• Roni Ramon-Gonen, Yelena Granovsky, and Shahar Shelly.
• The School of Business Administration, Bar-Ilan University, Ramat-Gan, Israel.
• Pain. 2024 Sep 27.

AbstractTraumatic 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.Copyright © 2024 International Association for the Study of Pain.

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