Supplemental Digital Content is available in the text. Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point—comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease—were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model’s performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660–0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636–0.810) and 0.529 (95% CI, 0.403–0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

中文翻译：

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机器学习方法在预测年轻高血压患者临床结果方面的价值

补充数字内容在文本中可用。年轻高血压患者的风险分层仍然具有挑战性。通常，机器学习 (ML) 被认为是传统临床预测方法的有前途的替代方法，因为它能够处理大量复杂数据。因此，我们探索了 ML 方法用于预测年轻高血压患者预后的可行性，并将其性能与现在临床实践中常用的方法进行比较。基线临床数据和复合终点——包括全因死亡、急性心肌梗死、冠状动脉血运重建、新发心力衰竭、新发心房颤动/心房扑动、持续性室性心动过速/心室颤动、外周动脉血运重建、新发- 中风，终末期肾病——在 508 名在三级医院接受过治疗的年轻高血压患者（30.83±6.17 岁）中进行了评估。在 33 个月的随访评估中构建了由递归特征消除、极端梯度提升和 10 倍交叉验证组成的 ML 模型，并将模型性能与 Cox 回归和重新校准弗雷明汉风险评分模型。11 个变量的组合被认为对于使用 ML 方法预测结果最有价值。对于 ML 模型，用于识别具有复合终点患者的 C 统计量为 0.757（95% CI，0.660–0.854），而对于 Cox 回归模型和重新校准的 Framingham 风险评分模型，C 统计量为 0.723（95% CI，0.636–0.810）和 0.529（95% CI，0.403–0.655）。