P176 A retrospective analysis of aortitis cases from radiology reports in a London teaching hospital: implications for screening and management
Bibliographic record
Abstract
Abstract Background Aortitis is a heterogeneous rare condition causing aortic inflammation, often due to infectious or autoimmune aetiology. Its presentation, diagnosis and optimal management remain poorly understood, often requiring multidisciplinary input. With the advent of improved imaging techniques, an increasing number of radiology reports are identifying features of vasculitis necessitating clinical investigation. We investigated how cases identified on imaging with features of aortitis reflected clinical diagnosis and treatment. We further examined whether radiological detection of aortitis enables more rapid management decisions to improve outcomes. Methods A search was conducted of all radiology reports from St George’s Hospital between 2008 and 2018 for key words indicating “aortitis”, including ‘hyperintense vessel uptake’ and ‘periaortic inflammation.’ From 15,963 initial results, 80 reports were identified with radiological suspicion of aortitis. Results Of 80 cases, 36/80 were diagnosed with autoimmune or idiopathic inflammatory pathology: 9 retroperitoneal fibrosis, 8 idiopathic, 7 Takayasu’s, 4 giant cell arteritis, 2 inflammatory aneurysms, 2 Behçet's, 2 IgG4 disease, 1 lupus, 1 granulomatosis with polyangiitis. Diagnosis of autoimmune or idiopathic aortitis was based on symptomatology, imaging, serology, vessel biopsy, and treatment response. 14/80 had infective aortitis: 11 had positive microbiology and 3 demonstrated empirical antibiotic response. Table 1 shows demographics. 12/80 had atheromatous disease. 18/80 were not further investigated due to comorbidity, lack of correlation with symptoms, or absence of follow-up. Initial imaging modalities with the highest yield were computed tomography (CTs) with aorta protocols and CTs of thorax/abdomen/pelvis. The most useful radiology report terms included: aortitis, periaortic inflammation, Takayasu’s, and hyperintense vessel uptake. Conclusion Our study is the first retrospective case analysis, to our knowledge, of more than 15,000 radiology reports used as a starting point to evaluate for aortitis, identifying a large dataset with a broad case-mix. It contrasts with current literature identifying cases histologically and post-operatively. We found that radiology can be a useful early alert for possible diagnoses which require further assessment. Consequently, we developed an alerting system within our radiology department based on the search terms and imaging modalities identified. This links to a multidisciplinary meeting including vascular and rheumatology, so highlighted cases are discussed early. Disclosures K. Townsend: None. G. Cattini: None. K. Moss: None. K. Stenson: None. P. Holt: None. R. Morgan: None. N. Sofat: None.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".