Machine Learning-Based Modeling for Predicting Hypopituitarism After Cranial Trauma

<black square> OBJECTIVE: Traumatic brain injury (TBI) remains a leading cause of disability and death globally, and post-traumatic hypopituitarism (PTHP) is a common complication for which there is a lack of accurate predictive models. This study aimed to develop a machine learning model to assess the risk of hypopituitarism after TBI. <black square> METHODS: A sample of 620 cases was analyzed using the logistic independent variable event count method. Data was split into a training set (70%) and a test set (30%), with 5-fold cross-validation applied. Ten machine learning models were evaluated: Logistic Regression, Decision Tree, Random Forest, LightGBM, XGBoost, CatBoost, Naive Bayes, Support Vector Machine, k-nearest neighbors, and Multi-Layer Perceptron. Performance metrics included accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve. The following variables were significantly associated with PTHP (P < 0.05): hypertension, intensive care unit admission, Glasgow Coma Scale <= 8, diffuse cerebral edema, cerebral herniation, midline shift >= 5 mm, elevated intracranial pressure, craniotomy, skull base fracture, and length of stay. These variables may contribute to a predictive model for PTHP risk. <black square> RESULTS: Logistic regression excelled in training and test sets, boasting an area under the curve (AUC) of 0.905 and 0.887, respectively, with balanced sensitivity and specificity. Naive Bayes and ensemble methods like LightGBM and CatBoost showed competitive AUCs. Midline shift >= 5 mm was the strongest predictor. The model demonstrated excellent calibration per the Hosmer-Lemeshow test. <black square> CONCLUSIONS: Logistic regression, with its strong AUC, sensitivity, specificity, and calibration, emerged as the superior model for PTHP prediction post-TBI, This study could facilitate earlier diagnosis and treatment of PTHP in TBI patients.