Study for updated gout classification criteria: Identification of features to classify gout

© 2015, American College of Rheumatology. 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. G...

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Main Authors: 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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Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/54824
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spelling th-cmuir.6653943832-548242018-09-04T10:24:13Z Study for updated gout classification criteria: Identification of features to classify gout 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 Medicine © 2015, American College of Rheumatology. 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. 2018-09-04T10:24:13Z 2018-09-04T10:24:13Z 2015-01-01 Journal 21514658 2151464X 2-s2.0-84940107702 10.1002/acr.22585 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84940107702&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54824
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Medicine
spellingShingle Medicine
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
Study for updated gout classification criteria: Identification of features to classify gout
description © 2015, American College of Rheumatology. 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.
format Journal
author 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
author_facet 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
author_sort William J. Taylor
title Study for updated gout classification criteria: Identification of features to classify gout
title_short Study for updated gout classification criteria: Identification of features to classify gout
title_full Study for updated gout classification criteria: Identification of features to classify gout
title_fullStr Study for updated gout classification criteria: Identification of features to classify gout
title_full_unstemmed Study for updated gout classification criteria: Identification of features to classify gout
title_sort study for updated gout classification criteria: identification of features to classify gout
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84940107702&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/54824
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