Does My Patient With Shoulder Pain Have a Rotator Cuff Tear? A Predictive Model From the ROW Cohort. Issue 7 (16th July 2018)

• Record Type: Journal Article
• Authors: Jain, Nitin B.; Fan, Run; Higgins, Laurence D.; Kuhn, John E.; Ayers, Gregory D.
• Is Part Of: Orthopaedic journal of sports medicine. Volume 6:Issue 7(2018)
• Publication Date: 2018-07-16
• Subjects: rotator cuff tears -- diagnostic accuracy -- predictive modeling; Sports medicine -- Periodicals; Orthopedics -- Periodicals; Arthroscopy -- Periodicals; Arthroplasty -- Periodicals; Knee -- Surgery -- Periodicals; 616.7
• DOI: 10.1177/2325967118784897
• Languages: English
• ISSNs: 2325-9671

Abstract

Background: Rotator cuff tears are the leading cause of shoulder pain and disability. However, the diagnosis of a rotator cuff tear based on patient characteristics, symptoms, and physical examination findings remains a challenge because of a lack of data. Moreover, data on the predictive ability of a combination of these characteristics and tests are not available from a large cohort of patients. Consequently, clinicians rely on expensive imaging, such as magnetic resonance imaging (MRI), to make a diagnosis. Purpose: To model patient characteristics, symptoms, and physical examination findings that predict a rotator cuff tear. We present a nomogram based on our predictive model that can be used in patients with shoulder pain to determine the probability of the diagnosis of a rotator cuff tear without the need for imaging. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: We recruited patients from outpatient clinics who were ≥45 years of age and who had shoulder pain of at least 4 weeks' duration. A rotator cuff tear was diagnosed based on expert clinical impression and the presence/absence of a tear on a blinded review of MRI. Ultimately, 301 patients were included in the analysis. Results: A total of 123 patients (41%) had rotator cuff tears, and 178 patients (59%) did not. The predictors of the diagnosis of a rotator cuff tear included external rotation strength ratio of the affected versus unaffected shoulder (odds ratio [OR], 1.20 [95% CI, 1.08-1.34]), male sex (OR, 1.98 [95% CI, 1.10-3.56]), positive lift-off test result (OR, 4.33 [95% CI, 1.46-12.86]), and positive Jobe test result (OR, 9.19 [95% CI, 4.69-17.99]). A nomogram based on these predictor variables was plotted. Conclusion: Presented is a model that can accurately predict the diagnosis of a rotator cuff tear with satisfactory discrimination and calibration based on 4 variables: sex, lift-off test, Jobe test, and external rotation strength ratio. Data from this study can be used to aid in the diagnosis of a rotator cuff tear in day-to-day clinical practice in outpatient settings without the need for expensive imaging such as MRI.