762 Comorbidities and Opioid Use Among Patients With Crohn's Disease
Bibliographic record
Abstract
INTRODUCTION: Comorbidities associated with Crohn's disease (CD) add to the symptoms of disease and may contribute to substantial disease burden. There is a lack of information on the development of comorbidities in CD patients in the real world. This study aims to compare comorbidities, including opioid use, in CD patients relative to patients without inflammatory bowel disease (IBD), using a large database and long-term follow-up. METHODS: This was a retrospective matched cohort study. Working age adults with CD (≥2 independent claims with a CD diagnosis ≥30 days apart and within 1 year; first CD diagnosis was the index date) and controls without diagnosis for IBD (random index date) were identified in a US healthcare claims database (OptumHealth Care Solutions, Inc.) of privately-insured patients (01/1999-03/2017). CD patients were matched 1:5 with non-IBD controls using baseline characteristics, including the Quan-Charlson comorbidity index and cardiovascular disease. Comorbidities during the 12-month baseline period before the index date were reported. During the follow-up, lasting at least 12 months, the incidence of new comorbidity, including opioid use as a proxy for pain, was compared to the non-IBD controls by Kaplan-Meier (KM) rate with log-rank test. RESULTS: The study sample was composed of 6,715 CD patients and 33,575 non-IBD matched patients (average age of 45 years old, and 54% female). Pain, anemia, and fatigue were significantly higher in the CD cohort at baseline (Figure 1). Over the follow-up, new cases of cardiovascular disease were significantly higher in the CD compared to the matched non-IBD cohort with a rate of 11.5 vs 7.8%, respectively, by year 6 (Figure 2). In addition, the rates of new cases of fatigue (5.3 vs 3.2%), anemia (6.0 vs 1.9%), respiratory disease (3.5 vs 1.8%), and anxiety (5.3 vs 2.8%) were also significantly higher in the CD cohort compared to the non-IBD cohort by year 6 (log-rank P -value < 0.001). When new cases of pain diagnosis or opioid use were analyzed, the rate was significantly higher in the CD cohort, reaching 74.0% compared to 59.1% in the non-IBD cohort at year 6 (Figure 3). CONCLUSION: In this study, comorbidities, in particular new cases of pain diagnosis or opioid use, were significantly higher in CD patients relative to non-IBD patients, suggesting that the development of comorbidities may contribute to the long-term disease burden of CD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".