Development and Validation of 10‐Year Prediction Models for Cardiovascular, Kidney and Metabolic Events Among Chinese Patients With Prediabetes in Hong Kong

ABSTRACT Aims Risk stratification can support frontline doctors and patients by enabling care and empowering early prevention of cardiovascular, kidney and metabolic disease. We aimed to develop and validate 10‐year prediction models for seven major clinical events and mortality among Chinese patients with prediabetes in Hong Kong. Methods This population‐based, retrospective cohort study identified 129 834 adults with prediabetes in 2012 and followed them through 2022. Backward stepwise Cox regression was used to develop an office‐based non‐laboratory model and three extended models with laboratory results, transformations, interactions and medications. Outcomes included cardiovascular disease (CVD), hypertension, Type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), CVD subtypes and all‐cause mortality. Four survival machine learning (ML) models were used for comparison. For each outcome, two models for haemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) were developed. Model performance was assessed by discrimination, calibration and decision curve analysis. Results During a median follow‐up of 10.4 years, 19.4% of patients developed CVD. Key predictors included age, sex, smoking status, Charlson comorbidity index, body mass index, systolic blood pressure, lipid profiles, estimated glomerular filtration rate and medications. Our Cox models demonstrated moderate‐to‐good discrimination: CVD (C‐index 0.702), hypertension (0.697), T2DM (0.704), CKD (0.790), mortality (0.816) and CVD subtypes (range: 0.673–0.830), with adequate calibration and positive net benefits. Four selected ML approaches showed comparable performance. HbA1c‐ and FPG‐based models showed comparable performance. Conclusions We developed interpretable prediction models for clinically actionable events among Chinese patients with prediabetes in Hong Kong to inform clinical strategies and resource allocation.