一种机器学习方法用于识别肺栓塞后面临长期后果风险的患者
Stephan Nopp1, Clemens Spielvogel2, Behnood Bikdeli3,4,5
1Clinical Division of Haematology and Haemostaseology, Department of Medicine I, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Scientific reports
|September 24, 2025
概括
机器学习模型在肺栓塞 (PE) 后预测慢性血栓栓塞性肺高血压 (CTEPH) 方面显示出前景,其表现优于现有的得分. 对于后PE综合征的预测准确性不那么有效,需要进一步改进.
科学领域:
- 心脏病学 心脏病学
- 肺部病理学 肺部病理学
- 数据科学数据科学数据科学
背景情况:
- 肺栓塞 (PE) 可能导致长期并发症,如持续性呼吸不良和慢性血栓栓塞性肺高血压 (CTEPH).
- 目前用于预测PE后严重结果的工具缺乏足够的准确性.
- 识别风险患者对于及时干预和改善患者管理至关重要.
研究的目的:
- 开发和验证用于预测CTEPH和后PE综合征的机器学习 (ML) 模型.
- 确定PE后长期后续的关键预测因素.
- 将ML模型的性能与现有的临床评分进行比较.
主要方法:
- 利用来自RIETE注册表的数据,这是一个大型的潜在国际PE注册表.
- 开发监督的ML模型,包括随机森林,使用111个预测变量.
- 使用蒙特卡洛交叉验证和基准CTEPH模型对现有评分进行验证的模型.
主要成果:
- 在CTEPH预测模型中,AUC达到0.74,明显超过现有得分 (AUC0.57).
- 后PE综合征模型的表现较差,AUC为0.62.2.
- 关键预测因素包括胸痛,PE位置,托波水平,凝块侧,以及出现时的呼吸障碍.
结论:
- 机器学习模型显示了在PE后预测CTEPH的潜力,比目前的方法提供了更高的准确性.
- 对于PE后综合征的预测性能需要提高,可能是通过整合像成像等额外数据.
- 需要进一步的研究和模型改进,以提高治疗PE长期后果的临床效用.
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