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Rigshospitalet - a part of Copenhagen University Hospital
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Study for Updated Gout Classification Criteria: Identification of Features to Classify Gout

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DOI

  1. Ultrasound for the diagnosis of gout-the value of gout lesions as defined by the Outcome Measures in Rheumatology ultrasound group

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Gradual reduction of tophaceous deposits during urate-lowering therapy

    Research output: Contribution to journalEditorialpeer-review

  3. Reversibel toføs polyartikulær artritis urica

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Assessing the sensitivity to change of the OMERACT ultrasound structural gout lesions during urate-lowering therapy

    Research output: Contribution to journalJournal articleResearchpeer-review

  5. Dual-energy CT in gout patients: Do all colour-coded lesions actually represent monosodium urate crystals?

    Research output: Contribution to journalJournal articleResearchpeer-review

  • William J Taylor
  • Jaap Fransen
  • Tim L Jansen
  • Nicola Dalbeth
  • H Ralph Schumacher
  • Melanie Brown
  • Worawit Louthrenoo
  • Janitzia Vazquez-Mellado
  • Maxim Eliseev
  • Geraldine McCarthy
  • Lisa K Stamp
  • Fernando Perez-Ruiz
  • Francisca Sivera
  • Hang-Korng Ea
  • Martijn Gerritsen
  • Carlo Scire
  • Lorenzo Cavagna
  • Chingtsai Lin
  • Yin-Yi Chou
  • Anne Kathrin Tausche
  • Ana Beatriz Vargas-Santos
  • Matthijs Janssen
  • Jiunn-Horng Chen
  • Ole Slot
  • Marco A Cimmino
  • Till Uhlig
  • Tuhina Neogi
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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.

Original languageEnglish
JournalArthritis Care & Research
Volume67
Issue number9
Pages (from-to)1304-15
Number of pages12
ISSN2151-464X
DOIs
Publication statusPublished - Sep 2015

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