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Random Forest Prognostication of Survival and 6-Month Outcome in Pediatric Patients Following Decompressive
Ryan D Morgan1, Brandon W Youssi1, Rafael Cacao2
1School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
Insights
Machine learning models accurately predict outcomes and mortality in pediatric patients after decompressive craniectomy (DC) for traumatic brain injury (TBI). These random forest models offer a promising tool for assessing patient prognosis.
Area of Science:
- Pediatric neurosurgery
- Medical artificial intelligence
- Clinical outcome prediction
Background:
- Limited literature exists on prognostic factors for pediatric decompressive craniectomy (DC) after traumatic brain injury (TBI).
- Predicting outcomes in this vulnerable population is crucial for guiding treatment decisions.
Purpose of the Study:
- To develop and validate random forest machine learning algorithms for predicting outcomes in pediatric patients undergoing DC for TBI.
- To identify key factors influencing survival and functional recovery.
Main Methods:
- A multi-institutional retrospective study analyzed 40 pediatric patients who underwent DC.
- Classification Random Forest (CRF) and Survival Random Forest (SRF) models were developed using clinical, radiographic, and laboratory data.
- Outcomes assessed included 6-month mortality and Glasgow Outcome Scale (GOS) scores.
Main Results:
- The hospital mortality rate was 27.5%, with 75.8% of survivors achieving a good outcome (GOS ≥4) at 6 months.
- The CRF model demonstrated high accuracy for predicting 6-month mortality (AUC=0.984) and good/bad outcomes (AUC=0.873).
- The SRF model also showed strong predictive performance for mortality (AUC=0.921).
Conclusions:
- Random forest models (CRF and SRF) effectively predicted 6-month outcomes and mortality in pediatric TBI patients following DC.
- These machine learning approaches show potential as efficacious tools for outcome prediction in this specific patient group.
Background:
There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) performed after traumatic brain injury (TBI). The aim of this study was to develop a random forest machine learning algorithm to predict outcomes following DC in pediatric patients.
Methods:
This multi-institutional retrospective study assessed the 6-month postoperative outcome in pediatric patients who underwent DC. We developed a machine learning model using classification random forest (CRF) and survival random forest (SRF) algorithms for prediction of outcomes. Data on clinical signs, radiographic studies, and laboratory studies were collected. Outcome measures for the CRF model were mortality and good or bad outcome based on Glasgow Outcome Scale at 6 months. A Glasgow Outcome Scale score of ≥4 indicated a good outcome. Outcome for the SRF model was mortality during the follow-up period.
Results:
The study included 40 pediatric patients. Hospital mortality rate was 27.5%, and 75.8% of survivors had a good outcome at 6-month follow up. The CRF model for 6-month mortality had a receiver operating characteristic area under the curve of 0.984, whereas, 6-month good and bad outcomes had a receiver operating characteristic area under the curve of 0.873. The SRF model was trained at the 6-month time point with a receiver operating characteristic area under the curve of 0.921.
Conclusions:
CRF and SRF models successfully predicted 6-month outcomes and mortality following DC in pediatric patients with TBI. These results suggest that random forest models may be efficacious for predicting outcome in this patient population.
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