A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications. OBJECTIVE: Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED. DESIGN: An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality. KEY RESULTS: Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality. CONCLUSION: The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

Promoting head CT exams in the emergency department triage using a machine learning model.

PURPOSE: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage. METHODS: We collected data of all adult ED visits in our institution for five consecutive years (1/2013-12/2017). Retrieved variables included the following: demographics, mode of arrival to the ED, comorbidities, home medications, structured and unstructured chief complaints, vital signs, pain scale score, emergency severity index, ED wing assignment, documentation of previous ED visits, hospitalizations and CTs, and current visit non-contrast head CT usage. A machine learning gradient boosting model was trained on data from the years 2013-2016 and tested on data from 2017. Area under the curve (AUC) was used as metrics. Single-variable AUCs were also determined. Youden's index evaluated optimal sensitivity and specificity of the models. RESULTS: The final cohort included 595,561 ED visits. Non-contrast head CT usage rate was 11.8%. Each visit was coded into an input vector of 171 variables. Single-variable analysis showed that chief complaint had the best single predictive analysis (AUC = 0.87). The best model showed an AUC of 0.93 (95% CI 0.931-0.936) for predicting non-contrast head CT usage at triage level. The model had a sensitivity of 88.1% and specificity of 85.7% for non-contrast head CT utilization. CONCLUSION: The developed model can identify patients that need to undergo head CT exam already in the ED triage level and by that allow faster diagnosis and treatment.