Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
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Methods: The first phase involved the construction of the application using the Delphi method. In the second phase, the application was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regression (odds ratio, 95% confidence interval). Results: The mean time to complete the application was 15 ± 7 minutes. The validity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.
Abstract
Methods: The first phase involved the construction of the application using the Delphi method. In the second phase, the application was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regression (odds ratio, 95% confidence interval). Results: The mean time to complete the application was 15 ± 7 minutes. The validity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.
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