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WEDNESDAY, Jan. 31, 2024 (HealthDay News) — Deployment of COMPOSER for early prediction of sepsis is associated with significant improvements in outcomes, including mortality, according to a study published online Jan. 23 in npj Digital Medicine.

Aaron Boussina, from University of California in San Diego (UCSD), and colleagues assessed the impact of a deep learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. The analysis included a before-and-after quasi-experimental study design at two emergency departments that saw 6,217 adult septic patients from Jan. 1, 2021, through April 30, 2023.

The researchers found that the deployment of COMPOSER was significantly associated with a 1.9 percent absolute reduction (17 percent relative decrease) in in-hospital sepsis mortality. Additionally, there was a 5.0 percent absolute increase (10 percent relative increase) in sepsis bundle compliance and a 4 percent reduction in 72-hour sequential organ failure assessment score change after sepsis onset in a causal inference analysis.

“Our COMPOSER model uses real-time data in order to predict sepsis before obvious clinical manifestations,” coauthor Gabriel Wardi, M.D., also from UCSD, said in a statement. “It works silently and safely behind the scenes, continuously surveilling every patient for signs of possible sepsis.”

Several authors are co-founders of Healcisio Inc., a UCSD start-up focused on commercialization of advanced analytical decision support tools.

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