Patient characteristics.

<div><p>Background</p><p>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 c...

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Main Author:	Yekai Zhou (17285873) (author)
Other Authors:	Celia Jiaxi Lin (21799742) (author), Qiuyan Yu (693213) (author), Joseph Edgar Blais (11785404) (author), Eric Yuk Fai Wan (8764275) (author), Emmanuel Wong (21799745) (author), Kathryn Tan (4110373) (author), David Chung-Wah Siu (21799748) (author), Kai Hang Yiu (12997416) (author), Esther Wai Yin Chan (8764280) (author), Doris Yu (10147354) (author), William Wong (238611) (author), Tak-Wah Lam (361424) (author), Ian Chi Kei Wong (7243154) (author), Ruibang Luo (237944) (author), Celine S. L. Chui (4781772) (author)
Published:	2025
Subjects:	Medicine Genetics Biotechnology Marine Biology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified primary prevention applying least absolute shrinkage four existing models different geographical location clinical laboratory tests 1000 bootstrap replicates missing data replacement peripheral artery disease identify cvd high cvd primary prevention coronary heart disease comparison risk models validation results suggest validation cohorts based study included 179 pooled cohort equation novel hybrid statistical 1 &# 176 full model incorporated pce ), china contemporary chinese cohorts demonstrated good performance hybrid statistical full model contemporary chinese chained equation big data risk variables risk individuals validation using cardiac ), xlink "> statistically significant similar level selection operator promising method multivariate imputation learning approach leading cause important subset hong kong hemorrhagic stroke healthcare utilization first diagnosis early intervention derivation cohort demographic factors calibration slope available variables 953 patients
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Patient characteristics.

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