在心力衰竭患者中对螺旋龙的治疗反应:基于机器学习的分析,对两个随机对照试验进行了分析
Karl-Patrik Kresoja1, Matthias Unterhuber1, Rolf Wachter2
1Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany.
EBioMedicine
|September 9, 2023
概括
机器学习确定了心力衰竭与保存喷射分数 (HFpEF) 患者,他们受益于螺旋. 这种方法可以帮助个性化使用螺旋乳治疗HFpEF,以获得更好的结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 药物基因组学 药物基因组学
背景情况:
- 由于对螺旋龙的反应不清楚,心力衰竭与保存的喷射分数 (HFpEF) 构成了挑战.
- 识别受益于螺旋乳的患者子集对于有效的HFpEF管理至关重要.
研究的目的:
- 应用机器学习来识别HFpEF患者中对螺旋乳的响应和不响应者.
- 在单独的临床试验队列中验证预测模型.
主要方法:
- 在Aldo-DHF队列中使用了重复的集群分配换方法来识别响应.
- 采用极端梯度增强 (XGBoost) 算法来描述响应特征并预测TOPCAT队列中的结果.
- 将主要终点定义为心血管死亡率,心脏骤停或心力衰竭住院治疗的复合结果.
主要成果:
- 在衍生队列中,38%的患者被确定为螺旋乳反应者,显示出显著的E/e'改善.
- 在验证队列中,螺旋乳显著降低了响应者 (p=0.008) 的初级结局,但不是非响应者 (p=0.52).
结论:
- 机器学习模型可以有效地识别HFpEF患者,他们可能对螺旋龙有好反应.
- 这种方法有可能在HFpEF中提供个性化治疗策略.
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