Phan Thanh G, Srikanth Velandai K, Cadilhac Dominique A, Nelson Mark, Kim Joosup, Olaiya Muideen T, Fitzgerald Sharyn M, Bladin Christopher, Gerraty Richard, Ma Henry, Thrift Amanda G
Department of Neurology Monash Medical Centre Melbourne Australia.
Stroke and Aging Research Group, Department of Medicine School of Clinical Sciences at Monash Health, Monash University Melbourne Australia.
J Am Heart Assoc. 2025 May 20;14(10):e040254. doi: 10.1161/JAHA.124.040254. Epub 2025 Apr 16.
The STANDFIRM (Shared Team Approach Between Nurses and Doctors for Improved Risk Factor Management; ANZCTR registration ACTRN12608000166370) trial was designed to test the effectiveness of chronic disease care management for modifying the Framingham risk score (FRS) among patients with stroke or transient ischemic attack. The primary outcome of change in FRS was not met. We determine baseline characteristics that predict reduction in FRS at 12 months and whether future FRS is predetermined at baseline.
We used machine learning regression methods to evaluate 35 variables encompassing demographics, risk factors, psychological, social and education status, and laboratory tests. We determine the optimal machine learning and associated tuning parameters from the following: random forest, extreme gradient boosting, category boosting, support vector regression, multilayer perceptron neural network, and K-nearest neighbor. Training (n=404) and test (n=103) data were evenly matched for age, sex, baseline, and 12-month FRS. The optimal model for predicting FRS at 12 months was category boosting (=0.712; root mean square error, 7.32). The 5 variables with highest Shapley values for category boosting were baseline FRS (Shapley additive explanation [SHAP], 8.42 of total of 12.12), age (SHAP, 1.58), systolic blood pressure (SHAP, 0.23), male sex (SHAP, 1.05), and London Handicap (SHAP, 0.18). Machine learning methods were poor at determining change in FRS at 12 months (<0.22).
Our findings suggest that change in FRS as an end point in secondary stroke trials may have limited value as it is largely determined at baseline. In this cohort, category boosting was the optimal method to predict future FRS but not change in FRS.
“坚定立场”（护士与医生共同改善风险因素管理的共享团队方法；澳大利亚和新西兰临床试验注册中心注册号ACTRN12608000166370）试验旨在测试慢性病护理管理对改善中风或短暂性脑缺血发作患者的弗雷明汉风险评分（FRS）的有效性。FRS变化的主要结果未达到。我们确定了预测12个月时FRS降低的基线特征，以及未来FRS在基线时是否预先确定。
我们使用机器学习回归方法评估了35个变量，包括人口统计学、风险因素、心理、社会和教育状况以及实验室检查。我们从以下方法中确定了最佳机器学习方法和相关的调整参数：随机森林、极端梯度提升、类别提升、支持向量回归、多层感知器神经网络和K近邻。训练数据（n = 404）和测试数据（n = 103）在年龄、性别、基线和12个月时的FRS方面进行了均匀匹配。预测12个月时FRS的最佳模型是类别提升（= 0.712；均方根误差，7.32）。类别提升中Shapley值最高的5个变量是基线FRS（Shapley相加解释[SHAP]，占总计12.12的8.42）、年龄（SHAP，1.58）、收缩压（SHAP，0.23）、男性（SHAP，1.05）和伦敦残障评分（SHAP，0.18）。机器学习方法在确定12个月时FRS的变化方面表现不佳（< 0.22）。
我们的研究结果表明，FRS变化作为二级中风试验的终点可能价值有限，因为它在很大程度上由基线决定。在这个队列中，类别提升是预测未来FRS的最佳方法，但不是FRS变化的最佳方法。
