Medicine and Health

Impact of a deep learning sepsis prediction model on quality of care and survival

A. Boussina, S. P. Shashikumar, et al.

This study explored the transformative effects of the deep-learning model COMPOSER on sepsis outcomes in emergency departments. The research shows significant improvements in sepsis mortality and care compliance, demonstrating a promising advancement in sepsis management conducted by Aaron Boussina and colleagues at the University of California San Diego.

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Abstract

This before-and-after quasi-experimental study assessed the impact of a deep-learning model (COMPOSER) on sepsis patient outcomes in two emergency departments. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction in in-hospital sepsis mortality (17% relative decrease), a 5.0% absolute increase in sepsis bundle compliance (10% relative increase), and a 4% reduction in 72-h SOFA change. These findings suggest that COMPOSER's deployment is associated with improved sepsis care and survival.

Publisher

npj Digital Medicine

Published On

Jan 23, 2024

Authors

Aaron Boussina, Supreeth P. Shashikumar, Atul Malhotra, Robert L. Owens, Robert El-Kareh, Christopher A. Longhurst, Kimberly Quintero, Allison Donahue, Theodore C. Chan, Shamim Nemati, Gabriel Wardi

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