(s)
- Yun Yan
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Hairong Gong
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Jie Hu
- Department of Critical Care Medicine, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Di Wu
- Department of School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Ziyu Zheng
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Lini Wang
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Chong Lei
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- Correspondence: Chong Lei
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Xinsai L, Zhengye W, Xuan H, Xueqian C, Kai P, Sisi C, Xuyan J, Suhua L. Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning. Front Cardiovasc Med 2022; 9:984772. [PMID: 36211563 PMCID: PMC9535339 DOI: 10.3389/fcvm.2022.984772] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 08/30/2022] [Indexed: 11/16/2022] Open
Abstract
Objective A clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML). Methods Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit. Results The final incidence of AKI was 69.4% (120/173) in 173 patients with TAAAD and 28.6% (81/283) in 283 patients with TBAAD. For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630–0.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623–0.847). Screening of the characteristic variables revealed that the common predictors among the two final prediction models for postoperative AKI due to AAD were baseline SCR, Blood urea nitrogen (BUN) and Uric acid (UA) at admission, Mechanical ventilation time (MVT). The specific predictors in the TAAAD-AKI model are: White blood cell (WBC), Platelet (PLT) and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were N-terminal pro B-type natriuretic peptide (BNP), Serum kalium, Activated partial thromboplastin time (APTT) and Systolic blood pressure (SBP) at admission, Combined renal arteriography in surgery. Finally, we used in terms of Discrimination, the ROC value of the RF model for TAAAD was 0.81 and the ROC value of the LightGBM model for TBAAD was 0.74, both with good accuracy. In terms of calibration, the calibration curve of TAAAD-AKI's RF fits the ideal curve the best and has the lowest and smallest Brier score (0.16). Similarly, the calibration curve of TBAAD-AKI's LightGBM model fits the ideal curve the best and has the smallest Brier score (0.15). In terms of Clinical benefit, the best ML models for both types of AAD have good Net benefit as shown by Decision Curve Analysis (DCA). Conclusion We successfully constructed and validated clinical prediction models for the occurrence of AKI after surgery in TAAAD and TBAAD patients using different ML algorithms. The main predictors of the two types of AAD-AKI are somewhat different, and the strategies for early prevention and control of AKI are also different and need more external data for validation.
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Affiliation(s)
- Li Xinsai
- Kidney Disease Center of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Xinjiang Branch of National Clinical Research Center for Kidney Disease, Institute of Nephrology of Xinjiang, Urumqi, China
- Xinjiang Blood Purification Medical Quality Control Center, Urumqi, China
| | - Wang Zhengye
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Huang Xuan
- Kidney Disease Center of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Xinjiang Branch of National Clinical Research Center for Kidney Disease, Institute of Nephrology of Xinjiang, Urumqi, China
- Xinjiang Blood Purification Medical Quality Control Center, Urumqi, China
| | - Chu Xueqian
- Kidney Disease Center of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Xinjiang Branch of National Clinical Research Center for Kidney Disease, Institute of Nephrology of Xinjiang, Urumqi, China
- Xinjiang Blood Purification Medical Quality Control Center, Urumqi, China
| | - Peng Kai
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Chen Sisi
- Kidney Disease Center of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Xinjiang Branch of National Clinical Research Center for Kidney Disease, Institute of Nephrology of Xinjiang, Urumqi, China
- Xinjiang Blood Purification Medical Quality Control Center, Urumqi, China
| | - Jiang Xuyan
- Kidney Disease Center of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Xinjiang Branch of National Clinical Research Center for Kidney Disease, Institute of Nephrology of Xinjiang, Urumqi, China
- Xinjiang Blood Purification Medical Quality Control Center, Urumqi, China
| | - Li Suhua
- Kidney Disease Center of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Xinjiang Branch of National Clinical Research Center for Kidney Disease, Institute of Nephrology of Xinjiang, Urumqi, China
- Xinjiang Blood Purification Medical Quality Control Center, Urumqi, China
- *Correspondence: Li Suhua
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Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9:11255-11264. [PMID: 35071556 PMCID: PMC8717516 DOI: 10.12998/wjcc.v9.i36.11255] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/15/2021] [Accepted: 11/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) after surgery appears to increase the risk of death in patients with liver cancer. In recent years, machine learning algorithms have been shown to offer higher discriminative efficiency than classical statistical analysis.
AIM To develop prediction models for AKI after liver cancer resection using machine learning techniques.
METHODS We screened a total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital, Shanghai City, China, from January 1, 2015 to August 31, 2020. The AKI definition used was consistent with the Kidney Disease: Improving Global Outcomes. We included in our analysis preoperative data such as demographic characteristics, laboratory findings, comorbidities, and medication, as well as perioperative data such as duration of surgery. Computerized algorithms used for model development included logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and decision tree (DT). Feature importance was also ranked according to its contribution to model development.
RESULTS AKI events occurred in 296 patients (12.1%) within 7 d after surgery. Among the original models based on machine learning techniques, the RF algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for XGBoost, 0.90 for DT, 0.90 for SVM, and 0.85 for LR. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variable that contributed the most in the RF algorithm was age, followed by cholesterol, and surgery time.
CONCLUSION Machine learning algorithms are highly effective in discriminating patients at high risk of developing AKI. The successful application of machine learning models may help guide clinical decisions and help improve the long-term prognosis of patients.
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Affiliation(s)
- Jun-Feng Dong
- Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, Shanghai 200003, China
| | - Qiang Xue
- Department of Neurosurgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University, Shanghai 200082, China
| | - Ting Chen
- Department of Intensive Rehabilitation, Zhabei Central Hospital, Shanghai 200070, China
| | - Yuan-Yu Zhao
- Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, Shanghai 200003, China
| | - Hong Fu
- Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, Shanghai 200003, China
| | - Wen-Yuan Guo
- Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, Shanghai 200003, China
| | - Jun-Song Ji
- Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, Shanghai 200003, China
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Wiberg S, Kjaergaard J, Møgelvang R, Møller CH, Kandler K, Ravn H, Hassager C, Køber L, Nilsson JC. Efficacy of a glucagon-like peptide-1 agonist and restrictive versus liberal oxygen supply in patients undergoing coronary artery bypass grafting or aortic valve replacement: study protocol for a 2-by-2 factorial designed, randomised clinical trial. BMJ Open 2021; 11:e052340. [PMID: 34740932 PMCID: PMC8573662 DOI: 10.1136/bmjopen-2021-052340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Coronary artery bypass grafting (CABG) and/or aortic valve replacement (AVR) are associated with risk of death, as well as brain, heart and kidney injury. Glucagon-like peptide-1 (GLP-1) analogues are approved for treatment of type 2 diabetes, and GLP-1 analogues have been suggested to have potential organ-protective and anti-inflammatory effects. During cardiopulmonary bypass (CPB), consensus on the optimal fraction of oxygen is lacking. The objective of this study is to determine the efficacy of the GLP-1-analogue exenatide versus placebo and restrictive oxygenation (50% fractional inspired oxygen, FiO2) versus liberal oxygenation (100% FiO2) in patients undergoing open heart surgery. METHODS AND ANALYSIS A randomised, placebo-controlled, double blind (for the exenatide intervention)/single blind (for the oxygenation strategy), 2×2 factorial designed single-centre trial on adult patients undergoing elective or subacute CABG and/or surgical AVR. Patients will be randomised in a 1:1 and 1:1 ratio to a 6-hour and 15 min infusion of 17.4 µg of exenatide or placebo during CPB and to a FiO2 of 50% or 100% during and after weaning from CPB. Patients will be followed until 12 months after inclusion of the last participant. The primary composite endpoint consists of time to first event of death, renal failure requiring renal replacement therapy, hospitalisation for stroke or heart failure. In addition, the trial will include predefined sub-studies applying more advanced measures of cardiac- and pulmonary dysfunction, renal dysfunction and cerebral dysfunction. The trial is event driven and aims at 323 primary endpoints with a projected inclusion of 1400 patients. ETHICS AND DISSEMINATION Eligible patients will provide informed, written consent prior to randomisation. The trial is approved by the local ethics committee and is conducted in accordance with Danish legislation and the Declaration of Helsinki. The results will be presented in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT02673931.
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Affiliation(s)
| | | | | | | | - Kristian Kandler
- Department of Cardiothoracic Surgery, Rigshospitalet, Copenhagen, Denmark
| | - Hanne Ravn
- Department of Cardiothoracic Anesthesiology, Rigshospitalet, Copenhagen, Denmark
| | | | - Lars Køber
- Department of Cardiology, Rigshospitalet, Copenhagen, Denmark
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Chang CY, Chien YJ, Kao MC, Lin HY, Chen YL, Wu MY. Pre-operative proteinuria, postoperative acute kidney injury and mortality: A systematic review and meta-analysis. Eur J Anaesthesiol 2021; 38:702-714. [PMID: 34101638 DOI: 10.1097/eja.0000000000001542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To investigate the association of pre-operative proteinuria with postoperative acute kidney injury (AKI) development as well as the requirement for a renal replacement therapy (RRT) and mortality at short-term and long-term follow-up. BACKGROUND Postoperative AKI is associated with surgical morbidity and mortality. Pre-operative proteinuria is potentially a risk factor for postoperative AKI and mortality. However, the results in literature are conflicting. METHODS We searched PubMed, Embase, Scopus, Web of Science and Cochrane Library from the inception through to 3 June 2020. Observational cohort studies investigating the association of pre-operative proteinuria with postoperative AKI development, requirement for RRT, and all-cause mortality at short-term and long-term follow-up were considered eligible. Using inverse variance method with a random-effects model, the pooled effect estimates and 95% confidence interval (CI) were calculated. RESULTS Twenty-eight studies were included. Pre-operative proteinuria was associated with postoperative AKI development [odds ratio (OR) 1.74, 95% CI, 1.45 to 2.09], in-hospital RRT (OR 1.70, 95% CI, 1.25 to 2.32), requirement for RRT at long-term follow-up [hazard ratio (HR) 3.72, 95% CI, 2.03 to 6.82], and long-term all-cause mortality (hazard ratio 1.50, 95% CI, 1.30 to 1.73). In the subgroup analysis, pre-operative proteinuria was associated with increased odds of postoperative AKI in both cardiovascular (OR 1.77, 95% CI, 1.47 to 2.14) and noncardiovascular surgery (OR 1.63, 95% CI, 1.01 to 2.63). Moreover, there is a stepwise increase in OR of postoperative AKI development when the quantity of proteinuria increases from trace to 3+. CONCLUSION Pre-operative proteinuria is significantly associated with postoperative AKI and long-term mortality. Pre-operative anaesthetic assessment should take into account the presence of proteinuria to identify high-risk patients. PROSPERO REGISTRATION CRD42020190065.
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Affiliation(s)
- Chun-Yu Chang
- From the Department of Anesthesiology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City (C-YC, M-CK, H-YL), Department of Anesthesiology, School of Medicine, Tzu Chi University, Hualien (C-YC, M-CK, H-YL), Department of Physical Medicine and Rehabilitation, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City (Y-JC), Department of Physical Medicine and Rehabilitation, School of Medicine, Tzu Chi University, Hualien (Y-JC), Department of Emergency Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City (Y-LC, M-YW) and Department of Emergency Medicine, School of Medicine, Tzu Chi University, Hualien, Taiwan (Y-LC, M-YW)
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Wang D, Zhang W, Luo J, Fang H, Jing S, Mei Z. Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal. BMJ Open 2021; 11:e046274. [PMID: 34011595 PMCID: PMC8137185 DOI: 10.1136/bmjopen-2020-046274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 04/07/2021] [Accepted: 04/26/2021] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Acute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review. METHODS AND ANALYSIS A systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool. ETHICS AND DISSEMINATION Ethical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences. OSF REGISTRATION NUMBER 10.17605/OSF.IO/X25AT.
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Affiliation(s)
- Danqiong Wang
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Weiwen Zhang
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Jian Luo
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Honglong Fang
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Shanshan Jing
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Zubing Mei
- Department of Anorectal Surgery, Anorectal Disease Institute of Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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12
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Mokhtar AT, Tennankore K, Doucette S, Herman CR. Predicting acute kidney injury following nonemergent cardiac surgery: A preoperative scorecard. J Card Surg 2021; 36:2204-2212. [PMID: 33738864 DOI: 10.1111/jocs.15503] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/07/2021] [Accepted: 03/08/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To determine the predictors of postoperative acute kidney injury (AKI) following nonemergent cardiac surgery among patients with variable preoperative estimated glomerular filtration rate (eGFR) levels. METHODS A retrospective study of patients who underwent elective or in-hospital cardiac surgical procedures was performed between January 2006 and November 2015. The procedures included isolated coronary artery bypass grafting (CABG), isolated aortic valve replacement (AVR), or combined CABG and AVR. The primary outcome AKI (any stage) following nonemergent cardiac surgery utilizing the 2012 Kidney Disease-Improving Global Outcomes (KDIGO) criteria. Patients were categorized based on the following renal outcomes: mild AKI, severe AKI (KDIGO stage 2 or 3), and postoperative dialysis. Patients with G5 preoperative kidney function (including dialysis patients) were excluded. RESULTS A total of 6675 patients were included in our study. The mean age was 66.8 years (SD ± 10.4), with 76.3% being males. A total of 4487 patients had normal or mildly decreased eGFR (G1 or G2) preoperatively (67.2%), while 1960 patients were in the G3 category (29.4%). Only 228 patients (3.4%) had G4 renal function. A total of 1453 (21.7%) patients experienced postoperative AKI. The need for postoperative dialysis occurred in 3.2% of the AKI subgroup. In-hospital mortality was higher among the AKI subgroup (7.2% vs. 0.5%; p < .0001). In an adjusted model, a lower preoperative eGFR category was the strongest predictor of AKI. A practical scorecard for the preoperative estimation of severe AKI for nonemergent cardiac procedures incorporating these parameters was developed. CONCLUSIONS Preoperative eGFR is the strongest predictor of postoperative AKI in individuals undergoing nonemergent cardiac surgery. A practical scorecard incorporating preoperative predictors of AKI may allow informed decision-making and predict AKI following nonemergent cardiac surgery.
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Affiliation(s)
- Ahmed T Mokhtar
- Department of Medicine, Division of Cardiology, Dalhousie University, Halifax, Nova Scotia, Canada.,Department of Medicine, Division of Cardiology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Karthik Tennankore
- Department of Medicine, Division of Nephrology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Steve Doucette
- Research Methods Unit, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | - Christine R Herman
- Department of Surgery, Division of Cardiac and Vascular Surgery, Dalhousie University, Halifax, Nova Scotia, Canada
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Woo SH, Zavodnick J, Ackermann L, Maarouf OH, Zhang J, Cowan SW. Development and Validation of a Web-Based Prediction Model for AKI after Surgery. KIDNEY360 2021; 2:215-223. [PMID: 35373024 PMCID: PMC8740985 DOI: 10.34067/kid.0004732020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 12/28/2020] [Indexed: 06/14/2023]
Abstract
BACKGROUND AKI after surgery is associated with high mortality and morbidity. The purpose of this study is to develop and validate a risk prediction tool for the occurrence of postoperative AKI requiring RRT (AKI-dialysis). METHODS This retrospective cohort study had 2,299,502 surgical patients over 2015-2017 from the American College of Surgeons National Surgical Quality Improvement Program Database (ACS NSQIP). Eleven predictors were selected for the predictive model: age, history of congestive heart failure, diabetes, ascites, emergency surgery, hypertension requiring medication, preoperative serum creatinine, hematocrit, sodium, preoperative sepsis, and surgery type. The predictive model was trained using 2015-2016 data (n=1,487,724) and further tested using 2017 data (n=811,778). A risk model was developed using multivariable logistic regression. RESULTS AKI-dialysis occurred in 0.3% (n=6853) of patients. The unadjusted 30-day postoperative mortality rate associated with AKI-dialysis was 37.5%. The AKI risk prediction model had high area under the receiver operating characteristic curve (AUC; training cohort: 0.89, test cohort: 0.90) for postoperative AKI-dialysis. CONCLUSIONS This model provides a clinically useful bedside predictive tool for postoperative AKI requiring dialysis.
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Affiliation(s)
- Sang H Woo
- Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Jillian Zavodnick
- Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Lily Ackermann
- Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Omar H Maarouf
- Division of Nephrology, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Jingjing Zhang
- Division of Nephrology, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Scott W Cowan
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania
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14
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Tian Y, Diao X, Wang Y, Wang C, Wang W, Xu X, Gao Y, Wang S, Liu J, Ji B, Zhou C, Zhang Q, Gao S. Prediction Scores for Any-Stage and Stage-3 Acute Kidney Injury After Adult Cardiac Surgery in a Chinese Population. J Cardiothorac Vasc Anesth 2021; 35:3001-3009. [PMID: 33810934 DOI: 10.1053/j.jvca.2021.02.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/07/2021] [Accepted: 02/17/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES This study was performed to internally derive and then validate risk score systems using preoperative and intraoperative variables to predict the occurrence of any-stage (stage 1, 2, 3) and stage-3 acute kidney injury (AKI) within seven days of cardiac surgery. DESIGN Single-center, retrospective, observational study. SETTING Single, large, tertiary care center. PARTICIPANTS Adult patients undergoing open cardiac surgery between January 1, 2012, and January 1, 2019. MEASUREMENTS AND MAIN RESULTS The clinical data were divided into the following two groups: a derivation cohort (n = 43,799) and a validation cohort (n = 14,600). AKI was defined using the Kidney Disease: Improving Global Outcomes criteria. Multivariate logistic regression analysis was used to develop the prediction models. The overall prevalence of any-stage AKI and stage-3 AKI after cardiac surgery were 34.3% and 1.7%, respectively. The discriminatory ability of the any-stage AKI prediction model measured with the area under the curve (AUC) was acceptable (AUC = 0.69, 95% confidence interval 0.68-0.69), and the calibration measured with the Hosmer-Lemeshow test was good (p = 0.95). The AUC for the stage-3 AKI prediction model was 0.84 (95% confidence interval 0.83-0.85), and the Hosmer-Lemeshow test also indicated a good calibration (p = 0.73). CONCLUSIONS This research study, which used preoperative and intraoperative variables, derived and internally validated two predictive scoring systems for any-stage AKI and stage-3 AKI as defined by modified Kidney Disease: Improving Global Outcomes criteria using a very large cohort of Chinese cardiac surgical patients.
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Affiliation(s)
- Yu Tian
- Department of Anesthesiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiaolin Diao
- Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yuefu Wang
- Department of Anesthesiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
| | - Chunrong Wang
- Department of Anesthesiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Weiwei Wang
- Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xinyi Xu
- Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yuchen Gao
- Department of Anesthesiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Sudena Wang
- Department of Anesthesiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jia Liu
- Department of Anesthesiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Bingyang Ji
- Department of Cardiopulmonary Bypass, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Chun Zhou
- Department of Cardiopulmonary Bypass, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Qiaoni Zhang
- Department of Cardiopulmonary Bypass, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Sizhe Gao
- Department of Cardiopulmonary Bypass, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Loschi D, Melloni A, Kahlberg A, Chiesa R, Melissano G. Kidney protection in thoracoabdominal aortic aneurysm surgery. THE JOURNAL OF CARDIOVASCULAR SURGERY 2020; 62:326-338. [PMID: 33307647 DOI: 10.23736/s0021-9509.20.11745-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Acute kidney injury (AKI) is a common complication of both open and endovascular repair of thoracoabdominal aortic aneurysms (TAAA). Its definition varies across difference studies, some standardized definitions (RIFLE, AKIN, KDIGO) have been proposed but still not uniformly employed in published papers. Acute kidney injury is multifactorial and is associated with increased in-hospital mortality, long-term mortality and late renal function decline. In addition, AKI is also associated with perioperative spinal cord ischemia. No specific pharmacological strategy has received a strong recommendation with high level of evidence as a protective measure. Fenoldopam, methylprednisolone or mannitol use to prevent AKI is commonly employed, but not supported by literature data. Avoiding nephrotoxic drugs and maintaining an adequate MAP, during and after the procedure plays a key role in preserving kidney function. During open TAAA surgery, renal arteries may be reimplanted using different techniques. The choice of the best option must be tailored to the patient, to reduce ischemic time and guarantee long-term patency. Current experience suggests that cold crystalloid solutions are the best substrates in preventing ischemia-reperfusion injury. Renal perfusion using Custodiol® (Dr Franz-Kohler Chemie GmbH; Bensheim, Germany) 4 °C, even if currently considered off-label, represents an encouraging organ protection tool. In endovascular TAAA repair, techniques such as fusion imaging, use of diluted contrast, and CO<inf>2</inf> subtraction angiography have the potential to reduce postoperative AKI. Visceral vessels patency is closely related to the anatomy. Therefore, accurate endograft design according to these characteristics is crucial for long-term preservation of renal function.
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Affiliation(s)
- Diletta Loschi
- Division of Vascular Surgery, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy -
| | - Andrea Melloni
- Division of Vascular Surgery, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy
| | - Andrea Kahlberg
- Division of Vascular Surgery, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy
| | - Roberto Chiesa
- Division of Vascular Surgery, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy
| | - Germano Melissano
- Division of Vascular Surgery, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy
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Deng F, Peng M, Li J, Chen Y, Zhang B, Zhao S. Nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data. Ren Fail 2020; 42:428-436. [PMID: 32401139 PMCID: PMC7269058 DOI: 10.1080/0886022x.2020.1761832] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 02/03/2023] Open
Abstract
Background: Acute kidney injury (AKI) is a significant cause of morbidity and mortality, especially in sepsis patients. Early prediction of AKI can help physicians determine the appropriate intervention, and thus, improve the outcome. This study aimed to develop a nomogram to predict the risk of AKI in sepsis patients (S-AKI) in the initial 24 h following admission.Methods: Sepsis patients with AKI who met the Sepsis 3.0 criteria and Kidney Disease: Improving Global Outcomes criteria in the Massachusetts Institute of Technology critical care database, Medical Information Mart for Intensive Care (MIMIC-III), were identified for analysis. Data were analyzed using multiple logistic regression, and the performance of the proposed nomogram was evaluated based on Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve.Results: We included 2917 patients in the analysis; 1167 of 2042 patients (57.14%) and 469 of 875 patients (53.6%) had AKI in the training and validation cohorts, respectively. The predictive factors identified by multivariate logistic regression were blood urea nitrogen level, infusion volume, lactate level, weight, blood chloride level, body temperature, and age. With the incorporation of these factors, our model had well-fitted calibration curves and achieved good C-indexes of 0.80 [95% confidence interval (CI): 0.78-0.82] and 0.79 (95% CI: 0.76-0.82) in predicting S-AKI in the training and validation cohorts, respectively.Conclusion: The proposed nomogram effectively predicted AKI risk in sepsis patients admitted to the intensive care unit in the first 24 h.
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Affiliation(s)
- Fuxing Deng
- Department of Critical Care Medicine & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China
| | - Milin Peng
- Department of Critical Care Medicine & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China
| | - Jing Li
- Department of Critical Care Medicine & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China
| | - Yana Chen
- Department of Critical Care Medicine & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China
| | - Buyao Zhang
- Department of Critical Care Medicine & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China
| | - Shuangping Zhao
- Department of Critical Care Medicine & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China
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18
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Ngu JMC, Jabagi H, Chung AM, Boodhwani M, Ruel M, Bourke M, Sun LY. Defining an Intraoperative Hypotension Threshold in Association with De Novo Renal Replacement Therapy after Cardiac Surgery. Anesthesiology 2020; 132:1447-1457. [PMID: 32205546 DOI: 10.1097/aln.0000000000003254] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Acute kidney injury (AKI) is a frequent and deadly complication after cardiac surgery. In the absence of effective therapies, a focus on risk factor identification and modification has been the mainstay of management. The authors sought to determine the impact of intraoperative hypotension on de novo postoperative renal replacement therapy in patients undergoing cardiac surgery, hypothesizing that prolonged periods of hypotension during and after cardiopulmonary bypass (CPB) were associated with an increased risk of renal replacement therapy. METHODS Included in this single-center retrospective cohort study were adult patients who underwent cardiac surgery requiring CPB between November 2009 and April 2015. Excluded were patients who were dialysis dependent, underwent thoracic aorta or off-pump procedures, or died before receiving renal replacement therapy. Degrees of hypotension were defined by mean arterial pressure (MAP) as less than 55, 55 to 64, and 65 to 74 mmHg before, during, and after CPB. The primary outcome was de novo renal replacement therapy. RESULTS Of 6,523 patient records, 336 (5.2%) required new postoperative renal replacement therapy. Each 10-min epoch of MAP less than 55 mmHg post-CPB was associated with an adjusted odds ratio of 1.13 (95% CI, 1.05 to 1.23; P = 0.002), and each 10-min epoch of MAP between 55 and 64 mmHg post-CPB was associated with an adjusted odds ratio of 1.12 (95% CI, 1.06 to 1.18; P = 0.0001) for renal replacement therapy. The authors did not observe an association between hypotension before and during CPB with renal replacement therapy. CONCLUSIONS MAP less than 65 mmHg for 10 min or more post-CPB is associated with an increased risk of de novo postoperative renal replacement therapy. The association between intraoperative hypotension and AKI was weaker in comparison to factors such as renal insufficiency, heart failure, obesity, anemia, complex or emergent surgery, and new-onset postoperative atrial fibrillation. Nonetheless, post-CPB hypotension is a potentially easier modifiable risk factor that warrants further investigation.
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Affiliation(s)
- Janet M C Ngu
- From the Division of Cardiac Surgery (J.M.C.N., H.J., M. Boodhwani, M.R.) the Division of Cardiac Anesthesiology (A.M.C., M. Bourke, L.Y.S.) Cardiocore Big Data Research Unit (L.Y.S.), University of Ottawa Heart Institute, Ottawa, Canada the School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada (L.Y.S.) the Cardiovascular Research Program, Institute for Clinical Evaluative Sciences, Toronto, Canada (L.Y.S.)
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Coulson T, Bailey M, Pilcher D, Reid CM, Seevanayagam S, Williams-Spence J, Bellomo R. Predicting Acute Kidney Injury After Cardiac Surgery Using a Simpler Model. J Cardiothorac Vasc Anesth 2020; 35:866-873. [PMID: 32713734 DOI: 10.1053/j.jvca.2020.06.072] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 06/21/2020] [Accepted: 06/22/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVE To develop a simple model for the prediction of acute kidney injury (AKI) and renal replacement therapy (RRT) that could be used in clinical or research risk stratification. DESIGN Retrospective analysis. SETTING Multi-institutional. PARTICIPANTS All cardiac surgery patients from September 2016 to December 2018. INTERVENTIONS Observational. MEASUREMENTS AND MAIN RESULTS The study cohort was divided into a development set (75%) and validation set (25%). The following 2 data epochs were used: preoperative data and immediate postoperative data (within 4 h of intensive care unit admission). Univariate statistics were used to identify variables associated with AKI or RRT. Stepwise logistic regression was used to develop a parsimonious model. Model discrimination and calibration were evaluated in the test set. Models were compared with previously published models and with a more comprehensive model developed using the least absolute shrinkage and selection operator. The study included 22,731 patients at 33 hospitals. The incidences of AKI (any stage) and RRT for the present analysis were 5,829 patients (25.6%) and 488 patients (2.1%), respectively. Models were developed for AKI, with an area under the receiver operating curve (AU-ROC) of 0.67 and 0.69 preoperatively and postoperatively, respectively. Models for RRT had an AU-ROC of 0.77 and 0.80 preoperatively and postoperatively, respectively. These models contained between 3 and 5 variables. Comparatively, comprehensive least absolute shrinkage and selection operator models contained between 21 and 26 variables, with an AU-ROC of 0.71 and 0.72 for AKI and 0.84 and 0.87 for RRT respectively. CONCLUSION In the present study, simple, clinically applicable models for predicting AKI and RRT preoperatively and immediate postoperatively were developed. Even though AKI prediction remained poor, RRT prediction was good with a parsimonious model.
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Affiliation(s)
- Tim Coulson
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; Centre for Integrated Critical Care, University of Melbourne, Melbourne, Australia; Department of Anesthesia, Austin Health, Melbourne, Melbourne, Australia.
| | - Michael Bailey
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; Centre for Integrated Critical Care, University of Melbourne, Melbourne, Australia
| | - Dave Pilcher
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; Department of Intensive Care, Alfred Health, Melbourne, Australia
| | - Christopher M Reid
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; School of Public Health, Curtin University, Perth, Australia
| | - Siven Seevanayagam
- Department of Anesthesia, Austin Health, Melbourne, Melbourne, Australia
| | - Jenni Williams-Spence
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - Rinaldo Bellomo
- Centre for Integrated Critical Care, University of Melbourne, Melbourne, Australia; Department of Anesthesia, Austin Health, Melbourne, Melbourne, Australia
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Acute kidney injury prediction models: current concepts and future strategies. Curr Opin Nephrol Hypertens 2020; 28:552-559. [PMID: 31356235 DOI: 10.1097/mnh.0000000000000536] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) is a critical condition associated with poor patient outcomes. We aimed to review the current concepts and future strategies regarding AKI risk prediction models. RECENT FINDINGS Recent studies have shown that AKI occurs frequently in patients with common risk factors and certain medical conditions. Prediction models for AKI risk have been reported in medical fields such as critical care medicine, surgery, nephrotoxic agent exposure, and others. However, practical, generalizable, externally validated, and robust AKI prediction models remain relatively rare. Further efforts to develop AKI prediction models based on comprehensive clinical data, artificial intelligence, improved delivery of care, and novel biomarkers may help improve patient outcomes through precise AKI risk prediction. SUMMARY This brief review provides insights for current concepts for AKI prediction model development. In addition, by overviewing the recent AKI prediction models in various medical fields, future strategies to construct advanced AKI prediction models are suggested.
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Accini Mendoza JL, Atehortua L LH, Nieto Estrada VH, Rebolledo M CE, Duran Pérez JC, Senior JM, Hernández Leiva E, Valencia AA, Escobar Serna JF, Dueñas Castell C, Cotes Ramos R, Beltrán N, Thomen Palacio R, López García DA, Pizarro Gómez C, Florián Pérez MC, Franco S, García H, Rincón FM, Danetra Novoa CA, Delgado JF. Consenso colombiano de cuidados perioperatorios en cirugía cardiaca del paciente adulto. ACTA COLOMBIANA DE CUIDADO INTENSIVO 2020; 20:118-157. [DOI: 10.1016/j.acci.2020.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]
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Recommendations for Preoperative Assessment and Shared Decision-Making in Cardiac Surgery. CURRENT ANESTHESIOLOGY REPORTS 2020; 10:185-195. [PMID: 32431570 DOI: 10.1007/s40140-020-00377-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Purpose of review Recommendations about shared decision-making and guidelines on preoperative evaluation of patients undergoing non-cardiac surgery are abundant, but respective recommendations for cardiac surgery are sparse. We provide an overview of available evidence. Recent findings While there currently is no consensus statement on the preoperative anesthetic evaluation and shared decision-making for the adult patient undergoing cardiac surgery, evidence pertaining to specific organ systems is available. Summary We provide a comprehensive review of available evidence pertaining to preoperative assessment and shared decision-making for patients undergoing cardiac surgery and recommend a thorough preoperative workup in this vulnerable population.
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Costs and consequences of acute kidney injury after cardiac surgery: A cohort study. J Thorac Cardiovasc Surg 2020; 162:880-887. [PMID: 32299694 DOI: 10.1016/j.jtcvs.2020.01.101] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 01/25/2020] [Accepted: 01/29/2020] [Indexed: 01/09/2023]
Abstract
OBJECTIVES Acute kidney injury (AKI) is common after cardiac surgery. We quantified the mortality and costs of varying degrees of AKI using a population-based cohort in Alberta, Canada. METHODS A cohort of patients undergoing cardiac surgery from 2004 to 2009 was assembled from linked Alberta administrative databases. AKI was classified by Kidney Disease Improving Global Outcomes stages of severity. Our outcomes were in-hospital mortality, length of stay, and costs; among survivors, we also examined mortality and costs at 365 days. Estimates were adjusted for demographic characteristics, comorbidities, and other covariates. RESULTS Ten thousand one hundred seventy participants were included, of whom 9771 patients were discharged to community. Overall in-hospital mortality, costs, and length of stay were 4%, 7 days, and Can $34,000, respectively. Postcardiac surgery, AKI occurred in 25%. Compared with those without AKI, AKI was independently associated with increased in-hospital mortality across severity categories, with the highest risk (adjusted odds ratio, 37.1; 95% confidence interval, 26.3-52.1; P < .001) in patients who required acute dialysis. AKI severity was associated with increased hospital days and costs, with costs ranging from 1.21 for stage 1 AKI (95% confidence interval, 1.17-1.23) to 2.74 for acute dialysis (95% confidence interval, 2.49-3.00) (P < .001) times higher than in patients without AKI, after covariate adjustment. Postdischarge to 365 days, patients with AKI continued to experience increased costs up to 1.35-fold, and patients who required dialysis acutely continued to experience a 2.86-fold increased mortality. CONCLUSIONS AKI remains an important indicator of mortality and health care costs postcardiac surgery.
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Lanktree MB, Pei Y. Does elevated urinary Dkkopf-3 level predict vulnerability to kidney injury during cardiac surgery? ANNALS OF TRANSLATIONAL MEDICINE 2020; 7:S296. [PMID: 32016015 DOI: 10.21037/atm.2019.11.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Matthew B Lanktree
- Divsion of Nephrology, St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, Ontario, Canada
| | - York Pei
- Divsion of Nephrology, University Health Network and University of Toronto, Toronto, Ontario, Canada
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Risk estimation model for acute kidney injury defined by KDIGO classification after heart valve replacement surgery. Gen Thorac Cardiovasc Surg 2019; 68:922-931. [PMID: 31865601 DOI: 10.1007/s11748-019-01278-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/12/2019] [Indexed: 10/25/2022]
Abstract
OBJECTIVES Risk prediction for postoperative acute kidney injury (AKI) has a great clinical value to achieve early prevention strategies for AKI after cardiac surgery. We aimed to identify the patients at risk of postoperative AKI and to create patient risk group for AKI using a simple risk estimation model in patients undergoing heart valve replacement surgery. METHODS Between May 2008 and February 2018, 219 consecutive patients undergoing heart valve replacement surgery with or without concomitant coronary artery bypass grafting (CAGB) were included in the study. To define postoperative AKI and its severity stages, KDIGO classification which is the latest uniform classification for determining and staging of AKI was used. RESULTS The AKI incidence was 38.8%, and Class I was the dominant stage (43.5%). Postoperative AKI development was associated with a serious of postoperative adverse events, early, and long-term mortality. Furthermore, the incidence of poor outcomes increased with the degree of AKI severity. The presence of older age, chronic obstructive pulmonary disease, NYHA class III-IV, diabetes, concomitant CABG, and longer cardiopulmonary bypass duration was found to be an independent predictor for AKI, and each factor was scored according to the integer value of their odds ratio, based on risk estimation model. Patient risk groups from mild to severe for AKI development were created. The patients at severe risk group exhibited a significantly higher rate of adverse events, early, and long-term mortality as well as lower long-term survival rates. CONCLUSIONS The risk estimation model is a useful tool to identify the patients at risk and to create patient risk groups for postoperative AKI defined by KDIGO after heart valve replacement surgery.
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Hodgson LE, Selby N, Huang TM, Forni LG. The Role of Risk Prediction Models in Prevention and Management of AKI. Semin Nephrol 2019; 39:421-430. [DOI: 10.1016/j.semnephrol.2019.06.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Arbel Y, Fuster V, Baber U, Hamza TH, Siami FS, Farkouh ME. Incidence, determinants and impact of acute kidney injury in patients with diabetes mellitus and multivessel disease undergoing coronary revascularization: Results from the FREEDOM trial. Int J Cardiol 2019; 293:197-202. [PMID: 31230933 DOI: 10.1016/j.ijcard.2019.05.064] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 04/22/2019] [Accepted: 05/23/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND The incidence and prognostic significance of acute kidney injury (AKI) in patients with diabetes mellitus and multivessel coronary artery disease undergoing coronary revascularization is not well known. The current analysis included patients randomized to PCI vs. CABG as part of the FREEDOM trial. We sought to examine the impact of AKI and its predictors in diabetic patients with multivessel coronary artery disease undergoing PCI vs. CABG. METHODS We conducted a pre-specified subgroup analysis of the FREEDOM trial to examine the incidence, correlates and impact of AKI according to revascularization strategy. AKI predictors were identified using multivariable logistic regression and associations between AKI and outcomes were examined using Cox regression. The primary endpoint was the composite occurrence of all-cause death, stroke or myocardial infarction at 5 years of follow-up. RESULTS KI occurred more frequently in patients following CABG (15.6%) compared with PCI (9.1%) (p < 0.001). AKI was associated with a higher risk for major cardiovascular events (MACE) at 5 years (34.6% vs. 20.5%, p < 0.001), an effect that remained large and significant irrespective of CABG (HR = 2.18 95% CI 1.44-3.31, p ≤0.001) or PCI (HR = 2.08 95% CI 1.35-3.21, p < 0.0001). There was a non-significant interaction (p-value = 0.89) between the revascularization method and AKI, supporting that AKI is a significant risk factor in both revascularization methods. CONCLUSIONS Although risk for AKI was higher in patients undergoing CABG, the impact of AKI on MACE was substantial irrespective of revascularization strategy. Preventive strategies to identify patients at risk for AKI are warranted to mitigate the long-term effects of this complication.
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Affiliation(s)
- Yaron Arbel
- Department of Cardiology, Tel Aviv Medical Center, Affiliated with the University of Tel Aviv, Tel Aviv, Israel.
| | - Valentin Fuster
- Icahn School of Medicine at Mount Sinai, New York, USA; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Usman Baber
- Icahn School of Medicine at Mount Sinai, New York, USA; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | | | - F S Siami
- New England Research Institute (NERI), USA
| | - Michael E Farkouh
- Peter Munk Cardiac Centre, and Heart and Stroke Richard Lewar Centre, University of Toronto, Toronto, Canada
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Chiofolo C, Chbat N, Ghosh E, Eshelman L, Kashani K. Automated Continuous Acute Kidney Injury Prediction and Surveillance: A Random Forest Model. Mayo Clin Proc 2019; 94:783-792. [PMID: 31054606 DOI: 10.1016/j.mayocp.2019.02.009] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/18/2018] [Accepted: 02/12/2019] [Indexed: 01/18/2023]
Abstract
OBJECTIVE To develop and validate a prediction model of acute kidney injury (AKI) of any severity that could be used for AKI surveillance and management to improve clinical outcomes. PATIENTS AND METHODS This retrospective cohort study was conducted in medical, surgical, and mixed intensive care units (ICUs) at Mayo Clinic in Rochester, Minnesota, including adult (≥18 years of age) ICU-unique patients admitted between October 1, 2004, and April 30, 2011. Our primary objective was prediction of AKI using extant clinical data following ICU admission. We used random forest classification to provide continuous AKI risk score. RESULTS We included 4572 and 1958 patients in the training and validation mutually exclusive cohorts, respectively. Acute kidney injury occurred in 1355 patients (30%) in the training cohort and 580 (30%) in the validation cohort. We incorporated known AKI risk factors and routinely measured vital characteristics and laboratory results. The model was run throughout ICU admission every 15 minutes and achieved an area under the receiver operating characteristic curve of 0.88 on validation. It was 92% sensitive and 68% specific and detected 30% of AKI cases at least 6 hours before the criterion standard time (AKI stages 1-3). For discrimination of AKI stages 2 to 3, the model had 91% sensitivity, 71% specificity, and 53% detection of AKI cases at least 6 hours before AKI onset. CONCLUSION We developed and validated an AKI prediction model using random forest for continuous monitoring of ICU patients. This model could be used to identify high-risk patients for preventive measures or identifying patients of prospective interventional trials.
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Affiliation(s)
- Caitlyn Chiofolo
- Philips Research North America, Cambridge, MA; Quadrus Medical Technologies, Inc, New York, NY
| | - Nicolas Chbat
- Philips Research North America, Cambridge, MA; Quadrus Medical Technologies, Inc, New York, NY
| | - Erina Ghosh
- Philips Research North America, Cambridge, MA
| | | | - Kianoush Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN.
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Nicolosi GL. Statistics is good, but what is best for the single patient? Int J Cardiol 2018; 272:60-61. [PMID: 30121179 DOI: 10.1016/j.ijcard.2018.08.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 08/09/2018] [Indexed: 11/30/2022]
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Nadim MK, Forni LG, Bihorac A, Hobson C, Koyner JL, Shaw A, Arnaoutakis GJ, Ding X, Engelman DT, Gasparovic H, Gasparovic V, Herzog CA, Kashani K, Katz N, Liu KD, Mehta RL, Ostermann M, Pannu N, Pickkers P, Price S, Ricci Z, Rich JB, Sajja LR, Weaver FA, Zarbock A, Ronco C, Kellum JA. Cardiac and Vascular Surgery-Associated Acute Kidney Injury: The 20th International Consensus Conference of the ADQI (Acute Disease Quality Initiative) Group. J Am Heart Assoc 2018; 7:JAHA.118.008834. [PMID: 29858368 PMCID: PMC6015369 DOI: 10.1161/jaha.118.008834] [Citation(s) in RCA: 192] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Mitra K Nadim
- Division of Nephrology & Hypertension, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Lui G Forni
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, United Kingdom.,Royal Surrey County Hospital NHS Foundation Trust, Guildford, United Kingdom
| | - Azra Bihorac
- Division of Nephrology, Hypertension & Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
| | - Charles Hobson
- Division of Surgical Critical Care, Department of Surgery, Malcom Randall VA Medical Center, Gainesville, FL
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, IL
| | - Andrew Shaw
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN
| | - George J Arnaoutakis
- Division of Thoracic & Cardiovascular Surgery, Department of Surgery, University of Florida College of Medicine, Gainesville, FL
| | - Xiaoqiang Ding
- Department of Nephrology, Shanghai Institute for Kidney Disease and Dialysis, Shanghai Medical Center for Kidney Disease, Zhongshan Hospital Fudan University, Shanghai, China
| | - Daniel T Engelman
- Division of Cardiac Surgery, Department of Surgery, Baystate Medical Center, University of Massachusetts Medical School, Springfield, MA
| | - Hrvoje Gasparovic
- Department of Cardiac Surgery, University Hospital Rebro, Zagreb, Croatia
| | | | - Charles A Herzog
- Division of Cardiology, Department of Medicine, Hennepin County Medical Center, University of Minnesota, Minneapolis, MN
| | - Kianoush Kashani
- Division of Nephrology & Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN
| | - Nevin Katz
- Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University, Baltimore, MD
| | - Kathleen D Liu
- Divisions of Nephrology and Critical Care, Departments of Medicine and Anesthesia, University of California, San Francisco, CA
| | - Ravindra L Mehta
- Department of Medicine, UCSD Medical Center, University of California, San Diego, CA
| | - Marlies Ostermann
- King's College London, Guy's & St Thomas' Hospital, London, United Kingdom
| | - Neesh Pannu
- Division of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Peter Pickkers
- Department Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Susanna Price
- Adult Intensive Care Unit, Imperial College, Royal Brompton Hospital, London, United Kingdom
| | - Zaccaria Ricci
- Department of Pediatric Cardiac Surgery, Bambino Gesù Children's Hospital, Roma, Italy
| | - Jeffrey B Rich
- Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH
| | - Lokeswara R Sajja
- Division of Cardiothoracic Surgery, STAR Hospitals, Hyderabad, India
| | - Fred A Weaver
- Division of Vascular Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Alexander Zarbock
- Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany
| | - Claudio Ronco
- Department of Nephrology, Dialysis and Transplantation, San Bortolo Hospital International Renal Research Institute of Vicenza, Italy
| | - John A Kellum
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, PA
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Callejas R, Panadero A, Vives M, Duque P, Echarri G, Monedero P. Preoperative predictive model for acute kidney injury after elective cardiac surgery: a prospective multicenter cohort study. Minerva Anestesiol 2018; 85:34-44. [PMID: 29756690 DOI: 10.23736/s0375-9393.18.12257-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Predictive models of acute kidney injury after cardiac surgery (CS-AKI) include emergency surgery and patients with hemodynamic instability. Our objective was to evaluate the performance of validated predictive models (Thakar and Demirjian) in elective cardiac surgery and to propose a better score in the case of poor performance. METHODS A prospective, multicenter, observational study was designed. Data were collected from 942 patients undergoing cardiac surgery, after excluding emergency surgery and patients with an intra-aortic balloon pump. The main outcome measure was CS-AKI defined by the composite of requiring dialysis or doubling baseline creatinine values. RESULTS Both models showed poor discrimination in elective surgery (Thakar's model, AUC=0.57, 95% CI: 0.50-0.64 and Demirjian's model, AUC=0.64, 95% CI: 0.58-0.71). We generated a new model whose significant independent predictors were: anemia, age, hypertension, obesity, congestive heart failure, previous cardiac surgery and type of surgery. It classifies patients with scores 0-3 as at low risk (<5%), patients with scores 4-7 as at medium risk (up to 15%), and patients with scores >8 as at high risk (>30%) of developing CS-AKI with a statistically significant correlation (P<0.001). Our model reflects acceptable discriminatory ability (AUC=0.72, 95% CI: 0.66-0.78) which is significantly better than Thakar and Demirjian's models (P<0.01). CONCLUSIONS We developed a new simple predictive model of CS-AKI in elective surgery based on available preoperative information. Our new model is easy to calculate and can be an effective tool for communicating risk to patients and guiding decision-making in the perioperative period. The study requires external validation.
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Affiliation(s)
- Raquel Callejas
- Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain -
| | - Alfredo Panadero
- Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Marc Vives
- Department of Anesthesia and Critical Care, Josep Trueta University Hospital, Girona, Spain
| | - Paula Duque
- Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Gemma Echarri
- Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Pablo Monedero
- Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
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Zangrillo A, Alvaro G, Belletti A, Pisano A, Brazzi L, Calabrò MG, Guarracino F, Bove T, Grigoryev EV, Monaco F, Boboshko VA, Likhvantsev VV, Scandroglio AM, Paternoster G, Lembo R, Frassoni S, Comis M, Pasyuga VV, Navalesi P, Lomivorotov VV. Effect of Levosimendan on Renal Outcome in Cardiac Surgery Patients With Chronic Kidney Disease and Perioperative Cardiovascular Dysfunction: A Substudy of a Multicenter Randomized Trial. J Cardiothorac Vasc Anesth 2018; 32:2152-2159. [PMID: 29580796 DOI: 10.1053/j.jvca.2018.02.039] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Acute kidney injury (AKI) occurs frequently after cardiac surgery. Levosimendan might reduce the incidence of AKI in patients undergoing cardiac surgery. The authors investigated whether levosimendan administration could reduce AKI incidence in a high-risk cardiac surgical population. DESIGN Post hoc analysis of a multicenter randomized trial. SETTING Cardiac surgery operating rooms and intensive care units of 14 centers in 3 countries. PARTICIPANTS The study comprised 90 patients who underwent mitral valve surgery with an estimated glomerular filtration rate <60 mL/min/1.73 m2 and perioperative myocardial dysfunction. INTERVENTIONS Patients were assigned randomly to receive levosimendan (0.025-0.2 μg/kg/min) or placebo in addition to standard inotropic treatment. MEASUREMENTS AND MAIN RESULTS Forty-six patients were assigned to receive levosimendan and 44 to receive placebo. Postoperative AKI occurred in 14 (30%) patients in the levosimendan group versus 23 (52%) in the placebo group (absolute difference -21.8; 95% confidence interval -41.7 to -1.97; p = 0.035). The incidence of major complications also was lower (18 [39%]) in the levosimendan group versus that in the placebo group (29 [66%]) (absolute difference -26.8 [-46.7 to -6.90]; p = 0.011). A trend toward lower serum creatinine at intensive care unit discharge was observed in the levosimendan group (1.18 [0.99-1.49] mg/dL) versus that in the placebo group (1.39 [1.05-1.76] mg/dL) (95% confidence interval -0.23 [-0.49 to 0.01]; p = 0.07). CONCLUSIONS Levosimendan may improve renal outcome in cardiac surgery patients with chronic kidney disease undergoing mitral valve surgery who develop perioperative myocardial dysfunction. Results of this exploratory analysis should be investigated in future properly designed randomized controlled trials.
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Affiliation(s)
- Alberto Zangrillo
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Gabriele Alvaro
- Department of Anesthesia and Intensive Care, AOU Mater Domini Germaneto, Catanzaro, Italy
| | - Alessandro Belletti
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Antonio Pisano
- Division of Cardiac Anesthesia and Intensive Care Unit, AORN dei Colli - Monaldi Hospital, Naples, Italy
| | - Luca Brazzi
- Department of Anesthesia and Intensive Care, AOU Città della Salute e della Scienza, Turin, Italy; Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Maria G Calabrò
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Fabio Guarracino
- Division of Cardiothoracic Anesthesia and Intensive Care, Department of Anesthesia and Critical Care Medicine, AOU Pisana, Pisa, Italy
| | - Tiziana Bove
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Evgeny V Grigoryev
- Department of Anesthesiology and Intensive Care, State Research Institute for Complex Issues of Cardiovascular Disease, Kemerovo, Russia
| | - Fabrizio Monaco
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Vladimir A Boboshko
- Department of Anesthesiology and Intensive Care, E. Meshalkin National Medical Research Center, Novosibirsk, Russia
| | - Valery V Likhvantsev
- Department of Anesthesiology and Intensive Care, Moscow Regional Clinical and Research Institute, Moscow, Russia
| | - Anna M Scandroglio
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Rosalba Lembo
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Samuele Frassoni
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Comis
- Department of Cardiovascular Anesthesia and Intensive Care, AO Ordine Mauriziano, Turin, Italy
| | - Vadim V Pasyuga
- Department of Anesthesiology and Intensive Care, Federal Center for Cardiovascular Surgery Astrakhan, Astrakhan, Russia
| | - Paolo Navalesi
- Department of Anesthesia and Intensive Care, AOU Mater Domini Germaneto, Catanzaro, Italy; Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Vladimir V Lomivorotov
- Department of Anesthesiology and Intensive Care, E. Meshalkin National Medical Research Center, Novosibirsk, Russia
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Kumada Y, Yoshitani K, Shimabara Y, Ohnishi Y. Perioperative risk factors for acute kidney injury after off-pump coronary artery bypass grafting: a retrospective study. JA Clin Rep 2017; 3:55. [PMID: 29457099 PMCID: PMC5804651 DOI: 10.1186/s40981-017-0125-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 09/28/2017] [Indexed: 12/21/2022] Open
Abstract
Background Acute kidney injury (AKI) after cardiac surgery is associated with increased morbidity and mortality. Although morbidity of AKI after off-pump coronary artery bypass grafting (OPCAB) has been investigated, little is known about risk factors for AKI after OPCAB. To identify risk factors for AKI, we examined the association between perioperative variables and AKI after OPCAB. Findings We reviewed the medical records of consecutive adult patients who underwent isolated OPCAB between January 2010 and February 2013 in a single institute, retrospectively. The primary outcome was the incidence of AKI evaluated using Acute Kidney Injury Network classifications during the first 48 h postoperatively. We investigated preoperative and intraoperative variables, including hemodynamic parameters, as potential risk factors for AKI. The relationship between candidates of AKI and incidence of AKI was examined by multivariate logistic regression analysis.A total of 298 patients were enrolled in this study. Acute kidney injury occurred in 47 patients (15.7%). Multivariate logistic regression analysis showed that intraoperative furosemide administration (odds ratio [OR], 5.163; 95% confidence interval, 2.171 to 12.185; P < 0.001] and diabetes mellitus (OR, 1.954; 95% confidence interval, 1.004 to 3.880; P = 0.049) were significantly associated with AKI. Conclusions Intraoperative furosemide administration and diabetes mellitus were significantly associated with AKI in patients who had received OPCAB.
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Affiliation(s)
- Yuta Kumada
- 1Department of Anesthesiology, National Cerebral and Cardiovascular Center, 5-7-1, Fujishirodai, Suita, Osaka 565-8565 Japan
| | - Kenji Yoshitani
- 1Department of Anesthesiology, National Cerebral and Cardiovascular Center, 5-7-1, Fujishirodai, Suita, Osaka 565-8565 Japan
| | - Yusuke Shimabara
- 2Department of Cardiac Surgery, National Cerebral and Cardiovascular Center, 5-7-1, Fujishirodai, Suita, Osaka 565-8565 Japan
| | - Yoshihiko Ohnishi
- 1Department of Anesthesiology, National Cerebral and Cardiovascular Center, 5-7-1, Fujishirodai, Suita, Osaka 565-8565 Japan
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Abstract
High-value CCC is rapidly evolving to meet the demands of increased patient acuity and to incorporate advances in technology. The high-performing CCC system and culture should aim to learn quickly and continuously improve. CCC demands a proactive, interactive, precise, an expert team, and continuity.
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Yamauchi T, Miyagawa S, Yoshikawa Y, Toda K, Sawa Y. Risk Index for Postoperative Acute Kidney Injury After Valvular Surgery Using Cardiopulmonary Bypass. Ann Thorac Surg 2017; 104:868-875. [DOI: 10.1016/j.athoracsur.2017.02.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 02/03/2017] [Accepted: 02/06/2017] [Indexed: 12/30/2022]
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