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

Abstract

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%–3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%–8.0%), and a 4% (95% CI, 1.1%–7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.

The potential immunological mechanisms of sepsis

“…The COMPOSER model was trained for the early identification of patients with a high risk of sepsis and abandoned the prediction of false predictions to improve accuracy (AUC = 0.925–0.953) ( 136 ). Recently this model was identified to be associated with an increase in the bundle compliance of sepsis ( 137 ).…”

Use of artificial intelligence in critical care: opportunities and obstacles

“…The data are messy and challenging, and creating models that can integrate, adapt, and analyze this type of data requires a deep understanding of the latest ML strategies and employ these strategies effectively. Presently, only few AI-based algorithms have shown evidence for improved clinician performance or patient outcomes in clinical studies [ 6 , 16 , 17 ]. Reasons proposed for this so-called AI chasm [ 18 ] are lack of necessary expertise needed for translating a tool into practice, lack of funding available for translation, underappreciation of clinical research as a translation mechanism, disregard for the potential value of the early stages of clinical evaluation and the analysis of human factors [ 19 ], and poor reporting and evaluations [ 2 , 8 , 20 ].…”

“…The rapid advances in AI, and in particular the release of publicly available generative AI applications leveraging advanced large language models, have greatly accelerated discussions considering the promises and pitfalls related to AI deployment in society and healthcare [ 7 – 17 ]. Heightened concerns about the development and deployment of AI have generated discussion about how to ensure that AI remains ‘aligned’ with human objectives and interests.…”