Healthcare informatics research · 2019
Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bigorda-Sague Albert, Trujillano Cabello Javier, Ariza Carrio Gemma, Campoy Guerrero Carmen
Аннотация
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.
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.