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Related Experiment Video

Updated: Feb 28, 2026

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis

Published on: December 9, 2022

Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model.

Andrew Wong¹, Danielle Currey^2,3, Megan Schwinne⁴

¹University of Michigan Medical School, Ann Arbor.

JAMA Network Open

|February 27, 2026

Summary

This summary is machine-generated.

The Epic Sepsis Model v2 shows improved early sepsis prediction across 4 health systems. However, it has high variability, low positive predictive value, and a high alert burden, impacting clinical adoption.

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Last Updated: Feb 28, 2026

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Published on: December 18, 2010

Area of Science:

Clinical Informatics
Critical Care Medicine
Health Services Research

Background:

The Epic Sepsis Model version 2 (ESM v2) is a widely used sepsis prediction tool.
External validation is crucial to guide its adoption and use in diverse clinical settings.

Purpose of the Study:

To perform a multicenter validation of the ESM v2.
To compare ESM v2 performance against the earlier ESM v1.
To assess ESM v2 performance relative to clinician recognition of sepsis.

Main Methods:

A prognostic study involving adult inpatient encounters across 4 large US health systems.
Data collected post-implementation of the new model, analyzed using Sepsis-3 criteria.
Model discrimination assessed via AUROC; performance compared to clinician recognition using order data.

Main Results:

Encounter-level AUROC for ESM v2 ranged from 0.82 to 0.92 across sites.
Prediction-level AUROC (12-hour horizon) ranged from 0.75 to 0.85.
ESM v2 showed slightly decreased performance compared to clinician recognition, with low positive predictive values (0.13-0.26).

Conclusions:

Multicenter validation of ESM v2 demonstrated improved early sepsis prediction discrimination.
Significant institutional variability, low positive predictive value, and high alert burden were observed.
Findings highlight the need for careful consideration of model performance and alert management in clinical practice.