Risk of Developing Additional Immune-Mediated Manifestations: A Retrospective Matched Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Immune-mediated inflammatory diseases (IMIDs) cause significant impairment in quality of life. Although they share similar genetic factors, environmental precipitants, and pathophysiological mechanisms, there is little evidence on the risk of developing subsequent IMIDs after an initial IMID diagnosis. We sought to assess the risk of developing subsequent IMIDs among patients diagnosed with an initial IMID. METHODS: This retrospective matched cohort study used a large US commercial health insurance claims database (01/01/2006-09/30/2015). The risks of developing secondary IMIDs among patients aged 18-64 years with a diagnosis of one of nine IMIDs of interest (ankylosing spondylitis, celiac disease, hidradenitis suppurativa [HS], inflammatory bowel disease, lupus, psoriatic arthritis [PsA], psoriasis, rheumatoid arthritis, and uveitis) as identified from diagnosis codes on medical claims were compared with up to 1000 matched controls without the primary IMID using Cox proportional hazards models. RESULTS: Across the nine IMIDs of interest, there were 398,935 unique case patients matched to 256,795,796 non-unique control patients. Case patients with an initial IMID had higher risks of developing each, any one, and any two of the other eight secondary IMIDs compared to their matched controls. Hazard ratios [95% confidence intervals] for the risk of developing any one secondary IMID ranged from 5.4 [5.0, 5.8] (initial IMID: HS) to 62.2 [59.9, 64.6] (initial IMID: PsA), and hazard ratios for developing any two secondary IMIDs ranged from 3.0 [2.3, 3.8] (HS) to 75.2 [69.3, 81.7] (PsA). CONCLUSIONS: This study demonstrates that the risk of developing a second IMID is significantly higher for individuals who have already experienced a first IMID in a large and contemporary US claims database. Certain pairs of IMIDs co-occur more frequently than others. The risk of developing subsequent IMIDs may be an important consideration for clinicians when selecting treatment strategies. FUNDING: Abbvie.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.000 |
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".