Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 41699026.
- Also identified by DOI 10.1038/s41746-026-02439-2 and PMC identifier 13022490.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Chronic kidney disease (CKD) is a common complication of type 2 diabetes mellitus (T2DM), with limited predictive tools for individualized prognosis, particularly in Asian populations. We developed deep learning-based prognostic models using a 17-year longitudinal electronic health record dataset from 569,680 individuals across 165 public healthcare facilities in Hong Kong. By integrating clinical, biochemical, and prescription history data, the models achieved robust time-dependent predictions of CKD progression at 2-, 5-, and 10-year intervals, with the area under the receiver operating characteristic curve (AUC) of 87.1%, 85.3%, and 84.7%, respectively. Shapley Additive exPlanations (SHAP) revealed key predictors, including serum creatinine, sex, age, and angiotensin prescription history. External validation in the UK Biobank and China Health and Retirement Longitudinal Study (CHARLS) cohorts confirmed generalizability, with AUCs ranging from 74.6% to 82.0%. These models provide a scalable and interpretable framework for early risk stratification and personalized intervention for T2DM-related CKD progression.