Primary infliximab failure in pediatric colonic inflammatory bowel disease: Development of a proteomics predictive model using a prospective Canadian cohort
Bibliographic record
Abstract
BACKGROUND: We aimed to build a serum proteomics-based model to predict primary nonresponse (PNR) to infliximab (IFX) in pediatric colonic inflammatory bowel disease, with early proactive therapeutic drug monitoring. METHODS: Children in the prospective Canadian Children IBD Network with ulcerative colitis (UC), inflammatory bowel disease unclassified (IBD-U), or colonic Crohn's disease (CD) with serum pre-IFX were eligible. We defined PNR as IFX cessation plus surgery/drug switch within 6 months. We compared clinical features between groups (Mann Whitney U, chi-square test). We measured serum proteins with Olink Inflammation/Immune Response panels. We built a regularized regression (generalized linear model [GLM]) machine learning model and compared its performance with other models with 10-fold cross-validation repeated 10 times (receiver-operating characteristic/precision-recall curves, predictive score separation). We ranked proteomic features by SHAP (SHapley Additive exPlanations) analysis. We hypothesized that treatment-naïve serum would be more informative than treatment-exposed serum. RESULTS: We included 96 patients: 71 UC/IBD-U (23 nonresponders), 42 treatment-naïve (12 nonresponders); and 25 CD, 19 treatment-naïve. Pre-third and pre-fourth dose serum infliximab levels were similar and robust (>10 µg/mL) in primary nonresponders and responders. Predictive performance was superior for diagnostic, treatment-naïve samples; the GLM showed good ability to separate primary nonresponders and responders. The GLM model on treatment-naïve serum (area under the curve ∼0.75) had better specificity to predict responders and included 21 proteins, with CSF1 and ITM2A top ranked. UC/IBD-U responders more often were steroid refractory and received infliximab as first maintenance. CONCLUSIONS: A serum proteomics linear model on treatment-naïve serum best predicted PNR. Findings require external validation but suggest that the diagnostic/pretreatment window may be key to understanding biology central to effective drug sequencing.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".