Alerting on mortality among patients discharged from the emergency department: a machine learning model. Issue 1157 (3rd December 2020)

• Record Type: Journal Article
• Authors: Barash, Yiftach; Soffer, Shelly; Grossman, Ehud; Tau, Noam; Sorin, Vera; BenDavid, Eyal; Irony, Avinoah; Konen, Eli; Zimlichman, Eyal; Klang, Eyal
• Is Part Of: Postgraduate medical journal. Volume 98:Issue 1157(2022)
• Page Start: 166
• Page End: 171
• Publication Date: 2020-12-03
• Subjects: accident & emergency medicine -- information technology; Medicine -- Periodicals; 610
• DOI: 10.1136/postgradmedj-2020-138899
• Languages: English
• ISSNs: 0032-5473

Abstract

Abstract: Objectives: Physicians 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. Methods: We 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. Results: Overall, 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). Conclusions: Although 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.