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New classification criteria for gout: a framework for progress

Department of Medicine, University of Auckland, Auckland, New Zealand, Department of Primary and Community Care, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands, Department of Rheumatology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands, Clinical Epidemiology Research & Training Unit, Boston University School of Medicine, Boston, MA, USA, Division of Rheumatology, University of Pennsylvania and VA Medical Center, Philadelphia, PA, USA and Department of Medicine, University of Otago, Wellington, New Zealand.
Rheumatology (Oxford, England) (Impact Factor: 4.44). 04/2013; 52(10). DOI: 10.1093/rheumatology/ket154
Source: PubMed

ABSTRACT The definitive classification or diagnosis of gout normally relies upon the identification of MSU crystals in SF or from tophi. Where microscopic examination of SF is not available or is impractical, the best approach may differ depending upon the context. For many types of research, clinical classification criteria are necessary. The increasing prevalence of gout, advances in therapeutics and the development of international research collaborations to understand the impact, mechanisms and optimal treatment of this condition emphasize the need for accurate and uniform classification criteria for gout. Five clinical classification criteria for gout currently exist. However, none of the currently available criteria has been adequately validated. An international project is currently under way to develop new validated gout classification criteria. These criteria will be an essential step forward to advance the research agenda in the modern era of gout management.

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    ABSTRACT: To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. This article is protected by copyright. All rights reserved. © 2015 American College of Rheumatology.
    03/2015; DOI:10.1002/acr.22585

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