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.

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开发基于机器学习的模型来预测 10 年心血管疾病风险：一项前瞻性队列研究

背景 以前的心血管疾病（CVD）预测算法是使用主要基于经验临床知识检索的风险因素建立的。本研究试图在综合变量空间中识别预测因子，然后采用机器学习 (ML) 算法开发新型 CVD 风险预测模型。方法 这项研究来自英国生物银行的一个基于人群的纵向队列，包括 473 611 名年龄在 37 岁至 73 岁之间的无 CVD 参与者。我们实施了基于 ML 的数据驱动管道，从涵盖全面健康相关因素的 645 个候选变量中识别预测因子，并评估了多个 ML 分类器，以建立 10 年 CVD 事件的风险预测模型。该模型通过留一中心交叉验证进行验证。结果 在中位随访 12.2 年期间，31 466 名参与者在基线就诊后 10 年内出现了 CVD。建立了一种新颖的英国生物银行CVD风险预测（UKCRP）模型，该模型包含10个预测因素，包括年龄、性别、胆固醇和血压药物、胆固醇比率（总/高密度脂蛋白）、收缩压、既往心绞痛或心脏病、服用的药物数量、胱抑素 C、胸痛和吸烟年数。我们的模型获得了令人满意的判别性能，受试者工作特征曲线下面积 (AUC) 为 0.762±0.010，优于多个现有的临床模型，并且经过良好校准，Brier 评分为 0.057±0.006。此外，UKCRP对于心肌梗塞（AUC 0.774±0.011）和缺血性中风（AUC 0.730±0.020）可以获得相当的性能，但对于出血性中风（AUC 0.644±0.026）性能较差。结论 基于 ML 的分类模型可以从潜在的高风险 CVD 参与者中学习表达表征，这些参与者可能会从早期的临床决策中受益。数据可在公共、开放访问存储库中获取。本研究中使用的所有数据均来自公开可用的英国生物银行资源，申请号为 19542。这些数据不能与其他研究人员共享。