ABS0783 DIAGNOSTIC DELAY IN SYSTEMIC AUTOINFLAMMATORY DISEASE: PRELIMINARY RESULTS FROM THE EUROFEVER REGISTRY
Bibliographic record
Abstract
Background: Systemic Autoinflammatory Diseases (SAIDs) are an umbrella term that consists of monogenic and polygenic or multifactorial origin disorders. These are rare diseases with the estimated prevalence lowest as 1-3/ 1,000,000. Most of the SAIDs have an early disease onset, so that many patients present in childhood or adolescence. These complex patients often see many medical practitioners over time, resulting in fragmented care, emergency room visits and hospitalizations leading to diagnostic delays. Even though the early diagnosis is essential to prevent mortality and life-long complications, the rarity of the SAIDs and the variety of clinical spectrum limit the understanding of potential diagnosis for health-care professionals [1]. Objectives: We aimed to identify and evaluate possible factors related with diagnostic delay in selected SAIDs from the Eurofever database. Methods: The most common monogenic SAIDs, including Familial Mediterranean Fever (FMF), cryopyrin-associated periodic syndromes (CAPS), Tumor Necrosis Factor Receptor-Associated Periodic Syndrome (TRAPS) and Mevalonate Kinase Deficiency (MKD), were selected for this study. Data on diagnostic delay is retrieved from the Eurofever database and defined as the time between symptom onset and diagnosis. Ages at disease onset and years of diagnostic delay were reported as median and interquartile range. Mann Whitney U and Kruskal Wallis tests were used to compare groups where appropriate. Results: In total, 2249 patients (1519, FMF; 271, TRAPS; 254, CAPS; 205, MKD) from 32 countries were included in the study. 51.9% (n=1168) were male and 64.4% were Caucasian-European (n=1448). The median age at disease onset was 3.1[IQR 1-7.8] overall and, 3.6 [IQR 1.6-8] for FMF, 4.4 [IQR 1-13.4] for TRAPS, 1.2 [IQR 0-6.1] for CAPS and 0 [IQR 0-2.6] for MKD. The median age at diagnosis was 8 [IQR 4.1-16.5] overall and, 7 [IQR 4-13.2] for FMF, 16.1 [IQR 6.2-38.4] for TRAPS, 12.3 [IQR 4.3-30.5] for CAPS and 7.4 [IQR 3.7-15.2] for MKD. The median diagnostic delay in years was 2.9 [IQR 1-8.3] overall and, 2.3 [IQR 0-5.7] for FMF, 5.8 [IQR 1.4-21] for TRAPS, 5.8 [IQR 2-20] for CAPS and 5 [IQR 1.6-14.2] for MKD. Frequency of having at least one concomitant disease was 17.7% (n=399) and 14.9% (n=334) had at least one complication. Diagnostic delay was shorter in patients with FMF compared to others (p<0.001). There was no statistically significant difference in diagnostic delay between males and females (2.9 [IQR 1-8.2] and 3 [IQR 1-8.7] years, respectively, p=0.82). However, diagnostic delay was significantly higher in patients with at least one concomitant disease (4.2 [IQR 1.4-10.2] vs 2.7 [IQR 1-7.9] years, p<0.001) and patients with at least one complication (8.2 [IQR 2.4-24.2] vs 2.6 [IQR 1-6.5] years, p<0.001). Conclusion: FMF patients have a shorter diagnostic delay compared to other SAIDs, which could be explained by the familiarity of the disease. Patients with concomitant disease and complications have longer diagnostic delay compared to others. One possible explanation could be that having concomitant diseases may confound physician's decision process. The next step of the project is to create a subset of patients fulfilling the Eurofever classification criteria and further identify and evaluate possible individual-, disease-, and country-level factors associated with diagnostic delay. REFERENCES: [1] Romano M, Arici ZS, Piskin D etal. The 2021 EULAR/American College of Rheumatology points to consider for diagnosis, management and monitoring of the interleukin-1 mediated autoinflammatory diseases: cryopyrin-associated periodic syndromes, tumour necrosis factor receptor-associated periodic syndrome, mevalonate kinase deficiency, and deficiency of the interleukin-1 receptor antagonist. Ann Rheum Dis. 2022 Jul;81(7):907-921. doi: 10.1136/annrheumdis-2021-221801. Epub 2022 May 27. PMID: 35623638. Acknowledgements: NIL . Disclosure of Interests: None declared . © 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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".