Accuracy of High Resolution Ultrasonography for Diagnosis of Rotator Cuff Tears Con-sidering Magnetic Resonance Imaging as Gold Standard
Keywords:Diagnostic accuracy, Magnetic Resonance Imaging, Rotator Cuff Tears, Ultrasonography.
AbstractObjective: To determine the diagnostic accuracy of high-resolution ultrasonography for the diagnosis of rotator cuff tears considering magnetic resonance imaging as gold standard. Methodology: This correlational study was done using non-probability consecutive sampling technique, at the Department of Orthopaedic Surgery and the Department of Radiology, Services Hospital Lahore from 15th July 2013 till 14th January 2016. All the patients, age between 40 to 70 years, who presented with shoulder pain for last three month, that was not settled with oral analgesic, and were positive for Hawkin’s -Kennedy test, Jobe’s test and drop arm were included in the study. Patients who had fracture of the clavicle, scapula or proximal end of humerus, and patients with prosthetic implants and pacemakers were excluded in the study. All clinical test positive patients underwent ultrasonography (USG) and magnetic resonance imaging (MRI). We calculated specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), accuracy and likelihood ratio for ultrasonography. Results: Out of the total 92 patients, on MRI there were 68 (73.9%) complete and 24 (26.1%) partial Rotator Cuff Tears. USG diagnosed 62 out of 68 complete tears accurately. There were 21 true negative and 03 false positive complete tears. USG showed sensitivity of 91%, specificity of 87% (p-value <0.001 using T-test), positive predictive value (PPV) of 95%, negative predictive value of 77%, accuracy of 90.2 %, positive likelihood ratio of 9.55 and negative likelihood ratio of 0.15. Conclusion: USG of the shoulder with a high definition linear array probe was found a highly accurate and reliable screening test for the Rotator Cuff Tears and it can be used reliably for the start of treatment of the rotator cuff tears.
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