Machine Learning Without Borders? An Adaptable Tool to Optimize Mortality Prediction in Diverse Clinical Settings

Background Mortality prediction aids clinical decision-making and is necessary for quality improvement initiatives. Validated metrics rely on pre-specified variables and often require advanced diagnostics which are unfeasible in resource-constrained contexts. We hypothesize that machine learning will generate superior mortality prediction in both high-income and low and middle-income country cohorts. Methods SuperLearner(SL), an ensemble machine-learning algorithm, was applied to data from three prospective trauma cohorts: a highest-activation cohort in the United States(US), a high-volume center cohort in South Africa(SA), and a multicenter registry in Cameroon. Cross-validation was used to assess model discrimination of discharge mortality by site using receiver operating characteristic curves. SuperLearner discrimination was compared with standard scoring methods. Clinical variables driving SL prediction at each site were evaluated. Results Data from 28,212 injured patients were used to generate prediction. Discharge mortality was 17%, 1.3%, and 1.7% among US, SA, and Cameroonian cohorts. SL delivered superior prediction of discharge mortality in the US (AUC 94-97%) and vastly superior prediction in Cameroon (AUC 90-94%) compared to conventional scoring algorithms. It provided similar prediction to standard scores in the SA cohort (AUC 90-95%). Context-specific variables (partial thromboplastin time in the US and hospital distance in Cameroon) were prime drivers of predicted mortality in their respective cohorts, while severe brain injury predicted mortality across sites. Conclusions Machine learning provides excellent discrimination of injury mortality in diverse settings. Unlike traditional scores, data-adaptive methods are well-suited to optimizing precise site-specific prediction regardless of diagnostic capabilities or dataset inclusion allowing for individualized decision-making and expanded access to quality improvement programming. Level of Evidence Level III Study Type Prognostic and Therapeutic Corresponding author: Catherine Juillard MD MPH, 1001 Potrero Ave, 3A, San Francisco, CA 94110, Phone: 415-206-4622, Fax: (415) 206-5484, Email: CatherineJuillard@ucsf.edu Conflicts of interest: For all authors, no conflicts of interest declared. Meetings: Podium Presentation at 48th Annual Meeting of the Western Trauma Association, February 25-March 3, 2018 in Whistler, British Columbia. Funding: Supported by PCORI R-IMC-1306-02735 (MJC), NIH #K01ES026834 (RAC). © 2018 Lippincott Williams & Wilkins, Inc.

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