使用不断变化的临床数据和胸部X射线图分析的ARDS死亡率预测模型
Ana Cysneiros1,2, Tiago Galvão3, Nuno Domingues3
1Nova Medical School, Universidade de Lisboa, 1649-004 Lisbon, Portugal.
Biomedicines
|February 24, 2024
概括
预测COVID-19相关ARDS (C-ARDS) 的死亡率是可能的,使用机器学习与临床数据和胸部X射线. 这种方法有助于量身定制治疗,改善患者的治疗结果.
科学领域:
- 肺部和重症监护医学 肺部和重症监护医学
- 传染性疾病 传染性疾病
- 医学成像分析 医学成像分析
背景情况:
- 与COVID-19相关的ARDS (C-ARDS) 在2019年底出现,需要对其病理生理学的研究.
- 鉴定C-ARDS的表型对于理解其异质性和改善患者管理至关重要.
- SARS-CoV-2 显著影响了 ARDS 病例,突出了对特定预测模型的需求.
研究的目的:
- 开发和验证C-ARDS死亡率的预测模型.
- 调查机器学习在分析胸部X射线 (CXR) 功能中对死亡率预测的实用性.
- 评估临床变量和成像数据的联合预测能力.
主要方法:
- 110名C-ARDS患者的回顾性分析 (2020年4月至2021年2月).
- 评估通风设置,动脉血液气体 (PaO2 / FiO2比率) 和第一和第三天的CXR.
- 使用卷积神经网络 (CNN) 进行CXR图像分析.
- 开发了一个二进制后勤回归模型,包括年龄,P/F比率和CNN提取的CXR特征.
主要成果:
- 该研究包括110名C-ARDS患者,平均年龄为63.2岁,61.2%是男性.
- 严重的ARDS发生在25%的患者中,整体死亡率为47.3%.
- 预测模型在测试数据上实现了接收器运行特征 (ROC) 曲线下的面积为0.862.
结论:
- 结合不断变化的P/F比率和CXR数据,可以预测重症监护室中C-ARDS死亡率.
- 应用于成像的机器学习可以揭示ARDS表型化隐藏的模式.
- 结合临床和机器学习成像功能,与单个组件相比,可以提供更好的死亡率预测.
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