Predictors of Anti-TNF Therapy Failure among Inflammatory Bowel Disease (IBD) Patients in Saudi Arabia: A Single-Center Study
Bibliographic record
Abstract
Background: The advent of monoclonal antibodies (mAbs) has revolutionized the management of many immune-mediated diseases such as inflammatory bowel disease (IBD). Infliximab and adalimumab were the first mAbs approved for the management of IBD, and are still commonly prescribed for the treatment of both Crohn’s disease (CD) and ulcerative colitis (UC). Although mAbs have demonstrated high effectiveness rates in the management of IBD, some patients fail to respond adequately to mAbs, resulting in disease progression and the flare-up of symptoms. Objective: The objective was to explore the predictors of treatment failure among IBD patients on infliximab (INF) and adalimumab (ADA)—as demonstrated via colonoscopy with a simple endoscopic score (SES–CD) of ≥1 for CD and a Mayo score of ≥2 for UC—and compare the rates of treatment failure among patients on those two mAbs. Methods: This was a prospective cohort study among IBD patients aged 18 years and above who had not had any exposure to mAbs before. Those patients were followed after the initiation of biologic treatment with either INF or ADA until they were switched to another treatment due to failure of these mAbs in preventing the disease progression. Univariate and multiple logistic regressions were conducted to examine the predictors and rates of treatment failure. Results: A total of 146 IBD patients (118 patients on INF and 28 on ADA) met the inclusion criteria and were included in the analysis. The mean age of the patients was 31 years, and most of them were males (59%) with CD (75%). About 27% and 26% of the patients had penetrating and non-stricturing–non-penetrating CD behavior, respectively. Patients with UC had significantly higher odds of treatment failure compared to their counterparts with CD (OR = 2.58, 95% CI [1.06–6.26], p = 0.035). Those with left-sided disease had significantly higher odds of treatment failure (OR = 4.28, 95% CI [1.42–12.81], p = 0.0094). Patients on ADA had higher odds of treatment failure in comparison to those on INF (OR = 26.91, 95% CI [7.75–93.39], p = 0.0001). Conclusion: Infliximab was shown to be more effective in the management of IBD, with lower incidence rates of treatment failure in comparison to adalimumab.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".