Relationship between serum total bilirubin levels and primary loss of response to Infliximab therapy in Crohn’s disease patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Crohn’s disease (CD) is a chronic inflammatory bowel disease whose global prevalence continues to rise. Infliximab (IFX) is the first-line biologic for moderate-to-severe CD, yet a high rate of loss of response (LOR) limits its long-term efficacy. Existing predictive tools are either costly or insufficient. This study aimed to evaluate the predictive value of serum total bilirubin (sTB) in combination with routine clinical and hematological parameters for IFX treatment outcomes in CD patients. METHODS: We retrospectively enrolled 85 CD patients who were treated with IFX (July 2019-December 2021) and stratified them into training (n = 59) and validation (n = 26) sets. LOR was defined as failure to achieve biochemical remission (fecal calprotectin < 250 µg/g or ≥ 50% reduction from baseline) at 26 weeks post-IFX. Baseline data (demographics, laboratory values, imaging, and clinical features) were analyzed via univariate and multivariate logistic regression. A nomogram integrating significant predictors was developed and validated. RESULTS: Among the 85 patients, 48 (56.5%) developed LOR. Patients with LOR had significantly lower baseline sTB levels (5.86 vs. 7.88 µmol/L, P = 0.001). Multivariate analysis revealed sTB (odds ratio (OR) = 0.83, 95% confidence interval (CI): 0.64–0.99), L1 Montreal phenotype (OR = 0.23, 95% CI: 0.10–0.85), perianal lesions (OR = 3.84, 95% CI: 1.27–11.57), and elevated monocyte percentage (OR = 1.20, 95% CI: 1.00-1.49) as independent predictors of LOR. The combined model in overall patients achieved an area under the curve (AUC) of 0.80 for LOR prediction, outperforming sTB alone (AUC = 0.715). Internal validation confirmed robust performance (training AUC = 0.82; validation AUC = 0.78). CONCLUSION: Lower baseline sTB levels were associated with LOR to IFX. A simple nomograms based on sTB, L1 phenotypes, perianal lesions, and elevated monocyte percentages provide a simple tool for the identification of CD patients at high risk 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 | 0.004 |
| 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 | 0.000 |
| 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".