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Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning

Gabriel Wardi¹, Morgan Carlile², Andre Holder³

¹Department of Emergency Medicine, University of California-San Diego, San Diego, CA; Division of Pulmonary, Critical Care, and Sleep Medicine, University of California-San Diego, San Diego, CA.

Annals of Emergency Medicine

|January 18, 2021

Summary

This summary is machine-generated.

Machine learning accurately predicts sepsis in the emergency department. Transfer learning enhances algorithm generalizability, improving sepsis prediction across different clinical sites.

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Area of Science:

Artificial Intelligence in Medicine
Machine Learning for Clinical Prediction
Sepsis Syndrome Research

Background:

Machine-learning algorithms offer enhanced prediction of sepsis syndromes in the emergency department (ED) using electronic medical record data.
Transfer learning, a machine learning subfield, enables algorithm generalizability across diverse clinical settings.
Accurate and timely sepsis prediction is crucial for improving patient outcomes in the ED.

Purpose of the Study:

To validate the Artificial Intelligence Sepsis Expert (AISE) algorithm for predicting delayed septic shock in an ED patient cohort.
To demonstrate the feasibility of transfer learning to enhance the external validity of the AISE algorithm at a second clinical site.
To assess the generalizability of a machine learning model for sepsis prediction across different healthcare institutions.

Main Methods:

An observational cohort study involved over 180,000 patients from two academic medical centers (2014-2019).
The AISE algorithm was trained using 40 input variables to predict delayed septic shock (onset >4 hours post-ED triage).
Transfer learning was applied to validate the algorithm's generalizability at a second site.

Main Results:

The AISE algorithm demonstrated excellent predictive performance (Area Under the ROC Curve >0.8) at 8 and 12-hour prediction windows for delayed septic shock.
Out of 9,354 patients with severe sepsis, 723 developed septic shock more than 4 hours after triage.
Transfer learning significantly improved the algorithm's test characteristics and achieved comparable performance at the validation site.

Conclusions:

The AISE algorithm accurately predicts the development of delayed septic shock.
Transfer learning significantly enhances the external validity and generalizability of the AISE algorithm across different clinical sites.
Prospective studies are warranted to evaluate the clinical utility of this predictive model in real-world settings.