In Journal of burn care & research : official publication of the American Burn Association
Reports of single center experience and studies of larger databases have identified several predictors of burn center mortality, including age, burn size, and inhalation injury. None of these analyses has been broad enough to allow benchmarking across burn centers. The purpose of this study was to derive a reliable, risk-adjusted, statistical model of mortality based on real-life experience at many burn centers in the United States. We used the American Burn Association 2020 Full Burn Research Dataset, from the Burn Center Quality Platform (BCQP) to identify 130,729 subjects from July 2015 through June 2020 across 103 unique burn centers. We selected 22 predictor variables, from over 50 recorded in the dataset, based on completeness (at least 75% complete required) and clinical significance. We used gradient boosted regression, a form of machine learning, to predict mortality and compared this to traditional logistic regression. Model performance was evaluated with AUC and PR curves. The CatBoost model achieved a test AUC of 0.980 with an average precision of 0.800. The logistic regression produced an AUC of 0.951 with an average precision of 0.664. While AUC, the measure most reported in the literature, is high for both models, the CatBoost model is markedly more sensitive, leading to a substantial improvement in precision. Using BCQP data, we can predict burn mortality allowing comparison across burn centers participating in BCQP.
Mandell Samuel P, Phillips Matthew H, Higginson Sara, Hoarle Kimberly, Hsu Naiwei, Phillips Bart, Thompson Callie, Weber Joan M, Weichmann-Murata Erica, Bessey P Q
BCQP, Mortality, benchmarking, quality improvement