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Study for Updated Gout Classification Criteria: Identification of Features to Classify Gout
Author(s) -
Taylor William J.,
Fransen Jaap,
Jansen Tim L.,
Dalbeth Nicola,
Schumacher H. Ralph,
Brown Melanie,
Louthrenoo Worawit,
VazquezMellado Janitzia,
Eliseev Maxim,
McCarthy Geraldine,
Stamp Lisa K.,
PerezRuiz Fernando,
Sivera Francisca,
Ea HangKorng,
Gerritsen Martijn,
Scire Carlo,
Cavagna Lorenzo,
Lin Chingtsai,
Chou YinYi,
Tausche Anne Kathrin,
VargasSantos Ana Beatriz,
Janssen Matthijs,
Chen JiunnHorng,
Slot Ole,
Cimmino Marco A.,
Uhlig Till,
Neogi Tuhina
Publication year - 2015
Publication title -
arthritis care and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.032
H-Index - 163
eISSN - 2151-4658
pISSN - 2151-464X
DOI - 10.1002/acr.22585
Subject(s) - multivariate statistics , gout , multivariate analysis , tophus , medicine , logistic regression , febuxostat , hyperuricemia , uric acid , mathematics , statistics
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.