Early Physician Gestalt Versus Usual Screening Tools for the Prediction of Sepsis in Critically Ill Emergency Patients - 26/03/24

Doi : 10.1016/j.annemergmed.2024.02.009

Sarah K.S. Knack, MD ^a, Nathaniel Scott, MD, MHA ^a, Brian E. Driver, MD ^a, Matthew E. Prekker, MD; MPH ^a, Lauren Page Black, MD ^b,^c, Charlotte Hopson, MS ^d, Ellen Maruggi, BS ^a, Olivia Kaus, BS ^a, Walker Tordsen, BS ^a, Michael A. Puskarich, MD, MS ^a,^e,^⁎
^a Hennepin Healthcare, Minneapolis, MN
^b University of Florida, College of Medicine, Jacksonville, FL
^c Northwestern University, Feinberg School of Medicine, Chicago, IL
^d University of Florida, Gainesville, FL
^e University of Minnesota, Minneapolis, MN

^∗Corresponding Author.

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Tuesday 26 March 2024

Abstract

Study objective

Compare physician gestalt to existing screening tools for identifying sepsis in the initial minutes of presentation when time-sensitive treatments must be initiated.

Methods

This prospective observational study conducted with consecutive encounter sampling took place in the emergency department (ED) of an academic, urban, safety net hospital between September 2020 and May 2022. The study population included ED patients who were critically ill, excluding traumas, transfers, and self-evident diagnoses. Emergency physician gestalt was measured using a visual analog scale (VAS) from 0 to 100 at 15 and 60 minutes after patient arrival. The primary outcome was an explicit sepsis hospital discharge diagnosis. Clinical data were recorded for up to 3 hours to compare Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), Modified Early Warning Score (MEWS), and a logistic regression machine learning model using Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection. The screening tools were compared using receiver operating characteristic analysis and area under the curve calculation (AUC).

Results

A total of 2,484 patient-physician encounters involving 59 attending physicians were analyzed. Two hundred seventy-five patients (11%) received an explicit sepsis discharge diagnosis. When limited to available data at 15 minutes, initial VAS (AUC 0.90; 95% confidence interval [CI] 0.88, 0.92) outperformed all tools including LASSO (0.84; 95% CI 0.82 to 0.87), qSOFA (0.67; 95% CI 0.64 to 0.71), SIRS (0.67; 95% 0.64 to 0.70), SOFA (0.67; 95% CI 0.63 to 0.70), and MEWS (0.66; 95% CI 0.64 to 0.69). Expanding to data available at 60 minutes did not meaningfully change results.

Conclusion

Among adults presenting to an ED with an undifferentiated critical illness, physician gestalt in the first 15 minutes of the encounter outperformed other screening methods in identifying sepsis.

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Plan

Introduction

Materials and Methods

	Please see page XX for the Editor’s Capsule Summary of this article.
	Supervising editor: Alan E. Jones, MD. Specific detailed information about possible conflict of interest for individual editors is available at editors.
	Author contributions: MAP, BD, NS, MEP conceived and designed the study. MAP, NS, and BD supervised the conduct of the trial and data collection. EM, OK, and WT undertook data collection form production, data entry, and management including quality control. LB and CH provided statistical assistance with the machine learning model portion of the project. SK took the lead in data cleaning and statistical analysis. SK, MAP, and NS drafted the manuscript, and all authors contributed substantially to its revision. SK and MAP take responsibility for the paper as a whole.
	Data sharing statement: Partial or complete data sets and data dictionaries are available from one year after publication upon request to Dr. Puskarich at mike.puskarich@hcmed.org to investigators who provide an IRB letter of approval.
	All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
	Funding and support: By Annals' policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist.
	Presentation information: Data presented at SAEM annual conference, Austin, TX, May 18, 2023.