对心力衰竭临床结果的并发症模式的影响:基于机器学习的集群分析
Bingxin Liu1, Yimei Zhong1, Xuan Yin1
1Department of Public Administration, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China.
The American journal of cardiology
|September 30, 2025
概括
确定心力衰竭 (HF) 的不同患者群体及其独特的健康状况至关重要. 这项研究发现,特定的并发症集群对HF患者的30天再接收风险产生重大影响.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 心力衰竭 (HF) 是一个重大的全球健康挑战.
- 在HF患者中,复杂的并发症模式可能会对临床结果产生负面影响,并使管理复杂化.
- 了解并发症异质性对于改善患者护理至关重要.
研究的目的:
- 根据他们的并发症概况,识别出不同的心力衰竭患者群.
- 评估这些已识别的集群与短期临床结果之间的关联,特别是30天再入院率.
- 为了评估机器学习模型在分层HF患者风险中的预测性能.
主要方法:
- 分析了来自中国1,010,573名心力衰竭患者 (2021-2024) 的电子健康记录.
- 应用聚类大型应用 (CLARA) 算法将患者分为5个不同的并发症集群.
- 利用XGBoost和其他机器学习模型进行预测性绩效评估,评估年龄,查尔森共同发病率指数和集群衍生特征.
主要成果:
- 在心力衰竭患者中发现了五种不同的并发症集群.
- 携带并发症负担最高的第5个群体显示,30天再入院的风险增加 (aOR:1.29).
- 与参考组相比,集群2和3呈现出较低的30天再接收风险. XGBoost模型实现了0.76的AUC和0.17的布莱尔得分.
结论:
- 在心力衰竭患者群体中存在实质性的异质性,这是由并发症模式驱动的.
- 基于并发症的聚类为心力衰竭的短期临床结果提供了宝贵的见解.
- 这种方法有可能提高风险分层,并为心力衰竭患者个性化护理策略.
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