• Mark Daley, Saoirse Cameron, GanesanSaptharishi LalgudiSLPediatrics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON N6A 3K7, Canada. Electronic address: rishi.ganesan@lhsc.on.ca., Maitray A Patel, Tanya Charyk Stewart, Michael R Miller, Ibrahim Alharfi, and Douglas D Fraser.
• Computer Science, Western University, London, ON N6A 3K7, Canada; The Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada. Electronic address: mdaley2@uwo.ca.
• Injury. 2022 Mar 1; 53 (3): 992-998.

IntroductionSevere traumatic brain injury (sTBI) is a leading cause of mortality in children. As clinical prognostication is important in guiding optimal care and decision making, our goal was to create a highly discriminative sTBI outcome prediction model for mortality.MethodsMachine learning and advanced analytics were applied to the patient admission variables obtained from a comprehensive pediatric sTBI database. Demographic and clinical data, head CT imaging abnormalities and blood biochemical data from 196 children and adolescents admitted to a tertiary pediatric intensive care unit (PICU) with sTBI were integrated using feature ranking by way of a forest of randomized decision trees, and a model was generated from a reduced number of admission variables with maximal ability to discriminate outcome.ResultsIn total, 36 admission variables were analyzed using feature ranking with variable weighting to determine their predictive importance for mortality following sTBI. Reduction analysis utilizing Borata feature selection resulted in a parsimonious six-variable model with a mortality classification accuracy of 82%. The final admission variables that predicted mortality were: partial thromboplastin time (22%); motor Glasgow Coma Scale (21%); serum glucose (16%); fixed pupil(s) (16%); platelet count (13%) and creatinine (12%). Using only these six admission variables, a t-distributed stochastic nearest neighbor embedding algorithm plot demonstrated visual separation of sTBI patients that lived or died, with high mortality predictive ability of this model on the validation dataset (AUC = 0.90) which was confirmed with a conventional area-under-the-curve statistical approach on the total dataset (AUC = 0.91; P < 0.001).ConclusionsMachine learning-based modeling identified the most clinically important prognostic factors resulting in a pragmatic, high performing prognostic tool for pediatric sTBI with excellent discriminative ability to predict mortality risk with 82% classification accuracy (AUC = 0.90). After external multicenter validation, our prognostic model might help to guide treatment decisions, aggressiveness of therapy and prepare family members and caregivers for timely end-of-life discussions and decision making.Level Of EvidenceIII; Prognostic.Copyright © 2022 Elsevier Ltd. All rights reserved.

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