Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.

识别主要不良心血管事件 (MACE) 风险增加的个体非常重要。然而，缺乏针对老年人的算法。数据分析来自一项随机试验，该试验涉及 18,548 名年龄≥ 70 岁（平均年龄 75.4 岁）的参与者，之前没有心血管疾病事件、痴呆或身体残疾。MACE包括冠心病死亡、致死性或非致死性缺血性中风或心肌梗塞。测试的潜在预测因素基于先前的证据并使用机器学习方法。Cox 回归分析用于计算 5 年预测风险，并根据受试者工作特征曲线评估区分度。还评估了校准，并使用引导程序在内部验证了结果。在初级保健环境中对 25,138 名健康老年人进行了外部验证。在中位随访 4.7 年期间，发生了 594 例 MACE。最终模型中的预测因素包括年龄、性别、吸烟、收缩压、高密度脂蛋白胆固醇 (HDL-c)、非 HDL-c、血清肌酐、糖尿病和抗高血压药物的摄入量。通过基于机器学习的变量选择，年龄、性别和肌酐是最重要的预测因素。最终模型的曲线下面积 (AUC) 为 68.1（95% 置信区间为 65.9；70.4）。该模型在内部验证中的 AUC 为 67.5，在外部验证中的 AUC 为 64.2。该模型很好地对风险进行了排序，但低估了外部验证队列中的绝对风险。预测健康老年人 MACE 事件的模型包括公认的、潜在可逆的危险因素，尤其是肾功能。当用于其他人群时，需要进行校准。