预测心力衰竭的死亡率和再住院:一种机器学习和对脆弱性和并发症的集群分析
Chukwuma Okoye1,2,3, Tessa Mazzarone4, Filippo Niccolai1
1Geriatrics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy.
Aging clinical and experimental research
|October 17, 2023
概括
机器学习根据脆弱性和并发性疾病确定了四种心力衰竭患者表型. 这些不同的群体预测死亡或再入院的风险在出院后的六个月内有所不同.
科学领域:
- 心脏病学 心脏病学
- 老年病的医生 老年病的医生
- 数据科学数据科学数据科学
背景情况:
- 老年心力衰竭 (HF) 患者面临高再入院和死亡风险.
- 现有的机器学习模型对于HF结果往往缺乏脆弱性和并发症评估.
- 这项研究解决了在预测HF结果时综合表型化的需求.
研究的目的:
- 为了识别基于机器学习的现象组,包括脆弱性和并发性疾病.
- 确定哪些现象组与心力衰竭患者在6个月内死亡或再入院的可能性最为相关.
- 增强针对老年HF患者的个性化护理模式.
主要方法:
- 一项前性单一中心研究包括571名因急性不补偿性心力衰竭 (ADHF) 出院的老年患者 (≥65岁).
- 随机森林和考克斯回归确定了不良结果的预测因素.
- 使用K-means和等级集群,根据脆弱性,并发症和BNP水平创建了四个不同的现象组.
主要成果:
- 集群分析揭示了四种不同的患者表型.
- 与参考集群相比,集群1 (非常脆弱/并发症) 显示不良结果的风险增加了3.53倍.
- 集群2 (脆弱前/低BNP) 的风险增加了2.59倍,而集群3 (脆弱前/并发性/高BNP) 的风险增加了3.75倍.
结论:
- 通过基于脆弱性,并发症和BNP的集群分析,确定了老年ADHF患者的四种不同的表型.
- 这些现象组显示出6个月的不良结果风险差异.
- 需要进一步的研究来验证这些表型,以进行个性化护理选择.
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