Sepsis Prediction for the General Ward Setting - Yu, Sean C.; Gupta, Aditi; Betthauser, Kevin D.; Lyons, Patrick G.; Lai, Albert M.; Kollef, Marin H.; Payne, Philip R. O.; Michelson, Andrew P. (Frontiers in Digital Health, 2022)
Objective: Develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design. Design: Retrospective analysis of data extracted from electronic health record (EHR). Setting: Single, tertiary-care academic medical center in St. Louis, MO, USA. Patients: Adult, non-surgical inpatients admitted between 1/1/2012 and 6/1/2019. Interventions: None. Measurements and Main Results: Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from EHR data and used to develop a machine learning model to predict sepsis 6-hours ahead of onset. The best performing model had an AUROC (c-statistic) of 0.862 ± 0.011 and AUPRC of 0.294 ± 0.021 compared to that of Logistic Regression (0.857 ± 0.008 and 0.256 ± 0.024) and NEWS 2 (0.699 ± 0.012 and 0.092 ± 0.009). In the pseudo-prospective trial, 388 (69.7%) septic patients were alerted on with a specificity of 81.4%. Within 24 hours of crossing the alert threshold, 20.9% had a sepsis-related event occur. Conclusions: A machine learning model capable of predicting sepsis in the general ward setting was developed using EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance, and revealed that the model was able to recognize critical illness 52.6 hours ahead of practitioners.