ArticleFrontiers in surgery2022
Machine learning for the prediction of acute kidney injury in patients after cardiac surgery.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 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
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.Journal of clinical medicine · 2026Review
- Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.iScience · 2026Article
- Comparative Performance of 3 Analytical Models in Identifying Associated Factors of Pulmonary Dysfunction-Depression Comorbidity: China Health and Retirement Longitudinal Study-Based Nationwide Cross-Sectional Study.JMIR medical informatics · 2026Article
- Predicting In-Hospital Acute Kidney Injury after Cardiac Surgery Using Machine Learning.Journal of cardiothoracic and vascular anesthesia · 2026Observational
- Association between stress hyperglycemia ratio and 28-day all-cause mortality in critically ill atrial fibrillation patients: a retrospective cohort study based on the MIMIC-IV database.Journal of thoracic disease · 2025Article
- From Logistic Regression to Foundation Models: Factors Associated With Improved Forecasts.Cureus · 2025Review
- Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net.Journal of bone oncology · 2025Article
- Clinical decision-making in bone cancer care management and forecast of ICU needs based on computed tomography.Journal of bone oncology · 2024Article
- A patch-based deep learning MRI segmentation model for improving efficiency and clinical examination of the spinal tumor.Journal of bone oncology · 2024Article
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Prediction of Acute Kidney Injury Following Isolated Coronary Artery Bypass Grafting in Heart Failure Patients with Preserved Ejection Fraction Using Machine Leaning with a Novel Nomogram.Reviews in cardiovascular medicine · 2024Article
- 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
- Development and validation of a machine learning-based predictive model for secondary post-tonsillectomy hemorrhage.Frontiers in surgery · 2023Article
- Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis.Frontiers in artificial intelligence · 2023Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792-0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.
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