TY - JOUR
T1 - Does My Patient With Shoulder Pain Have a Rotator Cuff Tear? A Predictive Model From the ROW Cohort
AU - Jain, Nitin B.
AU - Fan, Run
AU - Higgins, Laurence D.
AU - Kuhn, John E.
AU - Ayers, Gregory D.
N1 - Publisher Copyright:
© The Author(s) 2018.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
KW - diagnostic accuracy
KW - predictive modeling
KW - rotator cuff tears
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U2 - 10.1177/2325967118784897
DO - 10.1177/2325967118784897
M3 - Article
C2 - 30038917
AN - SCOPUS:85051081761
SN - 2325-9671
VL - 6
JO - Orthopaedic Journal of Sports Medicine
JF - Orthopaedic Journal of Sports Medicine
IS - 7
ER -