Development and Validation of a Machine Learning Model for Incident Heart Failure Prediction in Chronic Kidney Disease: A Multicenter Cohort Study.

backgroundChronic kidney disease (CKD) and heart failure (HF) share pathophysiological mechanisms, rendering HF one of the most burdensome cardiovascular complication in CKD. Current HF prediction models, derived from the general population, exhibit limited accuracy in CKD, thus necessitating a CKD-specific risk model and clinical implementation.

methodsThe development set comprised 52 251 patients with CKD from the China Renal Data System (70% training; 30% internal validation). External validation used 21 798 patients from independent Chinese hospitals and 3323 UK Biobank participants. Outcome was 5-year new-onset HF. Five machine learning models were developed, with performance assessed via area under the curve and compared using DeLong test. The top-performing extreme gradient boosting model was simplified via forward stepwise selection; feature importance quantified using Shapley additive explanations.

resultsIn the Chinese external validation cohort, the extreme gradient boosting model outperformed others (area under the curve, 0.879 [95% CI, 0.871-0.887]; DeLong

conclusionsThe 9-variable extreme gradient boosting model tailored for patients with CKD may help predict HF risk in this high-risk population. Validation across diverse populations is warranted to confirm its generalizability.