Comorbidity Influences the Comparative Safety of Biologic Therapy in Older Adults With Inflammatory Bowel Diseases
Bibliographic record
Abstract
INTRODUCTION: There are limited data on comparative risk of infections with various biologic agents in older adults with inflammatory bowel diseases (IBDs). We aimed to assess the comparative safety of biologic agents in older IBD patients with varying comorbidity burden. METHODS: We used data from a large, national commercial insurance plan in the United States to identify patients 60 years and older with IBD who newly initiated tumor necrosis factor-α antagonists (anti-TNF), vedolizumab, or ustekinumab. Comorbidity was defined using the Charlson Comorbidity Index (CCI). Our primary outcome was infection-related hospitalizations. Cox proportional hazards models were fitted in propensity score-weighted cohorts to compare the risk of infections between the different therapeutic classes. RESULTS: The anti-TNF, vedolizumab, and ustekinumab cohorts included 2,369, 972, and 352 patients, respectively, with a mean age of 67 years. The overall rate of infection-related hospitalizations was similar to that of anti-TNF agents for patients initiating vedolizumab (hazard ratio [HR] 0.94, 95% confidence interval [CI] 0.84-1.04) and ustekinumab (0.92, 95% CI 0.74-1.16). Among patients with a CCI of >1, both ustekinumab (HR: 0.66, 95% CI: 0.46-0.91, p-interaction <0.01) and vedolizumab (HR: 0.78, 95% CI: 0.65-0.94, p-interaction: 0.02) were associated with a significantly lower rate of infection-related hospitalizations compared with anti-TNFs. No difference was found among patients with a CCI of ≤1. DISCUSSION: Among adults 60 years and older with IBD initiating biologic therapy, both vedolizumab and ustekinumab were associated with lower rates of infection-related hospitalizations than anti-TNF therapy for those with high comorbidity burden.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".