Systematic Review of Cardiovascular Risk Prediction Models for the Korean Population.
TL;DR
Nine Korean CVD risk prediction models were identified, all lacking adequate external validation, andidated CVD risk prediction models are needed to optimize risk-based CVD prevention in Korea.
Abstract
Introduction of cardiovascular disease (CVD) risk prediction models is necessary for optimal risk stratification for CVD prevention. While several Korean models have been developed, their comprehensive and comparative assessment remain limited. We aimed to systematically review CVD risk prediction models developed for the Korean population.We performed a comprehensive literature search across PubMed, Embase, KoreaMed, and Kmbase databases up to September 2025. Two authors independently screened the titles, abstracts, and full texts of retrieved articles. Information on model characteristics was extracted using a standardized form, and the quality of the models was assessed using the Prediction model Risk Of Bias Assessment Tool. A total of 2,399 articles were initially identified, of which 9 were included in the systematic review. The prediction horizon of the models ranged from 3 to 12 years, with 6 models predicting 10-year risk. All 9 models included established risk factors such as age, smoking status, systolic blood pressure, and total cholesterol as risk predictors. Five models predicted a composite of myocardial infarction (MI) and stroke, 3 models predicted stroke, and one model predicted MI. Internal validation was performed for all models, with discriminatory performance reported across all models and calibration measures reported in 6 models. None had undergone adequate external validation. Nine Korean CVD risk prediction models were identified, all lacking adequate external validation. Validated CVD risk prediction models are needed to optimize risk-based CVD prevention in Korea.