Evaluation of Outcome of Subjects with Shoulder Pain- A Retrospective Analysis

Evaluation of Outcome of Subjects with Shoulder Pain

  • Raj Kishore Chaurasiya Assistant Professor, Department of Orthopaedics, L N Medical College & Research Centre Bhopal.
Keywords: Comorbidities, shoulder, physiological


Background: Shoulder pain is usually not related with favorable outcome in approximately 40-50% of all cases presenting to the primary health care hospital. Different prognostic factors have been regarded in few of the 16 studies like sex, type of injury, psychological factors, stresses, anatomical factors and impairment of strength. The aim of the present study was to evaluate the predictors of subject outcome with shoulder pain. Subjects and Methods: The present retrospective analysis was performed in the orthopedic department for a duration of 2 years. Anterior or posterior drawer tests were used to indicate the shoulder instability. Severe loss of motion was regarded when the patient had loss of more than 50% of the normal physiological motion range. Different treatment modalities were evaluated based on whether the patient needed that type of treatment or not. All the data thus obatined was arranged in a tabulated form and analyzed using SPSS software. Probability value of less than 0.05 was regarded as significant. Results: The mean change in quickdash score after treatment was 16.76+/-9.21. The mean number of total visits was 12.43+/-5.28 and the mean visits per week was 2.25+/-0.62. There was a significant change in the quickdash score amongst the subjects. The number of visits to the doctor also showed significant effect. The presence of comorbidities also showed a significant difference amongst the subjects. Conclusion: The best predictors found in the study were the quickdash score and the incidence of visits to the health care services.

How to Cite
Chaurasiya, R. (2019). Evaluation of Outcome of Subjects with Shoulder Pain- A Retrospective Analysis. Asian Journal of Medical Research, 8(1), OR01-OR03. Retrieved from https://aijournals.com/index.php/ajmr/article/view/461