Outcome Prediction in Pediatric Traumatic Brain Injury Utilizing Social Determinants of Health and Machine Learning Methods.

BACKGROUND AND PURPOSE: Considerable socioeconomic disparities exist among pediatric patients with traumatic brain injury (TBI). This study aims to analyze the effects of social determinants of health on head injury outcomes and to create a novel machine-learning algorithm (MLA) that incorporates socioeconomic factors to predict the likelihood of a positive or negative trauma-related finding on head CT.

MATERIALS AND

METHODS

A cohort of patients with blunt trauma younger than age 15 who presented to the largest safety net hospital in New England between January 2006 and December 2013 (n=211) was included in this study. Patient socioeconomic data such as race, language, household income, and insurance type were collected alongside other parameters like Injury Severity Score (ISS), age, sex, and mechanism of injury. Multivariable analysis was performed to identify significant factors in predicting a positive head CT outcome. The cohort was split into 80% training (168 samples) and 20% testing (43 samples) data sets by using stratified sampling. Twenty-two multiparametric MLAs were trained with 5-fold cross-validation and hyperparameter tuning via GridSearchCV, and top-performing models were evaluated on the test data set.

RESULTS

Significant factors associated with pediatric head CT outcome included ISS, age, and insurance type (P < .05). The age of the subjects with a clinically relevant trauma-related head CT finding (median = 1.8 years) was significantly different from the age of patients without such findings (median = 9.1 years). These predictors were utilized to train the machine learning models. With ISS, the fine Gaussian support vector machine (SVM) achieved the highest test AUC (0.923), with accuracy = 0.837, sensitivity = 0.647, and specificity = 0.962. The coarse tree yielded accuracy = 0.837, AUC = 0.837, sensitivity = 0.824, and specificity = 0.846. Without ISS, the narrow neural network performed best with accuracy = 0.837, AUC = 0.857, sensitivity = 0.765, and specificity = 0.885.

CONCLUSIONS

Key predictors of clinically relevant head CT findings in pediatric TBI include ISS, age, and social determinants of health, with children younger than 5 at higher risk. A novel fine Gaussian SVM model outperformed other MLAs, offering high accuracy in predicting outcomes. This tool shows promise for improving clinical decisions while minimizing radiation exposure in children.