Development of a Predictive Model to Identify Clinical Factors Associated With Response to Percutaneous Electrical Nerve Stimulation in Patients With Subacromial Pain Syndrome

Subacromial pain syndrome (SPS) includes a group of clinical entities related to rotator cuff tendinopathy and subacromial structures that cause shoulder pain and functional limitations. PENS applied to the suprascapular nerve (SN) has demonstrated effectiveness in reducing pain and improving short-term function in patients with SPS. However, variability in clinical response suggests the influence of individual characteristics that are not yet well understood.

This study will conduct a prospective, single-arm, uncontrolled clinical trial involving 269 patients diagnosed with SPS. All participants will undergo a single ultrasound-guided session of PENS on the SN. The primary outcome will be post-treatment pain intensity measured with the Visual Analogue Scale (VAS). n addition to the primary outcome, several baseline variables will be collected to develop a multivariable beta regression predictive model. These variables include demographic data (age, sex, weight, height, BMI), clinical characteristics (pain duration in weeks, baseline pain intensity measured with the Visual Analogue Scale), psychological measures (Hospital Anxiety and Depression Scale - HADS), physical activity levels (International Physical Activity Questionnaire - Short Form), and measures of neural mechanosensitivity (pressure pain threshold on the infraspinatus and result of the suprascapular neurodynamic test). These predictors will be incorporated into the model regardless of their univariate significance, in line with best practices in predictive modelling.

A beta regression model will be developed to predict post-treatment pain intensity. Internal validation and model stability will be assessed using bootstrap resampling methods. This protocol follows the TRIPOD guidelines for clinical prediction model development.

Identifying reliable predictors of response will support clinical decision-making, helping to personalize physiotherapy approaches and improve the cost-effectiveness of SPS management. The findings may provide a robust foundation for future research and eventual clinical implementation of prediction tools for individualized therapy planning.