Key result
LightGBM model predicts CSA-AKI in older adults with an AUC of ~0.78.
Why the study?
Cardiac surgery-associated acute kidney injury is a frequent and devastating postoperative complication, particularly among older adults, making accurate risk stratification and early prediction essential.
Does a machine learning model (LightGBM) accurately predict the risk of cardiac surgery-associated acute kidney injury in older adults?
Cohort (n=733)
Yes
Does a machine learning model (LightGBM) accurately predict the risk of cardiac surgery-associated acute kidney injury in older adults?
A LightGBM machine learning model incorporating intraoperative and baseline variables effectively predicts the risk of acute kidney injury in older adults following cardiac surgery, outperforming traditional logistic regression.
Answers are grounded in this paper and cite their sources.
Does not yet support clinical use of LightGBM for post-cardiac surgery AKI prediction in older adults; leaves open need for prospective validation.
Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a frequent and devastating postoperative complication, particularly among older adults. Accurate risk stratification and early prediction of CSA-AKI are essential for guiding preventive strategies and optimizing clinical decision-making. Methods: In this retrospective study, data from two centers (n=623) were utilized for model training and internal validation, whereas data from a third, distinct center (n=110) were reserved for external validation. CSA-AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) Serum creatinine criteria. Key predictors were identified using a consensus of four methods: Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), Boruta, and Random Forest-based filtering. Six machine learning (ML) models, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were developed utilizing five-fold cross-validation. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) approach was applied to interpret the best-performing model. Results: Development of CSA-AKI was noted in 177 patients (24.1%) during the first postoperative week. In terms of comparative performance, LightGBM exhibited the greatest AUC (0.784, 95% confidence interval [CI]: 0.702– 0.859). The most influential features were lactate, surgical duration, activated partial thromboplastin time (APTT), transfusion volume, and Prothrombin Time (PT). SHAP-based summary and force plots interpreted the model at global and local levels. Furthermore, SHAP dependence plots elucidated non-linear effects of single features on CSA-AKI risk. Conclusion: Machine learning models demonstrate high efficacy in predicting CSA-AKI risk in older adults. The LightGBM model outperformed other algorithms; coupled with interpretability tools, it can assist clinicians to identify high-risk patients earlier and optimize perioperative management.
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Fu et al. (2026) conducted a cohort in Cardiac surgery-associated acute kidney injury (CSA-AKI) (n=733). LightGBM machine learning model vs. Logistic Regression was evaluated on Prediction of CSA-AKI (Area Under the Curve) (AUC 0.784, 95% CI 0.702-0.859). The LightGBM machine learning model effectively predicted cardiac surgery-associated acute kidney injury in older adults, achieving an AUC of 0.784.
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