Incidence, outcomes, and impact of COVID‐19 on inflammatory bowel disease: propensity matched research network analysis
Bibliographic record
Abstract
BACKGROUND: Accurate estimates for the risk of COVID-19 in IBD, and an understanding of the impact of COVID-19 on IBD course and the risk of incident post-infectious IBD are needed. AIMS: To estimate the risk of COVID-19 in IBD and study its impact on IBD course and the risk of incident post-infectious IBD. METHODS: A retrospective propensity score matched cohort study utilising multi-institutional research network TriNetX. COVID-19 patients with and without IBD were identified to quantify the risk of COVID-19 in patients with IBD, COVID-19 outcomes in patients with IBD and the impact of COVID-19 on IBD disease course. The risk of incident post-infectious IBD in COVID-19 patients was compared to the population not infected with COVID-19 during a similar time period. RESULTS: Incidence rate ratio for COVID-19 was lower in IBD patients compared to the non-IBD population (0.79, 95% CI: 0.72-0.86). COVID-19-infected patients with IBD were at increased risk for requiring hospitalisation compared to non-IBD population (RR: 1.17, 95% CI: 1.02-1.34) with no differences in need for mechanical ventilation or mortality. Patients with IBD on steroids were at an increased risk for critical care need (RR: 2.22, 95% CI: 1.29-3.82). Up to 7% of patients with IBD infected with COVID-19 suffered an IBD flare 3-months post-infection. Risk for incident IBD post-COVID was lower than that seen in the non-COVID population (RR: 0.64, 95% CI: 0.54-0.65). CONCLUSION: We observed no increase in risk for COVID-19 amongst patients with IBD or risk for de novo IBD after COVID-19 infection. We confirmed prior observations regarding the impact of steroid use on COVID-19 severity in patients with IBD.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".