Whitney Dudley

Diagnosis of Gout: A Systematic Review in Support of an American College of Physicians Clinical Practice Guideline

Gout is the most common form of inflammatory arthritis (1). In its initial stages, gout is recognized by acute, intermittent episodes of synovitis presenting with joint swelling and pain (referred to as acute gouty arthritis, acute gout attacks, or acute gout flares) that may progress to chronic and persistent symptoms. Gout is the result of excess serum urate crystalizing in the body (as monosodium urate [MSU]). The time between flares is referred to as the intercritical period. Intermittent attacks may progress to more chronic symptoms as the result of either joint damage or chronic synovitis. In some persons, MSU may aggregate in intra- or extra-articular regions (for example, around tendons, in bone or bursa, or in other soft tissues) to form tophi. Monosodium urate crystals may directly stimulate the inflammasome in leukocytes, causing an acute inflammatory attack (2). Although the presence of MSU crystals in synovial fluid aspirated from the affected joint is sufficient for diagnosing gout, clinicians and researchers debate whether this approach is necessary. Classification and diagnostic algorithms that rely on other signs, symptoms, and laboratory tests without synovial fluid analysis exist. Joint aspiration may be technically difficult to perform and painful for the patient, particularly in smaller joints. Specimen handling and interpretation of synovial fluid analysis may be affected by experience and training. Moreover, the accuracy and utility of MSU assessment are affected by several factors (310). This review, conducted to support an American College of Physicians (ACP) clinical practice guideline, addresses the accuracy and safety of using clinical diagnostic or classification algorithms, dual-energy computed tomography (DECT), and ultrasonography for evaluating patients with gout symptoms compared with assessing MSU crystals in joint aspirate, which is considered the gold-standard diagnostic test. Methods We developed a protocol, followed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, and detailed our search and selection processes, inclusion criteria, and evidence tables in an evidence report (1113). Key Questions Key questions posed by ACP representatives were revised on the basis of input from a group of key informants, a technical expert panel, and public comments. Questions addressed in this article are as follows: What is the accuracy of clinical signs and symptoms and other diagnostic tests (such as serum urate analysis, ultrasonography, computed tomography, DECT, and plain radiography), alone or in combination, compared with synovial fluid analysis in diagnosing acute gouty arthritis in patients with no previous gout diagnosis? What are the adverse effects (including pain, infection at the aspiration site, and radiation exposure) or harms (related to false-positive, false-negative, or indeterminate results) associated with tests used to diagnose gout? Additional questions and evidence regarding the effects of practitioner type and other factors on successful joint aspiration and accuracy of interpretation are available in the full evidence report (12, 13). Data Sources and Searches We searched, without language restrictions, PubMed, EMBASE, the Cochrane Library, gray literature, and the Web of Science from inception through 29 February 2016 using the word gout combined with the terms for diagnostic methods (MSU crystal analysis, joint aspiration, DECT, ultrasound, and x-ray), clinical signs and symptoms, and outcome measures, without filters specific for the diagnostic tests, as recommended (14). Supplement Table 1 shows the search methodology. We also obtained relevant references from a search conducted for a simultaneous review on gout management, considered studies suggested by experts, searched ClinicalTrials.gov and the Web of Science for recently completed studies and unpublished or nonpeer-reviewed study findings, and contacted manufacturers of equipment and laboratory test kits used to diagnose gout for unpublished data specific to their use for gout diagnosis. Supplement. Supplemental Tables Study Selection Titles and abstracts identified by the literature searches were screened by 2 reviewers, who independently conducted a full-text review of all selections to exclude articles that reported only on the incidence or prevalence, risk factors, or treatment of gout; included persons younger than 18 years; provided no usable data (sensitivities and specificities or data that could be used to calculate them); reported the same data as another article; enrolled only participants with established gout diagnoses; or did not clearly indicate the use of a recognized diagnostic standard. If necessary, disagreements regarding inclusion at the full-text stage were reconciled with the project leaders input. We included original prospective and cross-sectional studies that assessed the accuracy (sensitivity and specificity) or safety of tests used to diagnose gout in persons with no prior definitive gout diagnosis who presented with joint inflammation, and in which the reference standard was MSU analysis or a combination of MSU analysis, American Rheumatism Association (ARA) (now the American College of Rheumatology [ACR]) criteria for gout diagnosis, and tests to confirm or rule out other causes of inflammatory arthritis. Studies that enrolled patients with asymptomatic hyperuricemia were excluded. To assess safety, we also included case reports and case series. Data Extraction and Quality Assessment Two reviewers abstracted study-level details from articles accepted for inclusion. Outcomes (sensitivity, specificity, and positive and negative predictive value [PPV and NPV]) were singly abstracted and verified by another reviewer. Risk of bias (study quality) of each included study was assessed independently by 2 reviewers using the QUADAS-2 (Revised Quality Assessment of Diagnostic Accuracy Studies) tool (15, 16). Supplement Table 2 includes the results of the quality assessment. Disagreements regarding study details were reconciled by group discussion, and those related to study quality were mediated by the project leader. Data Synthesis and Analysis We organized our narrative descriptions of evidence, which focused on study quality, settings, and findings, according to categories of tests, as well as chronologically. If several studies compared similar tests with the same reference standard, we used bivariate metaregression to pool studies (17). As a group, the reviewers assessed the overall strength of evidence (SOE) for each major comparison and outcome as high, moderate, low, or insufficient using guidance suggested by the Effective Health Care Program (18). Role of the Funding Source This topic was nominated by the ACP to the Agency for Healthcare Research and Quality (AHRQ) and funded by AHRQ. Staff at AHRQ and ACP helped to develop and refine the scope of the study and reviewed the draft report but had no role in data extraction, synthesis, or rating of evidence. Results The Figure depicts the search and selection process that identified 22 total articles addressing the accuracy (n= 21) or safety (n= 3) of various diagnostic methods. Characteristics and findings of the studies are detailed in Supplement Table 3. Figure. Literature flow diagram. Validity of Clinical Classification and Diagnostic Criteria Twelve studies examined diagnostic or classification algorithms for diagnosing gout (1930). Of these 12 studies, 11 compared the predictions from 10 clinical algorithms (described in Table 1) with assessment of synovial fluid MSU crystals in all enrolled patients (1923, 2530). Study quality was good for all but 2 studies (19, 23). All studies were conducted in academic rheumatology departments, although several purposely enrolled patients who were referred by primary care physicians. The ARAs algorithm included joint fluid culture to rule out the co-occurrence of septic arthritis (19). Sensitivities and specificities for the clinical algorithms varied (compared with MSU analysis as the reference standard) (Table 2). Table 1. Summary of Components of Clinical Algorithms for Diagnosing Gout* Table 2. Summary of Findings Rome and New York Criteria Three studies found that the Rome and New York criteriathe first sets of clinical criteria developed to classify gouthad limited sensitivity (70% to 80%) and specificity (79% to 80%) for diagnosing gout compared with identifying MSU crystals in affected joints (20, 27, 30). ARA Criteria In 1977, in a high-quality study using clinical characteristics of 706 patients seen in 38 rheumatology clinics across the United States, an ARA subcommittee developed an algorithm to classify gout for research purposes (19). The final algorithm included 12 clinical signs (not including MSU analysis or presence of tophi, which were considered diagnostic by themselves), with the presence of 6 or more of these signs required to classify a patient as having gout. The initial assessment reported sensitivities of 96%, 88%, and 74%, and specificities of 73% to 93%, 89% to 99%, and 97% to 100% for 5 or more, 6 or more, and 7 or more positive criteria, respectively. Only 47% of participants underwent MSU testing, with physician judgment serving as the reference standard for those not tested. Five subsequent tests of the ARA criteria were done in groups of patients with suspected gout, all of whom underwent MSU analysis (19, 20, 22, 27, 30). These studies, which enrolled 82 to 983 patients, reported sensitivities of 70% to 92% and specificities of 53% to 92% (Table 2). Positive predictive value ranged from 66% to 92%, and NPV from 69% to 86% (study quality was high for 2 studies and moderate for the other 2). In 1 study, among the patients with false-positive results, 50% had deposits of calcium pyrophosphate crystals (20). Two studies compared the sensitivity and specificity of the algorithm between patients with recent onset