Pediatric severe traumatic brain injury mortality prediction determined with machine learning-based modeling. Issue 3 (March 2022)

• Record Type: Journal Article
• Authors: Daley, Mark; Cameron, Saoirse; Ganesan, Saptharishi Lalgudi; Patel, Maitray A.; Stewart, Tanya Charyk; Miller, Michael R.; Alharfi, Ibrahim; Fraser, Douglas D.
• Is Part Of: Injury. Volume 53:Issue 3(2022)
• Page Start: 992
• Page End: 998
• Publication Date: 2022-03
• Subjects: Children -- Severe traumatic brain injury -- Machine learning -- Mortality -- Prognostic modeling; Wounds and injuries -- Surgery -- Periodicals; Accidents -- Periodicals; Wounds and Injuries -- surgery -- Periodicals; Lésions et blessures -- Chirurgie -- Périodiques; Electronic journals; Electronic journals; 617.1
• DOI: 10.1016/j.injury.2022.01.008
• Languages: English
• ISSNs: 0020-1383

Abstract

Highlights: Accurate prognostication of severe TBI, a leading killer of youth, can help guide clinicians for interventions and/or end-of-life discussions. A novel, highly discriminative clinical pediatric sTBI outcome prediction model was created by applying advanced machine learning techniques. The final six-variable mortality prediction model included PTT, motor GCS, serum glucose, fixed pupil(s), platelet count and creatinine. The resulting parsimonious model had an 82% classification accuracy and an AUC of 0.90, signifying high discriminative ability. Abstract: Introduction: Severe 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. Methods: Machine 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. Results: In 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). Conclusions: Machine 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 evidence: III; Prognostic.