Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Abstract

Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen identification, resistance testing, and effective antibiotic and supportive treatment, and thereby become a life-saving measure. Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU. Our analysis represents the largest multi-national, multi-centre in-ICU study for sepsis prediction using ML to date. Our dataset contains 156, 309 unique ICU admissions, which represent a refined and harmonised subset of five large ICU databases originating from three countries. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis label annotations, amounting to 26, 734 (17.1%) septic stays. We compared our approach, a deep self-attention model, to several clinical baselines as well as ML baselines and performed an extensive internal and external validation within and across databases. On average, our model was able to predict sepsis with an AUROC of 0.847 ± 0.050 (internal out-of sample validation) and 0.761 ± 0.052 (external validation). For a harmonised prevalence of 17%, at 80% recall our model detects septic patients with 39% precision 3.7 h in advance.

AI Gone Astray: Technical Supplement

“…Sepsis Prediction Task Sepsis is a potentially life-threatening condition where a body's reaction to an infection triggers damage in organ systems. Following recent work [2,3], we predict the onset of Sepsis-3 within 6 hours given 24 hours of ICU data. Sepsis-3 defines sepsis onset as an increase in SOFA-score of >= 2 points within a window of 48 hours before and 24 hours after a Suspicion of Infection, which occurs when there are concomitant orders of antibiotics and microbiological samples taken.…”