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Development and Validation of a Machine Learning Model for Incident Heart Failure Prediction in Chronic Kidney
Yi Lu1,2, Junzhe Chen1, Shiyu Zhou3
1Department of Nephrology The Third Affiliated Hospital of Southern Medical University Guangzhou China.
Insights
A new heart failure (HF) risk model for chronic kidney disease (CKD) patients was developed using machine learning. This 9-variable model accurately predicts HF risk in CKD populations, improving upon existing general models.
Area of Science:
- Nephrology
- Cardiology
- Data Science
Background:
- Chronic kidney disease (CKD) and heart failure (HF) share common pathophysiological pathways.
- HF is a major cardiovascular complication in CKD patients.
- Existing HF prediction models lack accuracy in CKD populations.
Purpose of the Study:
- To develop and validate a CKD-specific risk prediction model for new-onset heart failure.
- To improve the accuracy of HF risk assessment in patients with chronic kidney disease.
Main Methods:
- Utilized a large development set of 52,251 CKD patients from the China Renal Data System for model training and internal validation.
- Externally validated the model in independent Chinese cohorts (21,798 patients) and the UK Biobank (3,323 participants).
- Developed five machine learning models, selecting the top-performing extreme gradient boosting model, simplified to nine key predictors.
Main Results:
- The simplified extreme gradient boosting model demonstrated high predictive performance (AUC 0.879 in Chinese cohort, 0.851 in UK Biobank).
- Key predictors identified include estimated glomerular filtration rate and albuminuria.
- The CKD-specific model significantly outperformed established risk scores like ARIC and CVD-PREDICT.
Conclusions:
- A 9-variable extreme gradient boosting model offers a tailored approach to predict heart failure risk in CKD patients.
- This model can aid clinicians in identifying high-risk individuals within the CKD population.
- Further validation in diverse global populations is recommended to confirm generalizability.
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.
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