ArticleNPJ digital medicine2026
Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
14 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.