Cooley-Rieders Keaton, Zheng Kai
School of Medicine, University of California Irvine, 1001 Health Sciences Road, Irvine, CA, 92617, USA.
Department of Informatics, University of California Irvine, 6095 Donald Bren Hall, Irvine, CA, 92687, USA.
Intell Based Med. 2021;5. doi: 10.1016/j.ibmed.2021.100028. Epub 2021 Mar 10.
BACKGROUND/OBJECTIVE: Sepsis remains without good outcome prediction. Technological advances, specifically, natural language processing (NLP), has an opportunity to approach sepsis mortality prediction in a novel way.
Using the MIMIC III dataset, patients diagnosed with sepsis from 2008 to 2013 had physician progress notes analyzed using NLP. Researchers utilized concepts from analysis to build a model to predict for in-hospital-mortality, using notes in the first 24 hours of a patient admission. This model was retrospectively validated on septic admissions to University of California Irvine Medical Center (UCIMC) from 2013 to 2018 and compared to SOFA and qSOFA.
An 80-concept model was developed and validated on 7117 admissions to UCIMC. For severe sepsis, an Area Under Curve or AUC of 0.687 (95% CI 0.618-0.748) was demonstrated which was greater than SOFA at 0.571 (0.497-0.643). Additionally, for simple sepsis the model demonstrated an AUC of 0.696 (0.649-0.738) which was greater than qSOFA at 0.590 (0.545-0.638).
Physician clinical judgement extracted from notes using NLP has greater performance in predicting mortality and survival in sepsis compared to structured data used in SOFA and qSOFA.
背景/目的：脓毒症的预后预测效果仍不理想。技术进步，特别是自然语言处理（NLP），为以全新方式进行脓毒症死亡率预测提供了契机。
利用多中心重症医学信息数据库（MIMIC III）数据集，对2008年至2013年诊断为脓毒症的患者，使用NLP分析医生的病程记录。研究人员利用分析得出的概念构建模型，以预测患者入院后24小时内的院内死亡率。该模型在2013年至2018年加州大学欧文分校医学中心（UCIMC）的脓毒症入院病例中进行回顾性验证，并与序贯器官衰竭评估（SOFA）和快速序贯器官衰竭评估（qSOFA）进行比较。
开发了一个包含80个概念的模型，并在UCIMC的7117例入院病例中进行了验证。对于严重脓毒症，曲线下面积（AUC）为0.687（95%置信区间0.618 - 0.748），高于SOFA的0.571（0.497 - 0.643）。此外，对于单纯脓毒症，该模型的AUC为0.696（0.649 - 0.738），高于qSOFA的0.590（0.545 - 0.638）。
与SOFA和qSOFA中使用的结构化数据相比，通过NLP从病程记录中提取的医生临床判断在预测脓毒症死亡率和生存率方面表现更优。
