Safety of Combination Biologic and Antirejection Therapy Post–Liver Transplantation in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel disease (IBD) post-liver transplant (LT) may have bowel inflammation requiring biologic therapy. We aimed to evaluate the safety of combination biologic and antirejection therapy in IBD patients after LT from a tertiary center case series and an updated literature review. METHODS: Inflammatory bowel disease patients undergoing LT between 1985 and 2018 and requiring combination biologic and antirejection therapy post-LT were identified from the London Health Sciences Transplant Registry (Ontario, Canada). Safety outcomes were extracted by medical chart review. For an updated literature review, EMBASE, Medline, and CENTRAL were searched to identify studies evaluating the safety of combination biologic and antirejection therapy in IBD patients. RESULTS: In the case series, 19 patients were identified. Most underwent LT for primary sclerosing cholangitis (PSC; 14/19, 74%) treated with anti-integrins (8/19, 42%) or tumor necrosis factor α (TNF) antagonists (6/19, 32%). Infections occurred in 11/19 (58%) patients, most commonly Clostridium difficile (4/19, 21%). Two patients required colectomy, and 1 patient required re-transplantation. In the literature review, 13 case series and 8 case reports reporting outcomes for 122 IBD patients treated with biologic and antirejection therapy post-LT were included. PSC was the indication for LT in 97/122 (80%) patients, and 91/122 (75%) patients were treated with TNF antagonists. Infections occurred in 32/122 (26%) patients, primarily Clostridium difficile (7/122, 6%). CONCLUSIONS: Inflammatory bowel disease patients receiving combination biologic and antirejection therapy post-LT appeared to be at increased risk of Clostridium difficile. Compared with the general liver transplant population in the published literature, there was no increased risk of serious infection.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".