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Arthritis Care Res (Hoboken). 2015 Sep;67(9):1304-15. doi: 10.1002/acr.22585.

Study for Updated Gout Classification Criteria: Identification of Features to Classify Gout.

Author information

1
University of Otago, Wellington, New Zealand.
2
Radboud University Medical Centre, Nijmegen, The Netherlands.
3
University of Auckland, Auckland, New Zealand.
4
University of Pennsylvania and VA Medical Center, Philadelphia.
5
Chiang Mai University, Chiang Mai, Thailand.
6
Hospital General de México, Mexico City, Mexico.
7
Nasonova Research Institute of Rheumatology, Moscow, Russia.
8
University College, Mater Misericordiae University Hospital, Dublin, Ireland.
9
University of Otago Christchurch, Christchurch, New Zealand.
10
Hospital Universitario Cruces and BioCruces Health Research Institute, Vizcaya, Spain.
11
Hospital General Universitario de Elda, Alicante, Spain.
12
University of Paris Diderot, Sorbonne Paris Cité, UFR de Médecine, INSERM, UMR 1132, Hôpital Lariboisière, AP-HP, Paris, France.
13
Amsterdam Rheumatology Immunology Center, Westfries Gasthuis, Hoorn, The Netherlands.
14
Italian Society for Rheumatology, Milan, Italy.
15
University and IRCCS Policlinico S. Matteo Foundation, Pavia, Italy.
16
Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan.
17
Taichung Veterans' General Hospital, Taichung, Taiwan.
18
University Hospital Carl Gustav Carus, Dresden, Germany.
19
Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.
20
Rijnstate Hospital, Arnhem, The Netherlands.
21
China Medical University and China Medical University Hospital, Taichung, Taiwan.
22
Copenhagen University Hospital, Glostrup, Denmark.
23
University of Genoa, Genoa, Italy.
24
Diakonhjemmet Hospital, Oslo, Norway.
25
Boston University School of Medicine, Boston, Massachusetts.

Abstract

OBJECTIVE:

To determine which clinical, laboratory, and imaging features most accurately distinguished gout from non-gout.

METHODS:

We performed a cross-sectional study of consecutive rheumatology clinic patients with ≥1 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 (two-thirds) and test sample (one-third). Univariate and multivariate association between clinical features and monosodium urate-defined gout was determined using logistic regression modeling. Shrinkage of regression weights was performed to prevent overfitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement.

RESULTS:

In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n = 653), the following features were selected for the final model: joint erythema (multivariate odds ratio [OR] 2.13), difficulty walking (multivariate OR 7.34), time to maximal pain <24 hours (multivariate OR 1.32), resolution by 2 weeks (multivariate OR 3.58), tophus (multivariate OR 7.29), first metatarsophalangeal (MTP1) joint ever involved (multivariate OR 2.30), location of currently tender joints in other foot/ankle (multivariate OR 2.28) or MTP1 joint (multivariate OR 2.82), serum urate level >6 mg/dl (0.36 mmoles/liter; multivariate OR 3.35), ultrasound double contour sign (multivariate OR 7.23), and radiograph erosion or cyst (multivariate OR 2.49). The final model performed adequately in the test set, with no evidence of misfit, high discrimination, and predictive ability. MTP1 joint involvement was the most common joint pattern (39.4%) in gout cases.

CONCLUSION:

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.

PMID:
25777045
PMCID:
PMC4573373
DOI:
10.1002/acr.22585
[Indexed for MEDLINE]
Free PMC Article

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