ArticleBMC nephrology2023
Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery.
Article in BMC nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- The Systemic Immune Inflammation Index as a Novel Predictive Biomarker for Contrast-Induced Acute Kidney Injury Risk Following Percutaneous Coronary Intervention: A Meta-Analysis of Cohort Studies.Current vascular pharmacology · 2024Pooled it
- Acute kidney injury prediction and prognostication using machine learning.International urology and nephrology · 2026Review
- From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.International urology and nephrology · 2026Review
- Integration of intraoperative data in interpretable machine learning models to predict postoperative AKI in noncardiac surgery patients.JAMIA open · 2026Article
- Interpretable machine learning to predict postoperative adverse outcomes in cardiac surgery.BMC anesthesiology · 2026Article
- Development and validation of an explainable machine learning model for predicting acute kidney injury after robot-assisted partial nephrectomy: a retrospective multicenter study.BMC nephrology · 2025Article
- Machine learning prediction of contrast-induced AKI after PCI using the Systemic Immune-inflammation Index: insights from MIMIC-IV.Renal failure · 2025Article
- Perioperative multivariate analysis and risk prediction of acute kidney injury after cardiac surgery: Based on dynamic temperature changes during cardiopulmonary bypass.Journal of anesthesia and translational medicine · 2025Article
- Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery.International journal of nephrology and renovascular disease · 2024Article
- Article
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5 authors.
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Abstract
objectivePostoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI.
methodsThe retrospective cohort study included 2310 adult patients undergoing cardiac surgery in a tertiary teaching hospital, China. Postoperative AKI and severe AKI were identified by the modified KDIGO definition. The sample was randomly divided into a derivation set and a validation set based on a ratio of 4:1. Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO-AKI. We implemented the interpretation of models using SHapley Additive exPlanation (SHAP) analysis.
resultsPostoperative AKI and severe AKI occurred in 1020 (44.2%) and 286 (12.4%) patients, respectively. Compared with the five ML models, LR model for PO-AKI exhibited the largest AUC (0.812, 95%CI: 0.756, 0.860, all P < 0.05), sensitivity (0.774, 95%CI: 0.719, 0.813), accuracy (0.753, 95%CI: 0.719, 0.781) and Youden index (0.513, 95%CI: 0.451, 0.573). Regarding severe AKI, GBC algorithm showed a significantly higher AUC than the other four ML models (all P < 0.05). Although no significant difference (P = 0.173) was observed in AUCs between GBC (0.86, 95%CI: 0.808, 0.902) and conventional logistic regression (0.803, 95%CI: 0.746, 0.852), GBC achieved greater sensitivity, accuracy and Youden index than conventional LR. Notably, SHAP analyses showed that preoperative serum creatinine, hyperlipidemia, lipid-lowering agents and assisted ventilation time were consistently among the top five important predictors for both postoperative AKI and severe AKI.
conclusionLogistic regression and GBC algorithm demonstrated moderate to good discrimination and superior performance in predicting PO-AKI and severe AKI, respectively. Interpretation of the models identified the key contributors to the predictions, which could potentially inform clinical interventions.
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