Random Forest Prognostication of Survival and 6-Month Outcome In Pediatric Patients Following Decompressive Craniectomy For Traumatic Brain Injury

Introduction: There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) following traumatic brain injury (TBI). The aim of this study was to develop a random forest machine learning algorithm to predict outcomes following DC in pediatrics.

Methods and materials: This is a multi-institutional retrospective study assessing the 6-month postoperative outcome in pediatric patients that underwent DC. We developed a machine learning model using classification and survival random forest algorithms (CRF and SRF respectively) for the prediction of outcomes. Data was collected on clinical signs, radiographic studies, and laboratory studies. The outcome measures for the CRF were mortality and good or bad outcome based on Glasgow Outcome Scale (GOS) at 6-months. A GOS score of 4 or higher indicated a good outcome. The outcomes for the SRF model assessed mortality at during the follow-up period.

Results: A total of 40 pediatric patients were included. There was a hospital mortality rate of 27.5%, and 75.8% of survivors had a good outcome at 6-month follow up. The CRF for 6-month mortality had a ROC AUC of 0.984; whereas, the 6-month good/bad outcome had a ROC AUC of 0.873. The SRF was trained at the 6-month timepoint with a ROC AUC of 0.921.

Conclusion: CRF and SRF models successfully predicted 6-month outcomes and mortality following DC in pediatric TBI patients. These results suggest random forest models may be efficacious for predicting outcome in this patient population.