Kam Shojania: No conflict of interest. Has been involved with: Augurex, Pfizer, UCB, Abbvie, Janssen, Roche, and Celgene.
Mitigating Potential Bias
- Recommendations are consistent with published guidelines.
- Recommendations are consistent with current practice patterns.
- Treatments or recommendations in this article are unrelated to products/services/treatments involved in disclosure statements.
Neda Amiri: No dislosures.
What I did before
Gout is one of the most common forms of arthritis, affecting 1.4% of the population (approximately 6.1 million in the US) (1). Primary care physicians diagnose and manage most patients with gout. It is estimated that less than 10% of patients diagnosed as gout are referred to rheumatologists (2). While the gold standard for diagnosing gout is visualization of monosodium urate crystals (MSU) in joint fluid under polarization microscopy (3), this is not always done.
As a rheumatologist, patients often present after the acute episode has settled. Furthermore, joint fluid analysis requires polarization microscopy, which may not be immediately available. In these cases, physicians rely on clinical signs and symptoms to make the diagnosis. However, there has been limited data on validity of doing so. Therefore, there has been a need to examine the validity of gout diagnosis in primary care settings and design a predictive algorithm that would allow physicians to make a diagnosis of gout without synovial fluid analysis.
What changed my practice
In 2010, Janssen and colleagues performed a prospective diagnostic study of patients with acute monoarthritis by Dutch family physicians (FP) (4). In eligible patients, gout diagnosis by FP was evaluated using 2×2 tables, with the presence of MSU crystals as the reference test. Thereafter, prediction models were developed and validated in patients with gout by FP diagnosis using multivariate logistic regression analysis linking the presence of MSU crystals to clinical variables.
Of the 328 patients included in the cohort, the FP clinical diagnosis of gout yielded a sensitivity of 97%, specificity of 28%, positive predictive value of 64% and negative predictive value of 87%
The authors identified seven variables that could identify patients with gout with a weighted score with a maximal clinical score of 13. This included male sex (2 points), previous patient-reported arthritis attack (2 points), onset within 1 day (0.5 points), joint redness (1 point), 1st MTP involvement (1.5 points), hypertension or one or more cardiovascular diseases (angina, myocardial infarction, cerebrovascular accidents, heart failure, transient ischemic attacks, or peripheral vascular disease) (1.5 points), and serum uric acid level exceeding 350 mmol per litre (3.5 points)
A score of ≤4 ruled out gout in almost 100% of patients (97%), whereas in patients with score ≥8, equated to diagnosis of gout in more than 80% of patients (false positive rate of 17%, comparing to FP false-positive diagnosis of 36.3%). However, clinical uncertainty remained for patients with midrange scores between 4-8 (approximately 15% of the patients in the study). Incidentally, the authors noted that presence of tophus always coincided with presence of MSU crystals.
By using the clinical scoring system, the positive predictive value of the clinical diagnosis improved from 67% to 87%, and the negative predictive value from 87% to 95%.
In a follow up study published in the journal of Rheumatology in September 2014, Kienhorst and colleagues (5), aimed to validate the same model in patients seen in the rheumatology department. Again, in a prospective 2-year study in Netherlands, authors identified patients referred to rheumatology department for evaluation of possible gout. Information on the seven above mentioned variables were obtained, and joint fluid analysis was done (If no joint fluid was obtained, patients were classified as having non-gout; if joint fluid did not demonstrated MSU crystals, further blood work, including Rheumatoid Factor, Anti-CCP, ANA were done to determine the etiology of arthritis. Follow up was stopped, when definite diagnosis of another rheumatic disease – other than gout – was established).
Similar to the original study, the authors showed validity of their prediction model in this secondary population as well.
What I do now
In clinical practice, when assessing patients with acute monoarthritis, I first aim to rule out septic arthritis via a joint aspiration if I am concerned. In addition to assessing the patients for systemic features, I remain mindful of risk factors that predispose patients to septic arthritis including increased age, history of rheumatoid arthritis, skin infection, joint prosthesis (6), immunosuppression or atypical presentation.
In those whom I suspect gout, I evaluate the presence of seven clinical and laboratory features:
- Male Sex (2 points)
- Previous patient reported arthritis attach (2 points)
- Onset within 1 day (0.5 points)
- Joint redness (1 point)
- Involvement of the 1st MTP joint (2.5 points)
- Hypertension or >1 Cardiovascular diseases* (1.5 points)
- Serum uric acid >350 mmol/L (3.5 points)
*angina, myocardial infarction, cerebrovascular accidents, heart failure, transient ischemic attacks, or peripheral vascular disease
In patients who score greater than 8 points on the above scale, gout is present in more than >87%. I manage these patients as having gout per recommended guidelines (7), using NSAIDs, Colchicine, and urate lowering therapy where appropriate. I will also assess them for gout associated cardiovascular and renal disease. If they fail to improve on the above treatment, I will reassess the diagnosis, or obtain a joint fluid analysis.
Conversely, in patients who score less than 4 on the above model, I evaluate them for alternate diagnoses including CPPD (pseudogout), septic arthritis, osteoarthritis, or other inflammatory arthritis.
Finally, I recognize the diagnostic uncertainty that remains in patients who score between 4-8 as per the algorithm, and when possible do a joint fluid analysis, or investigate other diagnosis for arthritis including the differential listed above, based on historical and physical exam features.
- Annemans L, Spaepen E, Gaskin M, et al. Gout in the UK and Germany: prevalence, comorbidities and management in general practice 2000-2005. Ann Rheum Dis. 2008; 2007;67:960-966. (View)
- Pal B, Foxall M, Dysart T, Carey F, Whittaker M. How is gout managed in primary care? A review of current practice and proposed guidelines. Clin Rheumatol. 2000;19:21-25. (View with CPSBC or UBC)
- Zhang W, Doherty M, Pascual E, et al. EULAR evidence based recommendations for gout. Part I: Diagnosis. Report of a task force of the standing committee for international clinical studies including therapeutics (ESCISIT). Ann Rheum Dis. 2006;65:1301-1311. (View)
- Janssens HJ, Fransen J, van de Lisdonk EH, van Riel PM, van Weel C, Janssen M. A diagnostic rule for acute gouty arthritis in primary care without joint fluid analysis. Arch Intern Med. 2010; 170: 1120-1126. (Request from CPSBC or view with UBC)
- Kienhorst LB, Janssens HJ, Fransen J, et al. The validation of a diagnostic rule for gout without joint fluid analysis: a prospective study. Rheumatol. 2014 [epub ahead of print]. (View)
- Bent S, Moore D, Kohlwes J, Margaretten ME. Does this adult patient have septic arthritis? JAMA. 2007;297:1478-1488. (Request from CPSBC or view with UBC)
- Khanna D, Khanna PP, Fitzgerald JD, et al. 2012 American College of Rheumatology guidelines for management of gout. Part 2: therapy and antiinflammatory prophylaxis of acute gouty arthritis. Arthritis Care & Research. 2012;64:1447-1461. (View)