Shockwave therapy associated with progressive exercises in rotator cuff tendinopathy: a clinical trial protocol

Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach

The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid and safe treatment. Sometimes the symptoms cannot be relieved, or a relapse develops, affecting the patient's quality of life. Therefore, a prediction protocol could be a powerful tool aiding our clinical decisions. An artificial neural network was run, in particular a multilayer perceptron model incorporating input information such as the VAS and Constant-Murley score, administered at T0 and at T1 after six months. It showed a model sensitivity of 80.7%, and the area under the ROC curve was 0.701, which demonstrates good discrimination. The aim of our study was to identify predictive factors for minimal clinically successful therapy (MCST), defined as a reduction of ≥40% in VAS score at T1 following ESWT for chronic non-calcific supraspinatus tendinopathy (SNCCT). From the male gender, we expect greater and more frequent clinical success. The more severe the patient's initial condition, the greater the possibility that clinical success will decrease. The Constant and Murley score, Roles and Maudsley score, and VAS are not just evaluation tools to verify an improvement; they are also prognostic factors to be taken into consideration in the assessment of achieving clinical success. Due to the lower clinical improvement observed in older patients and those with worse clinical and functional scales, it would be preferable to also provide these patients with the possibility of combined treatments. The ANN predictive model is reasonable and accurate in studying the influence of prognostic factors and achieving clinical success in patients with chronic non-calcific tendinopathy of the supraspinatus treated with ESWT.

Effects of High-Energy Extracorporeal Shockwave Therapy on Pain, Functional Disability, Quality of Life, and Ultrasonographic Changes in Patients with Calcified Rotator Cuff Tendinopathy

Objective

The current trial was designed to evaluate the effects of high-energy shockwave therapy on objective and subjective outcomes among participants with calcified rotator cuff tendinopathy.

Methods

This parallel-group, randomized trial consists of 42 patients affected by calcific tendinopathies divided into two groups of 21 participants. Patients having calcified tendinopathy aged between 30 and 65 years with type A or B calcification were selected in the trial after signing the written consent form. Participants in the ESWT+RPT group received eight sessions of shockwaves, while the RPT group was treated by routine physical therapy. About 2000 shockwaves of 0.32 mJ/mm², 120 Hz per treatment, were given as 12 sessions for the first six weeks (2 sessions/week). Pain intensity and shoulder functional ability, ultrasonographic changes, and quality of life were assessed with the numeric pain rating scale (NPRS), Constant-Murley score (CMS), ultrasonography, and Western Ontario rotator cuff index (WORC).

Results

There were significant differences regarding NPRS and CMS between the two groups, at baseline and 6th and 12th weeks after intervention (p < 0.05). Within-group differences also showed statistically significant results after treatment (all p < 0.05). Significant results were seen in the WORC and ultrasonographic results pre- and posttreatment; more significant findings were found in the experimental group as compared to others.

Conclusion

High-energy shockwave therapy has been proved to be effective and thus strongly recommended for the management of calcified rotator cuff tendinopathy, improving the pain, functionality, and quality of life of these participants and decreasing the size of calcified deposits. Shockwave therapy is proved to be superior to routine physiotherapy.