Pediatric severe traumatic brain injury mortality prediction determined with machine learning-based modeling.
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 a...
| Publicado en: | Injury Vol. 53; no. 3; pp. 992 - 999 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155340984&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155340984 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00201383 JGC jtl: Injury issn: 00201383 maglogo: N pubinfo: dt: Mar2022 vid: 53 iid: 3 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 155340984 155340984 NLM35034778 155340984 10.1016/j.injury.2022.01.008 NLM35034778 155340984 ppf: 992 ppct: 7 formats: tig: atl: Pediatric severe traumatic brain injury mortality prediction determined with machine learning-based modeling. aug: au: Daley, Mark Cameron, Saoirse Ganesan, Saptharishi Lalgudi Patel, Maitray A. Stewart, Tanya Charyk Miller, Michael R. Alharfi, Ibrahim Fraser, Douglas D. affil: Computer Science, Western University, London, ON N6A 3K7, Canada sug: subj: Retrospective Design Adolescence Child Tomography, X-Ray Computed Prognosis Human Glasgow Coma Scale Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Adolescent: 13-18 years Child: 6-12 years ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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