Zhou, Yekai, Lin, Celia Jiaxi, Yu, Qiuyan, Blais, Joseph Edgar, Wan, Eric Yuk Fai, Wong, Emmanuel, Tan, Kathryn, Siu, David Chung-Wah, Yiu, Kai Hang, Chan, Esther Wai Yin, Yu, Doris, Wong, William, Lam, Tak-Wah, Wong, Ian Chi Kei, Luo, Ruibang and Chui, Celine S. L. (2025). Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach. PLoS ONE, 20 (7),
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of mortality and morbidity in China and worldwide while we are lacking in validated primary prevention model specifically for Chinese. To identify CVD high-risk individuals for early intervention, we created and validated a primary prevention risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (1°P-CARDIAC), in contemporary Chinese cohorts in Hong Kong. Methods: Patients without any history of CVD was categorized as derivation and validation cohorts based on their different geographical location of residence in Hong Kong. The outcome was the first diagnosis of a composite of coronary heart disease, ischemic or hemorrhagic stroke, peripheral artery disease, and revascularization. The full model incorporated all available variables in the dataset as clinical laboratory tests, disease and medication history, family history of disease, demographic factors, and healthcare utilization. We employed XGBoost Cox model and multivariate imputation with chained equation (MICE) for derivation and missing data replacement. A basic model was developed with the integration of statistically significant and important subset of risk variables by least absolute shrinkage and selection operator (LASSO) regression. Validation was performed by 1000 bootstrap replicates and compared to four existing models: PREDICT, pooled cohort equation (PCE), China-PAR, and Framingham (Asian). Results: The study included 179,953 patients in the derivation cohort and 1,083,924 patients across two independent validation cohorts. A total of 103 covariates were included in the full model whilst 8 covariates were included the basic model. It demonstrated good performance with C-statistic of 0.87 (95% CI: 0.87, 0.87), calibration slope of 0.94 in the full model. The C-statistic in the basic model was 0.75 (95% CI: 0.75, 0.75) with calibration slope of 0.91. Other comparison risk models have lower C statistic ranging from 0.68 to 0.72. Conclusion: We developed and validated 1°P-CARDIAC, a CVD risk prediction model for primary prevention applying a novel hybrid statistical and machine-learning approach. Validation results suggest that it may offer improved performance compared to commonly used risk models. The 1°P-CARDIAC yields the similar level of accuracy and performance between basic and full model. It demonstrated both effectiveness and versatility in harnessing the power of big data and which has the potential to serve as a promising method for CVD primary prevention and improving public health outcome.
Publication DOI: | https://doi.org/10.1371/journal.pone.0322419 |
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Divisions: | College of Health & Life Sciences > Aston Pharmacy School College of Health & Life Sciences Aston University (General) |
Funding Information: | This study is funded by the Innovation and Technology Fund, Innovation and Technology Commission in Hong Kong and Amgen Asia Holdings Limited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of th |
Additional Information: | Copyright © 2025 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Publication ISSN: | 1932-6203 |
Last Modified: | 30 Jul 2025 08:13 |
Date Deposited: | 29 Jul 2025 09:04 |
Full Text Link: | |
Related URLs: |
https://journal ... al.pone.0322419
(Publisher URL) |
PURE Output Type: | Article |
Published Date: | 2025-07-28 |
Accepted Date: | 2025-06-12 |
Authors: |
Zhou, Yekai
Lin, Celia Jiaxi Yu, Qiuyan Blais, Joseph Edgar Wan, Eric Yuk Fai Wong, Emmanuel Tan, Kathryn Siu, David Chung-Wah Yiu, Kai Hang Chan, Esther Wai Yin Yu, Doris Wong, William Lam, Tak-Wah Wong, Ian Chi Kei ( ![]() Luo, Ruibang Chui, Celine S. L. |