A235 INCIDENCE OF INFECTIONS IN INFLAMMATORY BOWEL DISEASE PATIENTS TREATED WITH ANTI-TUMOR NECROSIS FACTOR AGENTS
Bibliographic record
Abstract
The efficacy of anti-TNF therapy in IBD is well established, as are the effects of anti-TNF therapy on the host immune system. Observational studies have demonstrated that the risk of serious infections and opportunistic infections in patients being treated with anti-TNF agents are low. The risk of overall infections in patients exposed to anti-TNF agents is generally not reported. A retrospective observational study was undertaken to describe the incidence of overall infections in patients exposed to anti-TNF agents within a community practice. This retrospective study examined patients in a community GI practice with inflammatory bowel disease and treated with anti-TNF agents between October 2007 and July 2018. The chart review identified patients who had exposure to anti-TNF agents and developed any infectious complication. Other relevant data collected included: monotherapy versus combination therapy, demographic data, co-morbid conditions and previous surgeries for IBD. A total of 364 IBD patients were identified as having at least one exposure to anti-TNF treatment. A total of 329 unique infections were identified among 151 individual patients. Of these 151 patients, the male to female ratio was 64 to 87 with a mean onset of IBD at 32 years old. 117 patients had Crohn’s Disease, 34 had Ulcerative Colitis, 28 patients were smokers and 43 patients had previous surgeries related to IBD. Of the 151 patients 52% were treated with anti-TNF monotherapy and 47.5% were treated with combination therapy. The most notable comorbities identified were: diabetes mellitus-7, COPD-3, Latent TB-1, and HIV-1. Overall, the most frequent infections that occurred were UTI (66), bronchitis (51) and sinusitis (41) (see table 1). This retrospective observational study revealed that there was a high rate of infections occurring in patients treated with anti-TNF agents within this GI community practice. While serious and opportunistic infections were not specifically reported, the high frequency of infections in general has implications for patients, providers and the health care system. This study reminds us that the risks of infections are high and the importance of mitigating this risk by proper patient selection and prophylactic interventions. Limitations of this study included its retrospective nature, lack of comparative group included, variability in ant-TNF exposure and severity of disease. Incidence of Infections None
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".