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治疗严重传染性疾病的皮质类固醇:通过预测器官功能障碍轨迹分层的多中心向试验仿真

Suraj Rajendran¹, Zhenxing Xu², Weishen Pan²

¹Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA.

medRxiv : the preprint server for health sciences

|March 18, 2024

概括

皮质类固醇可能会伤害快速改善的感染性危急疾病患者,但对快速恶化的患者有益或无害. 机器学习识别了这些不同的患者群体,改进了治疗策略.

科学领域:

关键护理医学关键护理医学
药理学药理学是指药理学的学科.
在医疗保健中的数据科学.

背景情况:

皮质类固醇用于传染性严重疾病,但其确切的风险益处概况尚不清楚.
现有的研究缺乏细粒度的患者分层,可能会掩盖治疗效果.

研究的目的:

评估皮质类固醇使用与传染性严重疾病中的死亡率之间的关联.
根据器官功能障碍的轨迹,使用机器学习来确定患者的亚现象类型,以完善风险效益评估.

主要方法:

追溯性,多中心研究模拟目标试验框架.
使用预测机器学习 (ML) 来分类器官功能障碍轨迹的新型分层.
对28天死亡率和次要结果的发展 (eICU,MIMIC-IV) 和验证 (CEdAR) 队列的分析.

主要成果:

ML确定了四种子类型;分析了两个:"快速改善" (RI) 和"快速恶化" (RW).
RW患者从皮质类固醇中获得好处或没有伤害 (例如,发育队列HR 1.05对于死亡率).
RI患者似乎从皮质类固醇中获得伤害 (例如,发育队列HR 1.40对死亡率),二次结果更差.

结论:

皮质类固醇在传染性严重疾病中的有效性高度依赖于患者的发展轨迹,根据ML亚表型识别.
根据经验观察到的病理生物学来定制治疗策略,如RI与RW,对于优化皮质类固醇效用至关重要.
这些发现需要在观察性研究和临床试验中进行精细分层,以考虑不同的治疗反应.

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