在肺高血压死亡风险分层中聚类的实用性
Pasquale Tondo1,2, Lucia Tricarico3, Giuseppe Galgano4
1Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy.
Bioengineering (Basel, Switzerland)
|April 26, 2025
概括
机器学习确定了三个肺高血压 (PH) 患者群和死亡风险的预测模型. 这种方法可以优化治疗策略,以获得更好的患者结果.
科学领域:
- 心脏病学 心脏病学
- 肺部病理学 肺部病理学
- 生物医学信息学 生物医学信息学
背景情况:
- 肺高血压 (PH) 由于预后不佳而构成重大临床挑战,需要改进管理策略.
- 表型特征和准确的死亡率预测对于优化PH患者护理至关重要.
研究的目的:
- 用聚类技术识别肺高血压患者的不同表型.
- 开发和验证基于机器学习 (ML) 的预测模型,用于PH患者的五年死亡率.
主要方法:
- 一项涉及122名PH患者的多中心研究.
- 分析了临床和人口统计数据,使用聚类来识别表型.
- 用各种ML算法来构建死亡率的预测模型.
主要成果:
- 确定了三个不同的PH患者群,分别是年龄,性别,PH组,心脏功能 (NYHA类) 和呼吸功能 (FEV1%).
- 集群2表现出最差的呼吸功能,中间心脏功能,和明显更高的死亡率 (75%).
- 后勤回归证明了最好的预测性能 (AUC = 0.835),确定年龄,NYHA类和药物计数作为关键死亡率预测因素.
结论:
- 机器学习有助于在肺高血压中有效地分层风险.
- 将ML整合到临床实践中可以带来优化治疗策略和改善患者的治疗结果.
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