S8 How Can We Predict the Development of Antibodies to Infliximab in Crohn’s Disease?
Bibliographic record
Abstract
Background: Anti-tumour necrosis factor-α (anti-TNF-α) therapy is an effective treatment for the management of Crohn’s disease (CD). However, treatment failure is common. The aim of this study was to identify predictors of anti-TNF-α therapy failure. Methods: Retrospective single-center study including anti-TNF-α naïve patients with CD, who started on intravenous infliximab, between January 2019 and December 2021. Biochemical parameters included erythrocyte sedimentation rate (ESR), c-reactive protein (CRP), faecal calprotectin, infliximab serum concentrations and the presence of antibodies to infliximab (ATI). Anti-TNF-α therapy failure was defined as the development of ATI at 6 and 12 months, absence of clinical response at 6 months, absence of clinical remission or absence of objective response at 12 months. Clinical response was defined as a reduction in Harvey-Bradshaw index (HBI) of ≥3 points comparing with initial value or HBI <5 points if initial HBI ≥7 points and clinical remission as HBI ≤4 points. Objective response was assessed by endoscopic studies, defined by improvement of mucosal inflammation and absence of deep ulcerations, or imaging, defined as improvement in bowel wall thickness, inflammatory fat, mural blood flow and hyper-enhancement. Results: A total of 53 CD patients were included, 30 were female (56.6%), with a mean age at the beginning of treatment of 39 ± 2 years. Considering the Montreal classification, 31 patients had ileal disease (58.5%), 7 colonic (13.2%) and 15 ileocolonic disease (28.3%); 22 patients had nonstricturing, nonpenetrating disease (41.5%), 22 stricturing (41.5%) and 9 penetrating disease (17.0%). Eleven patients had perianal disease (20.8%). A total of 26 patients (49.1%) were treated with combination of anti-TNF-α and immunomodulatory therapy (thiopurine or methotrexate). Anti-TNF-α therapy failure occurred in 21 patients (39.6%). At 6 months, the development of ATI occurred in 6 patients (11.3%) and absence of clinical response in 9 patients (17.0%). At 12 months, absence of clinical remission was seen in 13.6% of patients and absence of objective response in 27.0% of patients. At 6 months, the development of ATI was significantly higher in patients with lower infliximab serum concentrations at week 14 (with antibodies 5.9 ± 3.2 µg/mL vs without 14.3 ± 7.7 µg/mL, P < 0.001). Additionally, the development of ATI was significantly higher in patients with a higher initial value of ESR (with antibodies median 35 vs without median 13, P = 0.045). The infliximab serum concentrations at week 14 (AUC 0.828; P = 0.009; with sensitivity 0.833 and specificity 0.404 for values ≤11.6) and the initial value of ESR (AUC 0.754; P = 0.045; with sensitivity 0.833 and specificity 0.468 for values ≥15) had very good and good discriminative capacity, respectively, in predicting the development of ATI. No statistically significant differences were found in initial values of CRP and faecal calprotectin, between both groups. Moreover, the other definers of anti-TNF-α therapy failure were not associated with the combination with immunomodulatory therapy, infliximab serum concentrations at week 14 or initial values of ESR, CRP and faecal calprotectin. Conclusion(s): Infliximab serum concentration after induction therapy is the most important factor in the development of ATI, influencing treatment response, regardless of the combination of anti-TNF-α therapy with immunomodulatory therapy. A higher initial value of ESR can also predict the development of ATI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".