S819 Incidence, Outcomes, and Impact of COVID-19 on Inflammatory Bowel Disease: Propensity Matched Analysis of a Large Research Network
Bibliographic record
Abstract
Introduction: Several studies have examined outcomes of COVID-19 in patients with IBD however these reports have inherent limitations including a lack of matched controls precluding accurate assessment of risk, and lack of follow up data to assess for IBD flares and risk of de novo IBD in COVID-19 affected populations. Methods: We performed a retrospective cohort study using a multi-institutional research network TriNETX. Patients with IBD >16 yrs of age with COVID-19 (up to Feb 7, 2021) were matched 1:1 with non-IBD COVID-19 patients based on demographic variables and known risk factors for severe COVID-19 infection. Outcomes of COVID-19 disease in IBD were compared to the matched control cohort, and outcomes in IBD subpopulations were explored. The incidence of COVID-19 disease in patients with IBD was compared to the general population, and the relationship of incident cases of IBD with prior COVID-19 disease was investigated. Results: A total of 628,947 patients with COVID-19 met inclusion criteria (3486 in IBD and 625,461 in the non- IBD cohort). Prevalence of COVID-19 in patients with IBD cohort was 5.95%. Patients with IBD had a lower incidence of COVID-19 compared to the general population; IRR: 0.79 (95% CI: 0.72-0.86). The IBD cohort had a higher rate of inpatient treatment with no difference in mortality, mechanical ventilation or AKI noted in the propensity-matched analysis. Patients with IBD corticosteroids at baseline were found to be at increased risk for mortality (RR: 2.08, 95% CI:1.06-4.10, hospitalization and 30-day composite outcome (hospitalization or mortality). No difference in composite outcome was noted in the 5-ASA, biologic agent, and anti-TNF group compared to non-IBD cohort. 210 patients (6.0%) and 243 patients (7.0%) developed an IBD flare in 1 month and 3 months after COVID-19 diagnosis respectively. A total of 774 patients (0.10%) with no prior history of IBD were diagnosed with de novo IBD after COVID-19 infection. The IBD diagnosis rate after COVID-19 was lower compared to a control group of patients who were never diagnosed with COVID-19, on propensity score-matched analysis (RR 0.59; 95% CI:0.54-0.65). Conclusion: In a large cohort of patients with IBD infected with COVID-19, we found a higher rate of in-patient treatment without differences in other clinical outcomes compared to a matched cohort of patients without IBD. The rate of de novo IBD after COVID-19 and disease exacerbations in cases of prior IBD was low, and no IBD therapy was linked to worse outcomes.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".