预测阴道切除治疗心房/心肌治疗后的不良结果
Juan C Quiroz1, David Brieger2, Louisa R Jorm1
1Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.
Heart, lung & circulation
|February 16, 2024
概括
机器学习模型可以预测心房动 (AF) 和心房动 (AFL) 导管切除后的不良结果. 模型准确地预测了复合结果,但在重大出血事件方面遇到了困难.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 导管切除是对心房动 (AF) 和心房动 (AFL) 的常见治疗方法.
- 预测切除后的不良结果对于患者管理至关重要.
- 现有的预后模型可能无法完全捕捉复杂的患者风险.
研究的目的:
- 开发和评估预后生存模型,以预测非膜AF和/或AFL的导管切除后的不良结果.
- 为了进行风险预测,比较传统和深度学习的生存模型.
- 为了确定导管切除后不良事件的关键预测因素.
主要方法:
- 利用澳大利亚新南威尔士州医院行政数据,处方要求,急诊室访问和死亡登记的链接数据集.
- 开发了传统和深度生存模型,以预测严重出血和复合结果 (心力衰竭,中风,心脏骤停,死亡).
- 评估模型性能使用对应指数进行风险歧视.
主要成果:
- 在3285名患者中,复合结局发生在5.3%的患者中;严重出血发生在5.1%的患者中.
- 预测复合结果的模型显示出高准确度 (一致性指数>0.79).
- 大型出血模型显示差异差 (一致性指数<0.66);关键预测因素包括并发症,年龄较大和心力衰竭疗法.
结论:
- 综合结局的预后模型显示,在切除后识别高风险患者方面具有前景.
- 目前的数据不足以准确预测主要出血事件.
- 机器学习模型可能有助于临床医生积极管理为AF和AFL进行切除的高风险患者.
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