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

Clinical Snapshot

Key Findings

• P Value: DeLong P<0.05 for model comparisons

• Effect Size: AUC 0.879 (Chinese validation) and 0.851 (UK Biobank validation)

• Primary Outcome: 5-year incident heart failure in chronic kidney disease patients

• Nnt Or Sensitivity: ΔC-statistic improvement of 0.050-0.194 over existing cardiovascular risk scores

• Confidence Interval: 95% CI 0.871-0.887 (Chinese) and 0.831-0.870 (UK)

Clinical Application

High feasibility with web-based calculator using 9 readily available clinical variables Relevant to Australian nephrology and cardiology practice; could inform PBS eligibility for heart failure medications and guide specialist referral patterns under Medicare Adults with chronic kidney disease requiring cardiovascular risk stratification

Abstract

BACKGROUND: Chronic 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. METHODS: The 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. RESULTS: In 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 P<0.05), with an area under the curve of 0.851 (95% CI, 0.831-0.870) in the UK Biobank cohort. The model was simplified to 9 predictors; Shapley additive explanations analysis ranked estimated glomerular filtration rate and albuminuria as among the most important features. In Chinese and UK Biobank external validation sets, the simplified extreme gradient boosting model showed a ΔC-statistic of 0.050 (0.045-0.056) and 0.006 (-0.013-0.024) versus the Atherosclerosis Risk in Communities risk score, and 0.194 (0.183-0.204) and 0.145 (0.107-0.182) versus the Predicting Risk of Cardiovascular Disease Events Equation, respectively. A web-based calculator was deployed (https://clinician.shinyapps.io/HF_Risk_Predictor_for_CKD/). CONCLUSIONS: The 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.

References

1. 1.Lu, Y., Chen, J., Zhou, S., Lukwaro, A. F., Zhang, X., Yu, W., Chen, H., Chen, Y., Nie, S., & Tang, Y. (2026). Development and validation of a machine learning model for incident heart failure prediction in chronic kidney disease: A multicenter cohort study. Journal of the American Heart Association, 15(7), e046156. https://doi.org/10.1161/JAHA.125.046156