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在STANDFIRM随机对照试验中,在12个月的Framingham风险评分预测

Thanh G Phan^1,2, Velandai K Srikanth³, Dominique A Cadilhac²

¹Department of Neurology Monash Medical Centre Melbourne Australia.

Journal of the American Heart Association

|April 16, 2025

概括

在中风后的弗雷明汉风险评分 (FRS) 降低在很大程度上是在基线预先确定的. 机器学习可以准确地预测未来的FRS,但不能预测FRS的变化,这表明FRS变化作为试验终点的价值有限.

科学领域:

心血管医学心血管医学
神经学神经学
数据科学数据科学数据科学

背景情况:

在STANDFIRM试验中,研究了慢性疾病管理,以修改中风/TIA患者的弗雷明汉风险评分 (FRS).
主要结果,即FRS的变化,在试验中没有实现.
这项研究旨在确定FRS减少的基线预测因素,并评估未来FRS是否预先确定.

研究的目的:

确定基线特征,预测中风或过渡性缺血性发作后12个月的弗雷明汉风险评分 (FRS) 降低.
调查未来的FRS是否在基线预先确定.
评估机器学习模型在预测FRS及其变化的有效性.

主要方法:

使用机器学习回归方法 (随机森林,XGBoost,CatBoost,SVR,MLP,KNN) 来分析35个变量.
训练和测试的模型分别来自404名和103名患者的数据,根据年龄,性别和基线12个月的FRS相匹配.
采用沙普利增量解释 (SHAP) 来识别关键预测变量.

主要成果:

分类提升是预测12个月FRS的最佳模型 (R2=0.712).
FRS的主要预测因素包括基线FRS,年龄,静脉压,男性性别和伦敦残疾人得分.
机器学习模型在预测FRS变化方面表现不佳 (R2<0.22).

结论:

弗雷明汉风险评分 (FRS) 的变化可能在二次中风试验中作为终点具有有限的价值,因为它具有强大的基线确定性.
增强类别有效预测了未来的FRS,但在这个队列中没有FRS变化.
基线特征显著影响未来的心血管风险评估在中风后的患者.

关键词:

机器学习是机器学习.有关风险因素的风险因素.一次性中风中风中风中风中风过渡性缺血性攻击是短暂的

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