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AB0933 Predictive model for shoulder pain using clinical and epidemiological variables
AB0933 Predictive model for shoulder pain using clinical and epidemiological variables
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AB0933 Predictive model for shoulder pain using clinical and epidemiological variables
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AB0933 Predictive model for shoulder pain using clinical and epidemiological variables
AB0933 Predictive model for shoulder pain using clinical and epidemiological variables

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AB0933 Predictive model for shoulder pain using clinical and epidemiological variables
AB0933 Predictive model for shoulder pain using clinical and epidemiological variables
Journal Article

AB0933 Predictive model for shoulder pain using clinical and epidemiological variables

2017
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Overview
BackgroundShoulder pain is a very common complaint with poor prognosis and high recurrence. To evaluate the shoulder pain, anamnesis and physical examination are used, but a diagnosis of certainty is difficult. Clinical history and specific exploration maneuvers tend to be poorly correlated with the underlying problem. There are few studies that assess the predictability of shoulder pathology using patient characteristics and exploration.ObjectivesTo assess if the combination of exploratory maneuvers and clinical data predicts the type of affection of the painful shoulder in a sensitive and specific way.MethodsWe conducted a prospective study with patients who attended to the Rheumatology Department of HUP La Fe by painful shoulder between February 2016 and January 2017, excluding those with known inflammatory diseases. A rheumatologist performed the anamnesis and the selected exploratory maneuvers: Jobe and Gerber test and palpation of the acromioclavicular joint. A second rheumatologist, blind to physical examination and medical history, performed the shoulder ultrasound scan. Biostatistic analysis was performed using software R version 3.3.2.Results119 patients (66.4% women) with a mean age of 60±12.56 years and shoulder pain were collected. Time of pain evolution was 20.43±24.09 months and the right shoulder was the most affected one (71.4%). The association between the maneuvers of Jobe and the involvement of the supraspinatus (SE), as well as the Gerber maneuver with the affectation of the subscapular were statistically significant. However the sensitivity and specificity of both maneuvers are very low, so that alone is not suitable to identify the affected tendon or the type of alteration. Thus, a predictor model (nomogram) of the most common shoulder pathologies (subacromiodeltoid bursitis, tendinosis or SE tears) was developed using epidemiological and clinical examination variables.ConclusionsBased on our results, the predictor model performed using epidemiological and clinical examination variables would be able to predict the most frequent pathologies of the shoulder. Imaging tests have a certain delay time, and by applying this predictor model, a diagnosis of presumption could be established in primary care, giving the opportunity to institute an early treatment. In addition, patients could be referred more efficiently to the appropriate specialty (rheumatology, traumatology or rehabilitation), avoiding delays.Disclosure of InterestNone declared