Development and internal validation of a therapeutic effect predictive model for myofascial pain syndrome.

Background: Myofascial Pain Syndrome (MPS) is a common chronic pain disorder, and there are significant individual differences in its clinical efficacy. Currently, there is a lack of reliable prediction tools to guide individualized treatment decisions. This study aimed to construct and validate a prediction model based on clinical and biomarker data to evaluate the responses of MPS patients to different treatment regimens and optimize treatment strategies. Methods: A total of 340 MPS patients was retrospectively enrolled and randomly split into a training set ( Results: The final model included 6 key predictive factors (including disease duration, baseline pain intensity, Patient Health Questionnaire-9 depression score, pain catastrophizing score, interleukin-6, and high-sensitivity C-reactive protein levels). The AUC value of the support vector machine model reached 895 (95%CI: 0.840-0.950) in the training set and remained at a relatively high level of 0.873(95%CI: 0.794-0.953) in the validation set, and the calibration was good (Hosmer-Lemeshow test, Conclusion: A MPS efficacy prediction model, which had good internal predictive efficacy and interpretability, integrating clinical, psychological and inflammatory indicators was successfully constructed and internally validated. In the future, multi-center external validation and model optimization are needed to further improve its clinical applicability and promotion value.