Drug persistence and need for dose intensification to adalimumab therapy; the importance of therapeutic drug monitoring in inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND: Therapeutic drug monitoring (TDM) aid therapeutic decision making in patients with inflammatory bowel disease (IBD) who lose response to anti-TNF therapy. Our aim was to evaluate the frequency and predictive factors of loss of response (LOR) to adalimumab using TDM in IBD patients. METHODS: One hundred twelve IBD patients (with 214 TDM measurements, CD/UC 84/28, male/female 50/62, mean age CD/UC: 36/35 years) were enrolled in this consecutive cohort from two referral centres in Hungary. Demographic data were comprehensively collected and harmonized monitoring strategy was applied. Previous and current therapy, laboratory data and clinical activity were recorded at the time of TDM. Patients were evaluated either at the time of suspected LOR or during follow-up. TDM measurements were determined by commercial ELISA (LISA TRACKER, Theradiag, France). RESULTS: Among 112 IBD patients, LOR/drug persistence was 25.9%/74.1%. The cumulative ADA positivity (>10 ng/mL) and low TL (<5.0 μg/mL) was 12.1% and 17.8% after 1 year and 17.3% and 29.5% after 2 years of adalimumab therapy. Dose intensification was needed in 29.5% of the patients. Female gender and ADA positivity were associated with LOR (female gender: p < 0.001, OR:7.8 CI 95%: 2.5-24.3, ADA positivity: p = 0.007 OR:3.6 CI 95%: 1.4-9.5). CONCLUSIONS: ADA development, low TL and need for dose intensification were frequent during adalimumab therapy and support the selective use of TDM in IBD patients treated with adalimumab. ADA positivity and gender were predictors of LOR.
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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 | 0.005 |
| 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 | 0.001 |
| 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".