Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.

Abstract

Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. We developed random forest machine learning (ML) models to estimate needs for critical care within 24h and inpatient care within 72h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Among8032 patientswith laboratory-confirmed influenza,incidence of critical care needs was 6.3% and incidence of inpatient care needswas 19.6%.Themost common reasons for ED visitwere symptoms of respiratory tract infection,fever,and shortness of breath. Model AUCswere0.89 (95% CI 0.86-0.93)for prediction of criticalcare and0.90(95% CI0.88-0.93)for inpatient care needs;Brier scores were0.026 and0.042, respectively.Importantpredictorsincludedshortness of breath,increasingrespiratory rate,and ahigh number of comorbid diseases. ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.