Explainable machine learning prediction of 1-year kidney function progression among patients with type 2 diabetes mellitus and chronic kidney disease: a retrospective study.

Abstract

Objective: This study aimed to develop and validate a risk prediction model for 1-year CKD progression in patients with T2DM and CKD by employing various machine learning (ML) algorithms.

Methods: This study included a total of 12,151 patients with T2DM and CKD with estimated glomerular filtration rate (eGFR) between 30 and 59.9 mL/min/1.73 m2 from a tertiary hospital in Wuhan, enrolled between 2012 and 2024. The cohort was divided into a training set of 5,954 patients, an internal validation set of 2,552 patients, and an external validation set of 3,645 patients. We developed 1-year CKD progression risk prediction models using 10 different machine learning algorithms. CKD progression was defined as a decline in eGFR by more than 30% from baseline and/or a reduction in eGFR to below 15 mL/min/1.73 m2. The SHAP (SHapley Additive exPlanations) method was utilized to explain the predictions of a model.

Results: Among the 10 ML models, the XGBoost model achieved the best predictive performance for 1-year progression of kidney function with an AUC of 0.906 in the internal validation set and 0.768 in the external validation set. The final predictive model incorporating only nine variables has been implemented into a web application to enhance its usability in clinical settings.

Conclusion: Our findings suggest that the XGBoost model may serve as a valuable decision-support tool for predicting kidney function decline in patients with T2DM and CKD.