在使用机器学习的肺栓塞患者队列中预测急性和长期死亡率
Wahbi K El-Bouri1, Alexander Sanders2, Gregory Y H Lip1
1Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK; Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
European journal of internal medicine
|July 24, 2023
概括
与传统方法相比,新的机器学习得分可以提高肺栓塞 (PE) 患者的死亡率预测. 这种人工智能驱动的工具提供了增强的风险分层,以便在PE管理中获得更好的患者结果.
科学领域:
- 心脏病学和肺部医学.
- 医疗信息学和机器学习
- 生物统计学和风险预测建模
背景情况:
- 肺栓塞 (PE) 是一种与高死亡率和发病率相关的严重疾病.
- 现有的临床风险评分旨在识别30天死亡风险高的患者.
- 需要改善PE患者的风险分层工具.
研究的目的:
- 开发和验证基于机器学习的风险评分,用于预测PE患者的30天,90天和365天死亡率.
- 将机器学习得分的性能与已建立的临床风险指数 (如PESI和sPESI) 进行比较.
主要方法:
- 利用了来自伯明翰和黑国静脉血栓栓塞 (BBC-VTE) 注册表的数据,其中包括2183名患者.
- 在70%的训练队列上训练随机森林模型,并在30%的持有集上进行验证.
- 使用AUC比较机器学习预测与肺栓塞严重性指数 (PESI) 和简化肺栓塞严重性指数 (sPESI),并用Shapley值分析预测因素的重要性.
主要成果:
- 机器学习得分实现了30天死亡率的AUC为0.71,超过了sPESI (AUC0.65) 和PESI (AUC0.64).
- 该模型显示了90天 (AUC 0.74) 和365天 (AUC 0.73) 死亡率的强大预测能力.
- 短期死亡率的关键预测因素包括中性粒细胞计数,白细胞计数,C反应蛋白和血红蛋白水平,年龄在所有时间点都是一个一致的预测因素.
结论:
- 与当前标准的临床工具相比,机器学习算法为PE患者提供了优越的风险分层.
- 开发的ML得分显示了提高PE死亡率预测的前景.
- 在临床实施这种新型风险评分之前,建议进行外部验证.
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