Machine learning-based models to predict the need for neurosurgical intervention after moderate traumatic brain injury.

Background and Aims: Traumatic brain injury (TBI) is a widespread global health issue with significant economic consequences. However, no existing model exists to predict the need for neurosurgical intervention in moderate TBI patients with positive initial computed tomography scans. This study determines the efficacy of machine learning (ML)-based models in predicting the need for neurosurgical intervention. Methods: This is a retrospective study of patients admitted to the neuro-intensive care unit of Emtiaz Hospital, Shiraz, Iran, between January 2018 and December 2020. The most clinically important variables from patients that met our inclusion and exclusion criteria were collected and used as predictors. We developed models using multilayer perceptron, random forest, support vector machines (SVM), and logistic regression. To evaluate the models, their F1-score, sensitivity, specificity, and accuracy were assessed using a fourfold cross-validation method. Results: Based on predictive models, SVM showed the highest performance in predicting the need for neurosurgical intervention, with an F1-score of 0.83, an area under curve of 0.93, sensitivity of 0.82, specificity of 0.84, a positive predictive value of 0.83, and a negative predictive value of 0.83. Conclusion: The use of ML-based models as decision-making tools can be effective in predicting with high accuracy whether neurosurgery will be necessary after moderate TBIs. These models may ultimately be used as decision-support tools to evaluate early intervention in TBI patients.

Epidemiology of Cervical Spine Fractures.

BACKGROUND: Epidemiology of cervical spine fractures (CSfx) in trauma patients of general population is not yet exclusively known. OBJECTIVES: The purpose of this study was to evaluate the epidemiology of CSfx in trauma patients. PATIENTS AND METHODS: Data from trauma patients admitted in the emergency room (ER) of Shiraz Shahid Rajaei hospital during the 3.5 years period from September 22, 2009 to March 21, 2013, were gathered. All trauma patients with CSfx and/or spinal cord injuries were included in the study. The time of the trauma, mechanism of trauma, injury position, and incidence of cervical spine fractures in the patients were recorded. RESULTS: A total of 469 patients met the inclusion criteria. The mean age of the patients was 34.7 years old, with a minimum age of 16 years old and a maximum age of 89 years old. Young adults were most frequently affected. Out of 469 cases, 368 patients (78.47%) were male and 101 (21.53%) were female. We had a total of 17 SCI cases among our patients (3.62%), out of which 5 (29.41%) were deceased. The total number of deaths in our study was 29 (6.18%); 5 (17.24%) with SCI and 24 (82.76%) without SCI. CONCLUSIONS: This study demonstrated that most victims of CSfx in our region are 16 to 40 years of age. A male predominance was observed, and motor vehicle collisions were the most frequent trauma mechanism leading to cervical spine injury (mostly due to car rollover accidents), with falls as the second most frequent. The rate of SCI in our study was 3.62% of all cases and the mortality rate was 6.18%.