OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.METHODSThe clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.RESULTS“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.
Risk factors for blunt cerebrovascular injury (BCVI) may differ between children and adults, suggesting that children at low risk for BCVI after trauma receive unnecessary computed tomography angiography (CTA) and high-dose radiation. We previously developed a score for predicting pediatric BCVI based on retrospective cohort analysis. Our objective is to externally validate this prediction score with a retrospective multi-institutional cohort. We included patients who underwent CTA for traumatic cranial injury at four pediatric Level I trauma centers. Each patient in the validation cohort was scored using the "Utah Score" and classified as high or low risk. Before analysis, we defined a misclassification rate <25% as validating the Utah Score. Six hundred forty-five patients (mean age 8.6 ± 5.4 years; 63.4% males) underwent screening for BCVI via CTA. The validation cohort was 411 patients from three sites compared with the training cohort of 234 patients. Twenty-two BCVIs (5.4%) were identified in the validation cohort. The Utah Score was significantly associated with BCVIs in the validation cohort (odds ratio 8.1 [3.3, 19.8], p < 0.001) and discriminated well in the validation cohort (area under the curve 72%). When the Utah Score was applied to the validation cohort, the sensitivity was 59%, specificity was 85%, positive predictive value was 18%, and negative predictive value was 97%. The Utah Score misclassified 16.6% of patients in the validation cohort. The Utah Score for predicting BCVI in pediatric trauma patients was validated with a low misclassification rate using a large, independent, multicenter cohort. Its implementation in the clinical setting may reduce the use of CTA in low-risk patients.
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