Stratified Patient Profiling for Vedolizumab Effectiveness in Crohn’s Disease: Identifying Optimal Subgroups for Enhanced Treatment Response in the EVOLVE Study
Bibliographic record
Abstract
INTRODUCTION: This analysis evaluated the relative performance of vedolizumab and anti-tumor necrosis factor alpha (anti-TNFα) agents in subpopulations of biologic therapy-naive patients with Crohn's disease (CD) and assessed whether patients in whom vedolizumab would have a larger treatment effect vs anti-TNFα agents could be identified. METHODS: Data were from EVOLVE, a real-world, multicountry, retrospective cohort study of patients with inflammatory bowel disease who initiated first-line biologic treatment with vedolizumab (n = 195) or anti-TNFα agents (n = 245). Prediction models for time to clinical remission were developed in vedolizumab- and anti-TNFα-treated patients and used to estimate effect scores, a metric of predicted comparative efficacy, for each patient. Patients were ranked by effect scores and potential subpopulations were investigated. Simplified rules to identify these subpopulations were also developed using classification tree analysis. RESULTS: Among all patients, median time to clinical remission was 7.8 months (vedolizumab) and 11.1 months (anti-TNFα) (P < 0.05). Among patients in the top 40% of the effect score distribution, the median time to clinical remission was 4.8 months (vedolizumab) vs 18.1 months (anti-TNFα) (adjusted hazard ratio 2.0, 95% confidence interval 1.3-2.9). A simplified rule for identifying a subpopulation more likely to benefit from vedolizumab was based on having an ongoing CD exacerbation, no prior emergency visits, and non-stricturing disease. CONCLUSIONS: Subpopulations of biologic-naive patients with CD in whom vedolizumab appeared to have a larger effect relative to anti-TNFα agents for the outcome of clinical remission were identified. Validation of the identified subpopulations and simplified rules are warranted to confirm these findings. GOV IDENTIFIER: NCT03710486. Graphical Abstract available for this article.
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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 | 0.001 | 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".