The UK Biobank CVD risk prediction model (UKCRP) achieved an AUC of 0.762 for predicting 10-year incident cardiovascular disease, outperforming multiple existing models (p<0.001).
Cohort (n=473,611)
Yes
Does a machine learning-based risk prediction model improve the prediction of 10-year incident cardiovascular disease compared to existing clinical models in CVD-free adults?
A machine learning-based model using 10 predictors (UKCRP) demonstrated superior discriminative performance for 10-year cardiovascular disease risk compared to established clinical models like ASCVD and SCORE.
Effect estimate: AUC 0.762±0.010
p-value: p=<0.001
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.
You et al. (Thu,) conducted a cohort in Cardiovascular disease (CVD) (n=473,611). UK Biobank CVD risk prediction model (UKCRP) vs. Existing cardiovascular disease prediction models (e.g., QRISK V.3, SCORE V.2, AHA/ASCVD, FGCRS) was evaluated on 10-year risk of incident cardiovascular disease (CVD) (AUC 0.762±0.010, p=<0.001). The UK Biobank CVD risk prediction model (UKCRP) achieved an AUC of 0.762 for predicting 10-year incident cardiovascular disease, outperforming multiple existing models (p<0.001).