Developing a Clinical Prediction Model for Failed Nonoperative Management of SLAP Tears (220). Issue 10 (31st October 2021)

• Record Type: Journal Article
• Authors: Berlinberg, Elyse; Kingery, Matthew; Manjunath, Amit; Markus, Danielle; Strauss, Eric
• Is Part Of: Orthopaedic journal of sports medicine. Volume 9:Issue 10(2021)Supplement 5
• Publication Date: 2021-10-31
• Subjects: Sports medicine -- Periodicals; Orthopedics -- Periodicals; Arthroscopy -- Periodicals; Arthroplasty -- Periodicals; Knee -- Surgery -- Periodicals; 616.7
• DOI: 10.1177/2325967121S00328
• Languages: English
• ISSNs: 2325-9671

Abstract

Objectives: Patients with a superior labral anterior to posterior (SLAP) tear of the shoulder are often initially treated non-operatively, but many do not respond and require surgery. Identifying patients who are likely to fail non-operative management and would benefit from early surgical intervention can shorten time of disability and limit resources utilized on unsuccessful treatments. The purpose of this study is to create a clinical prediction model to determine which patients are likely to fail non-operative treatment of SLAP tears and require surgical intervention. Methods: This was a case-control study consisting of patients treated at a single institution for isolated, non-degenerative SLAP tears. Patients with concomitant rotator cuff tears were excluded from this analysis. Patients were retrospectively surveyed using the Research Electronic Data Capture (REDCap) system regarding clinical features of their shoulder injury, non-operative treatments that they received, and key functional outcomes during their post-injury period. Responders underwent additional medical record review to identify other variables related to the clinical presentation and treatment of their shoulder injury. In order to simplify the predictive model and optimize its interpretability, the lasso (least absolute shrinkage and selection operator) method of penalized logistic regression analysis was used to identify the characteristics that were most closely associated with failure of nonoperative treatment. The data was randomly split into a training set and test set. Using the training set, the value of lambda which minimized cross-validation prediction error rate was determined (Figure 1). The final lasso model was then computed. The predictive accuracy of the final model was assessed using the test data set. Results: Overall, 189 patients were contacted and included in the analysis. The mean age of included patients was 29.9 +/- 6.7 years. Thirty-eight patients (20.1%) were female. One hundred and six patients (56.1%) failed non-operative management and required surgical intervention. The final lasso model identified a total of 9 variables that were significantly associated with failure of non-operative management of SLAP tears (Table 1). These predictors included pre-injury overhead sports participation, presence of specific symptoms, severity of pain, and the type of non-operative treatment modalities used. Injury to the dominant extremity, history of prior shoulder injury, patient age, use of NSAIDs, and occupation involving manual labor or overhead work were not associated with failure of nonoperative treatment. The predictive accuracy of the model was 70.3% (95% CI 53.0%, 84.1%). Sensitivity of the model was 81.0% and specificity was 56.3%. Conclusions: A clinical prediction model consisting of variables describing patient characteristics, specific symptoms, and the type of non-operative treatment modalities utilized was found to predict failure of non-operative management of SLAP tears with moderate accuracy. Further refinements of this prediction model, including the inclusion of additional physical examination and imaging variables, will be required before future iterations are tested in clinical practice.