A machine learning approach to predict treatment response in myofascial pain patients receiving masseter trigger point injections

Abstract

Background: Trigger point injection (TPI) therapy is widely used for masseter myofascial pain syndrome (MPS), yet outcomes vary substantially. Individualized prediction tools are lacking, often leading to trial-and-error treatment selection.

Objective: To develop, externally validate, and generalize ensemble machine learning models for predicting composite treatment success following masseter TPI, and to deploy a web-based clinical decision support system (CDSS).

Methods: This multicenter study included 1,181 patients with DC/TMD‑diagnosed masseter MPS treated with one of six injectable modalities. Baseline variables included pain (VAS), maximum mouth opening (MMO), and oral health‑related quality of life (OHIP-14). Composite treatment success was defined as simultaneous clinically meaningful improvement at 3 months: VAS reduction ≥ 2 points, MMO increase ≥ 5 mm, and OHIP-14 reduction ≥ 5 points. Random Forest (RF) and XGBoost models were trained on an internal cohort and externally validated on a geographically independent cohort. Performance was assessed using ROC‑AUC, precision‑recall AUC (PR‑AUC), calibration, decision curve analysis (DCA), and SHAP interpretability.

Results: Overall composite success rate was 43.1%. Internal validation ROC‑AUC was 0.914 (RF) and 0.888 (XGBoost); external validation ROC‑AUC was 0.771 and 0.787, respectively. External PR‑AUC values were 0.759 (RF) and 0.761 (XGBoost). Both models showed good calibration and positive net benefit on DCA across clinically relevant thresholds. SHAP analysis identified baseline MMO, OHIP-14, age, pain intensity, and injectable modality as the most influential predictors, with consistent rankings across models and cohorts. The validated XGBoost model was deployed as a web‑based CDSS.

Conclusions: Machine learning models demonstrated good ability to predict multidimensional treatment success following masseter TPI. Baseline MMO, OHIP-14, age, pain intensity, and injectable modality were the strongest outcome determinants. External validation, SHAP interpretability, and DCA support model robustness and potential clinical utility for personalized treatment planning in MPS.

Clinical relevance: This tool may support treatment selection, reduce ineffective interventions, and improve patient outcomes in myofascial pain management.