• Yiftach Barash, Shelly Soffer, Ehud Grossman, Noam Tau, Vera Sorin, Eyal BenDavid, Avinoah Irony, Eli Konen, Eyal Zimlichman, and Eyal Klang.
• Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Ramat Gan, Israel.
• Postgrad Med J. 2022 Mar 1; 98 (1157): 166-171.

ObjectivesPhysicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients.MethodsWe retrospectively analysed visits of adult patients discharged from a single ED (1/2014-12/2018). Data included demographics, evaluation and treatment in the ED, and discharge diagnosis. The data comprised of both structured and free-text fields. A gradient boosting model was trained to predict mortality within 30 days of release from the ED. The model was trained on data from the years 2014-2017 and validated on data from the year 2018. In order to reduce potential end-of-life bias, a subgroup analysis was performed for non-oncological patients.ResultsOverall, 363 635 ED visits of discharged patients were analysed. The 30-day mortality rate was 0.8%. A majority of the mortality cases (65.3%) had a known oncological disease. The model yielded an area under the curve (AUC) of 0.97 (95% CI 0.96 to 0.97) for predicting 30-day mortality. For a sensitivity of 84% (95% CI 0.81 to 0.86), this model had a false positive rate of 1:20. For patients without a known malignancy, the model yielded an AUC of 0.94 (95% CI 0.92 to 0.95).ConclusionsAlthough not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.

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