P395 Dermatological reactions associated with anti–TNF therapy in inflammatory bowel disease- an 12 year experience
Bibliographic record
Abstract
Anti-TNF therapies have significantly influenced our treatment paradigms and are amongst our most effective therapies to induce and maintain remission. They are, however, not without side effects. Data on dermatological complications are limited. Our aim was to describe the incidence, management and outcome of dermatological complications of anti-TNF therapy in IBD patients at our centre. We conducted a retrospective review of 435 consecutive IBD patients treated with anti-TNF (Infliximab or Adalimumab) therapy between 2005 and 2017. Data were collected from electronic case records and included demographics, disease characteristics, medical history, treatment and dermatological complications. Forty-four patients (10.1%) had dermatological complications. Of these, 25 were female (56.8%). Thirty-four patients had Crohn’s disease (CD = 77.2%) and 10 had ulcerative colitis (UC = 22.8%). Disease phenotype in CD was A2(24) L3(22) B1(21) in respective categories whereas 7/10 UC patients had Montreal E3 disease. Mean age at diagnosis was 34.31 years (median 28, IQR 23.5). Thirty-four patients were treated with Infliximab. The most common reaction reported was a non-specific rash in 11 patients, eczema in 8, and psoriasiform reactions in 7 patients. Shingles was noted in 5 cases and 3 patients had urticaria. Seborrheic dermatitis, cellulitis, itching, acne, and skin abscesses were noted in two patients each along with one case each of Lichen planus and chicken pox. The average time of onset of symptoms from initiation of treatment was 20 months(median 7, IQR 27). Twenty of 34 patients treated with infliximab, continued treatment following the dermatological side effect without any further reactions,12 were switched to adalimumab of which 1 patient had a further psoriasiform reaction leading to cessation of Anti TNF therapy and initiation of ustekinumab. One patient was not given any further Anti-TNF treatment after the initial reaction and one patient was switched to ustekinumab after the reaction. Of 10 patients on adalimumab who had dermatological side effects, all continued on therapy without any further reactions. The incidence of dermatological effects was 10.1% in our cohort. We did not observe a “class effect” as after switching to a different anti TNF the tolerability was good. Dermatological events were more common in patients with colonic CD treated with infliximab. Further studies on cumulative dermatological effects, risk factors, time to event and outcomes are underway at our institution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".