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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology

OBJECTIVES: To design and validate a computer application for the diagnosis of shoulder locomotor system pathology. 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 sho...

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Detalles Bibliográficos
Autores principales: Bigorda-Sague, Albert, Trujillano Cabello, Javier, Ariza Carrio, Gemma, Campoy Guerrero, Carmen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Medical Informatics 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517628/
https://www.ncbi.nlm.nih.gov/pubmed/31131142
http://dx.doi.org/10.4258/hir.2019.25.2.82
Descripción
Sumario:OBJECTIVES: To design and validate a computer application for the diagnosis of shoulder locomotor system pathology. 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.