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Identification of patients with gout: elaboration of a questionnaire for epidemiological studies
  1. P Richette1,2,
  2. P Clerson3,
  3. S Bouée4,
  4. G Chalès5,
  5. M Doherty6,
  6. R M Flipo7,
  7. C Lambert8,
  8. F Lioté1,2,
  9. T Poiraud9,
  10. T Schaeverbeke10,
  11. T Bardin1
    1. 1Université Paris Diderot, UFR médicale, Paris, France; Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiére, Fédération de Rhumatologie, Paris, Cedex, France
    2. 2INSERM 1132, Université Paris-Diderot, Hôpital Lariboisière, Paris, France
    3. 3Orgamétrie Biostatistiques, Roubaix, France
    4. 4Cemka-Eval, Bourg La Reine, France
    5. 5Service de rhumatologie, Hôpital Sud, CHU Rennes, Université de Rennes-1, Rennes, Cedex, France
    6. 6Academic Rheumatology, University of Nottingham, City Hospital, Nottingham, UK
    7. 7Service de Rhumatologie, Université de Lille 2, Hôpital Roger-Salengro, CHRU de Lille
    8. 8Département médical, Ipsen, Boulogne, France
    9. 9Département médical, Ménarini, Rungis, France
    10. 10Département de Rhumatologie, Hôpital Pellegrin, CHU de Bordeaux, place Amélie-Raba-Léon, Bordeaux, France
    1. Correspondence to Professor Pascal Richette, Fédération de Rhumatologie, Hôpital Lariboisière, 2 Rue Ambroise Paré, Paris 75475, Cedex 10, France; pascal.richette{at}, thomas.bardin{at}


    Objectives In France, the prevalence of gout is currently unknown. We aimed to design a questionnaire to detect gout that would be suitable for use in a telephone survey by non-physicians and assessed its performance.

    Methods We designed a 62-item questionnaire covering comorbidities, clinical features and treatment of gout. In a case–control study, we enrolled patients with a history of arthritis who had undergone arthrocentesis for synovial fluid analysis and crystal detection. Cases were patients with crystal-proven gout and controls were patients who had arthritis and effusion with no monosodium urate crystals in synovial fluid. The questionnaire was administered by phone to cases and controls by non-physicians who were unaware of the patient diagnosis. Logistic regression analysis and classification and regression trees were used to select items discriminating cases and controls.

    Results We interviewed 246 patients (102 cases and 142 controls). Two logistic regression models (sensitivity 88.0% and 87.5%; specificity 93.0% and 89.8%, respectively) and one classification and regression tree model (sensitivity 81.4%, specificity 93.7%) revealed 11 informative items that allowed for classifying 90.0%, 88.8% and 88.5% of patients, respectively.

    Conclusions We developed a questionnaire to detect gout containing 11 items that is fast and suitable for use in a telephone survey by non-physicians. The questionnaire demonstrated good properties for discriminating patients with and without gout. It will be administered in a large sample of the general population to estimate the prevalence of gout in France.

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