Granovsky, Yelena

Abstract Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.

Abstract Traumatic brain injury (TBI) annually impacts 69 million individuals worldwide. Mild TBI constitutes approximately 90% of all TBIs. Chronic pain post-mTBI occurs in 29% to 58% of patients. This study aims to introduce a predictive model for chronic pain development in individuals diagnosed with mild traumatic brain injury (mTBI) immediately postinjury. We included individuals who had sustained mTBI in motor vehicle accident (MVA). All patients had initial assessments within the first 72 hours (representing the subacute period) after the injury and performed follow-ups for 1 year. Machine learning model was applied to the integrated measures of clinical pain, pain-related psychological parameters, mTBI clinical signs, and sociodemographic information. This study included 203 patients experiencing acute head or neck pain attributable to mTBI post-MVA. We categorized these patients into 2 groups: patients who progressed to develop chronic head or neck pain (n = 89, 43.8%) and patients who recovered (low/mild pain) (n = 114, 56.2%). Severity of the subacute neck pain, number of painful body areas, and education years were identified as the most significant factors predicting chronic pain. The optimized predictive model demonstrated high efficacy, with an accuracy of 83%, a sensitivity of 92%, and an area under the receiver operating characteristic curve of 0.8. Our findings indicate feasibility in predicting chronic post-MVA pain within the critical 72-hour window postinjury using simple bedside metrics. This approach offers a promising avenue for the early detection of individuals at increased risk for chronic pain, enabling the implementation of targeted early interventions.