Benefits include significant reduction in mortality and significant increase in sepsis bundle compliance<\/p>\n<\/h3>\n
By Lori Solomon HealthDay Reporter<\/b><\/p>\n
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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\u00a0npj Digital Medicine<\/em>.<\/p>\n 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.<\/p>\n 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.<\/p>\n “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.”<\/p>\n Several authors are co-founders of Healcisio Inc., a UCSD start-up focused on commercialization of advanced analytical decision support tools.<\/p>\n