PT  - JOURNAL ARTICLE
AU  - You, Jia
AU  - Guo, Yu
AU  - Kang, Ju-Jiao
AU  - Wang, Hui-Fu
AU  - Yang, Ming
AU  - Feng, Jian-Feng
AU  - Yu, Jin-Tai
AU  - Cheng, Wei
TI  - Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study
AID  - 10.1136/svn-2023-002332
DP  - 2023 Apr 27
TA  - Stroke and Vascular Neurology
PG  - svn-2023-002332
4099  - http://svn.bmj.com/content/early/2023/04/26/svn-2023-002332.short
4100  - http://svn.bmj.com/content/early/2023/04/26/svn-2023-002332.full
AB  - Background Previous prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model.Methods From a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation.Results During a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.Data are available in a public, open access repository. All data used in this study were accessed from the publicly available UK Biobank Resource under application number 19542. These data cannot be shared with other investigators.