Sepsis Prediction Models are Trained on Labels that Diverge from Clinician-Recommended Treatment Times

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Clinical Decision Support, Critical Care, Internal Medicine or Medical Subspecialty, Infectious Diseases and Epidemiology
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Many sepsis prediction models use the Sepsis-3 definition or its variants as a training label. However, among the few sepsis models ever deployed in practice, there is scant evidence that they offer clinically meaningful decision support at the bedside. As a potential mechanism to explain this limitation, we hypothesized that clinician-recommended treatment times for sepsis would diverge from onset time defined by Sepsis-3. We conducted an electronic survey that was completed by 153 clinicians at three large and geographically diverse medical centers using vignettes derived from eight real cases of sepsis. After reviewing these vignettes, participants suggested antibiotic treatment to start an average of 7.0 hours (95% confidence interval 5.3 to 8.8) before the Sepsis-3 definition onset. Thus, predicting Sepsis-3 onset as a treatment prompt could lead to inappropriate and delayed treatment recommendations. Building predictive decision support systems that identify outcomes aligned with bedside decisions would increase their clinical utility.

Speaker(s):
GARY WEISSMAN, MD, MSHP
University of Pennsylvania

Author(s):
GARY WEISSMAN, MD, MSHP - University of Pennsylvania; Rebecca Hubbard, PhD - University of Pennsylvania; Blanca Himes, PhD - University of Pennsylvania; Kelly Goodman-O'Leary, MSN, CRNP - PAIR Center, University of Pennsylvania; Michael Harhay, PhD - University of Pennsylvania Perelman School of Medicine; Jennifer Ginestra, MD, MSHP - University of Pennsylvania Perelman School of Medicine; Rachel Kohn, MD, MSCE - University of Pennsylvania Perelman School of Medicine; Andrew Admon, MD, MPH - University of Michigan; Stephanie Parks Taylor, MD - Atrium Health; Scott Halpern, MD, PhD - University of Pennsylvania;