From the Hypertension Center (X.W., W.W., K.L., Y.Q., X.S., W.M., Y.Z., H.Z., X.Z., H.W., X.J., J.C., L.S.), Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
School of Reliability and Systems Engineering, Beihang University, Beijing, People's Republic of China (X.Y., W.C., S.Z.).
Hypertension. 2020 May;75(5):1271-1278. doi: 10.1161/HYPERTENSIONAHA.119.13404. Epub 2020 Mar 16.
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.
年轻高血压患者的风险分层仍然具有挑战性。一般来说，机器学习（ML）被认为是传统临床预测方法的一种有前途的替代方法，因为它能够处理大量复杂的数据。因此，我们探索了 ML 方法在预测年轻高血压患者结局方面的可行性，并将其与目前临床实践中常用的方法进行了比较。在一家三级医院接受治疗的 508 名年轻高血压患者（30.83±6.17 岁）中评估了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、新发卒中和终末期肾病——构建 ML 模型包括递归特征消除、极端梯度增强和 10 倍交叉验证，在 33 个月的随访评估中进行，比较了模型的性能与 Cox 回归和重新校准的 Framingham 风险评分模型。使用 ML 方法，11 个变量组合被认为对预测结局最有价值。用于识别复合终点患者的 ML 模型的 C 统计量为 0.757（95%CI，0.660-0.854），而 Cox 回归模型和重新校准的 Framingham 风险评分模型的 C 统计量分别为 0.723（95%CI，0.636-0.810）和 0.529（95%CI，0.403-0.655）。ML 方法在确定年轻高血压患者的临床预后方面与 Cox 回归相当，优于重新校准的 Framingham 风险评分模型。
