Sepsis Prediction for the General Ward Setting - Sean C. Yu; Sean C. Yu; Aditi Gupta; Kevin D. Betthauser; Patrick G. Lyons; Patrick G. Lyons; Albert M. Lai; Marin H. Kollef; Philip R. O. Payne; Andrew P. Michelson; Andrew P. Michelson (Frontiers Media S.A., 2022)
ObjectiveTo develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design.DesignRetrospective analysis of data extracted from electronic health records (EHR).SettingSingle, tertiary-care academic medical center in St. Louis, MO, USA.PatientsAdult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019.InterventionsNone.Measurements and Main ResultsOf the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating Characteristic curve (AUROC or c-statistic) of 0.862 ± 0.011 and Area Under the Precision-Recall Curve (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 h of crossing the alert threshold, 20.9% had a sepsis-related event occur.ConclusionsA machine learning model capable of predicting sepsis in the general ward setting was developed using the EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance and demonstrated a 29.1% Positive Predictive Value (PPV) for sepsis-related intervention or outcome within 48 h.