休克病因学的临床间诊断协议:一个多中心观察性研究
Lauren M Janczewski1, Carolyn J Hu1, Tiannan Zhan1
1Department of Surgery, Feinberg School of Medicine, Northwestern University, 676 N. St. Clair, Suite 650, Chicago, IL 60611 USA.
Health information science and systems
|January 15, 2026
概括
在18.2%的患者中,缺乏关于冲击病因的临床间协议. 机器学习模型可以在没有完全诊断协议的情况下预测患者,有助于更好的患者护理和结果.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 临床决策 临床决策
背景情况:
- 临床间诊断协议对于在重症监护室 (ICU) 有效的患者管理至关重要.
- 在诊断休克病因方面存在分歧,可能导致治疗效率低于最佳,患者的治疗结果更差.
- 量化和预测诊断异议对于改善护理质量至关重要.
研究的目的:
- 量化临床医生之间关于休克病因学的诊断分歧的程度.
- 开发和评估机器学习模型,用于预测缺乏对冲击病因学完全临床间诊断协议的患者.
主要方法:
- 一项多中心的回顾性队列研究,对7302名在2018-2023年期间在ICU中被诊断患有震荡病的患者进行了多中心回顾性队列研究.
- 冲击病因被表示为从临床医生的笔记中提取的9维向量,使用正则表达式.
- 临床医生间的一致性是使用共弦相似度得分来计算的;机器学习模型预测得分小于1的病例.
主要成果:
- 18.2%的患者 (1327/7302) 缺乏完全的临床间诊断协议.
- 没有协议的患者更有可能是黑人,具有更高的并发病率和SOFA得分,并经历更高的住院死亡率.
- 后勤回归,随机森林和梯度增强模型准确地预测了没有诊断协议的患者 (AUC-ROC > 0.92).
结论:
- 经常缺乏对冲击病因学的完整的临床间诊断协议.
- 机器学习模型在没有诊断协议的情况下,在预测患者方面表现出很高的准确性.
- 这些预测模型为识别由于诊断不确定性而处于风险的患者提供了有价值的工具.
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