The new criteria for classification of Rheumatoid Arthritis have been recently released. They incorporate the anti-Citrullinated Protein antibody testing and the other classic criteria in a score system (the diagnosis of definite rheumatoid arthritis is made by a total score ≥6). These criteria try to meet the pressing needs to gain sensitivity in early disease. Symptoms, elevated acute-phase response, serologic abnormality, joint involvement were all considered for scoring after confirming the presence of synovitis in at least 1 joint in the absence of an alternative diagnosis that better explains the synovitis. However, no sensitivity and specificity has been showed. Moreover, Area Under Curve of the Receiver Operating Characteristic curves (a measure of performance of the test) was not optimal in almost two of the three studied cohorts. On the contrary, the old criteria of the American College of Rheumatology had been tested to calculate sensitivity and specificity. Moreover, sensitivity and specificity of anti-citrullinated peptide auto-antibodies are available for clinical reasoning based on pre-test and post-test probabilities of the disease. The use of likelihood ratios applied to both the old criteria and anti-citrullinated autoantibodies could help clinicians to effectively manage early arthritis patients implementing Bayesian reasoning. Here, we tried to explain the methodology applied to the body of knowledge currently available about rheumatoid arthritis for diagnostic decision-making based on the Bayesian approach.
|Numero di pagine||3|
|Rivista||European Journal of Internal Medicine|
|Stato di pubblicazione||Published - 2011|
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