Development and validation of an algorithm to identify severe sepsis onset from electronic medical records.

IF 4.7 Q2 HEALTH CARE SCIENCES & SERVICES

JAMIA Open Pub Date : 2026-07-18 eCollection Date: 2026-08-01 DOI:10.1093/jamiaopen/ooag129

Ramin Homayouni, Shane Morrell, Joel David Karsten, Riya Chhabra, Paul D Bozyk

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引用次数: 0

Abstract

Objective: To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t₀), from the electronic medical records (EMR).

Materials and methods: We developed an algorithm to construct a comprehensive timeline of systemic inflammatory response syndrome (SIRS) criteria and organ dysfunction (OD) using structured data, and documentation of infection (DOI) using both structured data and unstructured clinical notes. Algorithm performance was assessed using 2030 manually abstracted adult sepsis cases from a multicenter health system in southeast Michigan.

Results: On average, the algorithm DOI time was significantly earlier than abstractors (mean: -0.33 hour, 95% Cl, -0.55 to -0.11), resulting in a significantly earlier t₀ (mean: -0.60 hour, 95% CI, -0.74 to -0.45).

Discussion: Automated approaches to analyzing EMR data offer a scalable framework for SEP-1 monitoring, research, and quality improvement.

Conclusion: Incorporating unstructured clinical notes improves DOI detection.

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从电子医疗记录中识别严重败血症发作的算法的开发和验证。

目的：开发和评估一种从电子病历（EMR）中识别脓毒症发病的自动算法，称为零时间（t0）。材料和方法：我们开发了一种算法，使用结构化数据构建全身性炎症反应综合征（SIRS）标准和器官功能障碍（OD）的综合时间表，并使用结构化数据和非结构化临床记录构建感染记录（DOI）。使用密歇根州东南部多中心卫生系统中人工提取的2030例成人脓毒症病例评估算法性能。结果：平均而言，算法DOI时间明显早于抽象者（平均：-0.33小时，95% CI, -0.55 ~ -0.11），导致算法DOI时间明显早于抽象者（平均：-0.60小时，95% CI, -0.74 ~ -0.45）。讨论：分析EMR数据的自动化方法为SEP-1监控、研究和质量改进提供了可伸缩的框架。结论：纳入非结构化临床记录可提高DOI的检测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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