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比较基于行政病历与电子病历的方法识别败血症住院患者。

Comparison of Administrative versus Electronic Health Record-based Methods for Identifying Sepsis Hospitalizations.

机构信息

Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.

Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan; and.

出版信息

Ann Am Thorac Soc. 2023 Sep;20(9):1309-1315. doi: 10.1513/AnnalsATS.202302-105OC.

Abstract

Despite the importance of sepsis surveillance, no optimal approach for identifying sepsis hospitalizations exists. The Centers for Disease Control and Prevention Adult Sepsis Event Definition (CDC-ASE) is an electronic medical record-based algorithm that yields more stable estimates over time than diagnostic coding-based approaches but may still result in misclassification. We sought to assess three approaches to identifying sepsis hospitalizations, including a modified CDC-ASE. This cross-sectional study included patients in the Veterans Affairs Ann Arbor Healthcare System admitted via the emergency department (February 2021 to February 2022) with at least one episode of acute organ dysfunction within 48 hours of emergency department presentation. Patients were assessed for community-onset sepsis using three methods: ) explicit diagnosis codes, ) the CDC-ASE, and ) a modified CDC-ASE. The modified CDC-ASE required at least two systemic inflammatory response syndrome criteria instead of blood culture collection and had a more sensitive definition of respiratory dysfunction. Each method was compared with a reference standard of physician adjudication via medical record review. Patients were considered to have sepsis if they had at least one episode of acute organ dysfunction graded as "definitely" or "probably" infection related on physician review. Of 821 eligible hospitalizations, 449 were selected for physician review. Of these, 98 (21.8%) were classified as sepsis by medical record review, 103 (22.9%) by the CDC-ASE, 132 (29.4%) by the modified CDC-ASE, and 37 (8.2%) by diagnostic codes. Accuracy was similar across the three methods of interest (80.6% for the CDC-ASE, 79.6% for the modified CDC-ADE, and 84.2% for diagnostic codes), but sensitivity and specificity varied. The CDC-ASE algorithm had sensitivity of 58.2% (95% confidence interval [CI], 47.2-68.1%) and specificity of 86.9% (95% CI, 82.9-90.2%). The modified CDC-ASE algorithm had greater sensitivity (69.4% [95% CI, 59.3-78.3%]) but lower specificity (81.8% [95% CI, 77.3-85.7%]). Diagnostic codes had lower sensitivity (32.7% [95% CI, 23.5-42.9%]) but greater specificity (98.6% [95% CI, 96.7-99.55%]). There are several approaches to identifying sepsis hospitalizations for surveillance that have acceptable accuracy. These approaches yield varying sensitivity and specificity, so investigators should carefully consider the test characteristics of each method before determining an appropriate method for their intended use.

摘要

尽管脓毒症监测很重要,但目前尚无确定脓毒症住院的最佳方法。疾病预防控制中心成人脓毒症事件定义(CDC-ASE)是一种基于电子病历的算法,与基于诊断编码的方法相比,它可以提供更稳定的估计,但仍可能导致分类错误。我们旨在评估三种识别脓毒症住院的方法,包括改良的 CDC-ASE。这项横断面研究纳入了退伍军人事务部安阿伯医疗系统通过急诊科入院的患者(2021 年 2 月至 2022 年 2 月),这些患者在急诊科就诊后 48 小时内至少有一次急性器官功能障碍发作。使用三种方法评估患者的社区获得性脓毒症:)明确的诊断代码,)CDC-ASE,和)改良的 CDC-ASE。改良的 CDC-ASE 需要至少两个全身炎症反应综合征标准,而不是血液培养采集,并且对呼吸功能障碍有更敏感的定义。每种方法都与通过病历审查的医生裁决参考标准进行了比较。如果患者至少有一次急性器官功能障碍发作,且在医生审查中被评为“肯定”或“可能”与感染有关,则认为患者患有脓毒症。在 821 例符合条件的住院患者中,有 449 例被选择进行医生审查。在这些患者中,98 例(21.8%)经病历审查被归类为脓毒症,103 例(22.9%)经 CDC-ASE 归类,132 例(29.4%)经改良的 CDC-ASE 归类,37 例(8.2%)经诊断代码归类。三种感兴趣的方法的准确性相似(CDC-ASE 为 80.6%,改良的 CDC-ADE 为 79.6%,诊断代码为 84.2%),但敏感性和特异性不同。CDC-ASE 算法的敏感性为 58.2%(95%置信区间 [CI],47.2-68.1%),特异性为 86.9%(95% CI,82.9-90.2%)。改良的 CDC-ASE 算法的敏感性更高(69.4%[95%CI,59.3-78.3%]),但特异性较低(81.8%[95%CI,77.3-85.7%])。诊断代码的敏感性较低(32.7%[95%CI,23.5-42.9%]),但特异性较高(98.6%[95%CI,96.7-99.55%])。有几种用于监测的识别脓毒症住院的方法具有可接受的准确性。这些方法的敏感性和特异性各不相同,因此研究人员在确定适合其预期用途的方法之前,应仔细考虑每种方法的测试特征。

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