POS1297 DIAGNOSTIC ACCURACY AND TRAJECTORIES OF REFERRALS FOR GOUT TO RHEUMATOLOGY
Bibliographic record
Abstract
Background: Prior studies delineating suboptimal quality of gout care have focused on treatment benchmarks. It is unknown, however, whether inaccurate gout diagnoses could be contributing to care gaps in gout management. This research question is important, as an accurate diagnosis of gout is critical for implementing an appropriate treatment plan. Objectives: We aimed to evaluate the diagnostic accuracy and trajectories of referrals for gout to rheumatology, as well as factors associated with an accurate diagnosis of gout by the referring provider. Methods: We performed a retrospective cohort study at the Division of Rheumatology at William Osler Health System, a hybrid community and academic health sciences centre in Brampton, Ontario, Canada with a catchment population of over 1.3 million people, staffed by 4 full-time rheumatologists focusing on general adult rheumatology. All referrals seen in consultation from December 2019 to January 2023 were retrieved for referring diagnoses, patient demographics and referring physician information. Subsequently, we identified and descriptively analyzed all referrals specifically for gout. The accuracy of the referring provider's initial diagnosis for gout was referenced to the rheumatologist's "gold standard" post-assessment primary diagnosis at the visit closest to 12-months after initial consultation, by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Cohen's kappa (κ) coefficient, cumulatively and stratified by referring specialty. Final alternative diagnoses made by the consultant rheumatologist, other than gout, were descriptively analyzed. Using multivariable logistic regression analysis, we identified clinical factors associated with an accurate diagnosis of gout by the referring provider. Results: During the study timeframe, 4,315 patients were seen in initial consultation, with 81.8% of referrals from primary care providers and 216 patients ultimately diagnosed with gout. Compared to referrals for other reasons (n=4,124), patients referred for gout (n=191, 4.4% of all referrals) were more likely to be male (77.0% versus 29.4%, p<0.001) and older (58.4 versus 53.3 years old, p<0.001). Primary care providers (72.8%), emergency room physicians (10.5%), internists (7.3%) and nephrologists (4.5%) referred the majority of patients for gout (181 patients or 94.8% cumulatively). Of the 191 referrals for gout, 159 (83.2%) were ultimately diagnosed with gout by the consultant rheumatologist, with alternative final diagnoses being osteoarthritis (9.4%), rheumatoid arthritis (2.6%), autoimmune inflammatory arthritis (1.6%), psoriatic arthritis (1.0%), calcium pyrophosphate deposition disease (0.5%), calcinosis cutis (0.5%), reactive arthritis (0.5%) and regional musculoskeletal disorders (0.5%). Compared to a "gold standard" rheumatologist diagnosis for gout, referring physicians had moderate-to-high sensitivity (73.6%, 95% CI: 67.2 to 79.4), specificity (99.2%, 95% CI: 98.9 to 99.5), PPV (83.2%, 95% CI: 77.2 to 88.2), NPV (98.6%, 95% CI: 98.2 to 99.0) and inter-rater reliability as measured by the Cohen's κ coefficient (0.77, 95% CI: 0.72 to 0.82). Internists had the highest sensitivity for gout diagnoses (84.6%, 95% CI: 54.6 to 98.1) and Cohen's κ coefficient (0.80, 95% CI: 0.62 to 0.97) while emergency room physicians had the highest PPV (95.0%, 95% CI: 75.1 to 99.9). Cumulative and stratified specificities and NPVs were high, driven by the large agreement in negative diagnoses for gout (Table 1). In multivariable logistic regression analysis, male sex (OR 14.32, 95% CI: 4.44 to 46.17, p<0.001), serum uric acid ≥500 µmol/L (OR 9.10, 95% CI: 2.19 to 37.78, p=0.002), lower extremity monoarticular involvement (OR 5.08, 95% CI: 1.59 to 16.27, p=0.006), and symptom duration ≤2 weeks (OR 3.87, 95% CI 1.23 to 12.21, p=0.021) were associated with a final gout diagnosis by the consultant rheumatologist, among all referrals for gout (Table 2). Conclusion: In a large general rheumatology cohort, referring providers had reasonably high accuracy in diagnosing gout, with acute care specialties including internal medicine and emergency medicine having the highest sensitivities and positive predictive values respectively. Traditional gout risk factors were associated with a concordant gout diagnosis with the consultant rheumatologist. Our results suggest that care gaps in gout care are likely not at point of diagnosis. Future applications stemming from the high degree of referring provider accuracy and robust predictors for gout diagnoses may lie in the development of rapid access gout clinics to facilitate triaging of patients and improve access to specialized gout care, specifically focusing on mitigating treatment care gaps. REFERENCES: NIL . Table 1. Diagnostic characteristics of referrals for gout, cumulatively and stratified by referring specialty. Table 2. Multivariable logistic regression model for predictors of a final gout diagnosis, among all referrals for gout (n=191) Acknowledgements: NIL . Disclosure of Interests: Timothy Kwok Novartis, speaker honorarium for journal club, sangeeta bajaj: None declared, Tripti Papneja: None declared, Vandana Ahluwalia: None declared, Gregory Choy: None declared, Raman Joshi Abbvie, Amgen, Celltrion, Eli Lilly, Frenius Kabi, Novartis, Pfizer, Sandoz, Sobi and UCB. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".