Therapeutic Drug Monitoring Guides the Management of Crohn’s Patients with Secondary Loss of Response to Adalimumab
Bibliographic record
Abstract
Background: Managing loss of response (LOR) in Crohn's disase (CD) patients remains challenging. Compelling evidence supports therapeutic drug monitoring (TDM) to guide management in patients on infliximab, but data for other biologics are less robust. We aimed to asses if empiric dose escalation led to improved clinical outcome in addition to TDM-guided optimization in CD patients with LOR to adalimumab (ADA). Methods: Retrospective chart review of patients followed between 2014 and 2016 at McGill IBD Center with index TDM for LOR to ADA was performed. Primary outcomes were composite remission at 3, 6, and 12 months in those with empiric adjustments versus TDM-guided optimization. Results: There were 104 patients (54.8% men) who were included in the study. Of this group, 81 patients (77.9%) had serum level (SL) ≥5µg/ml at index TDM with a median value of 12µg/ml (IQR 6.1-16.5). There were 10 patients (9.6%) who had undetectable SL with high anti-ADA antibodies and 48 (46.2%) received empiric escalation. TDM led to change in treatment in 58 patients (55.8%). Among them, 28 (48.3%) had discontinued ADA, 12 (21.7%) had addition of immunomodulator or steroid, and 18 (31%) had ADA dose escalation. Empiric dose escalation before TDM-based optimization was not associated with improved outcomes at 3, 6, and 12 months, irrespective of SL levels. Clear SL cutoff associated with composite remission was not identified. Conclusions: Our data do not support empiric dose adjustment beyond that based on the result of the TDM in patients with LOR to ADA. TDM limits unnecessary dose escalation and provides appropriate treatment strategy without compromising clinical outcomes. 10.1093/ibd/izy044_video1izy044.video15768828880001.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".