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Outcomes of Second- and Third-LineTumor Necrosis Factor-α Antagonists in Inflammatory Bowel Disease

2016· article· en· W2977523770 on OpenAlexaffabout
Harith Baharith, Neeraj Narula, Yuhong Yuan, Christopher Stallwood, Michael R. Fine, John K. Marshall

Bibliographic record

VenueThe American Journal of Gastroenterology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineDiscontinuationInternal medicineInfliximabAdverse effectInflammatory bowel diseaseRetrospective cohort studyProportional hazards modelLogistic regressionSurgeryTumor necrosis factor alphaGastroenterologyDisease

Abstract

fetched live from OpenAlex

Introduction: Anti-Tumor Necrosis Factor-α (Anti-TNF) failure in inflammatory bowel diseases (IBD) due to intolerance or loss of response is common. We reviewed the safety and efficacy of second- and third-line Anti-TNF after failure of a first-line Anti-TNF to determine predictors and probabilities of treatment success. Methods: We conducted a retrospective chart review of outpatient records from January 2007 to November 2015 at McMaster University Medical Center (Hamilton, Ontario) to identify all patients exposed to at least two Anti-TNF. We recorded demographics, adverse events, and 12-week response and remission rates after second- and, if applicable, third-line Anti-TNF therapy. Multivariable logistic regression was used to identify predictors of second-line Anti-TNF failure. Kaplan- Meier survival curves were used to determine the probability of remaining on second-line Anti-TNF. Log rank tests were used to compare survival curves. Where available, we also reviewed therapeutic drug monitoring results. Results: 149 patients were included in the analysis (Table 1). Sixty-one patients (40.9%) discontinued the second Anti-TNF after a mean of 31.2 months and 9 of 61 (14.8%) switched to a third Anti-TNF. Six of 9 patients (66.6 %) on a third Anti-TNF discontinued therapy after a mean of 14 months. The primary reason of discontinuation for the second Anti-TNF was secondary loss of response (22/61 patients; 36%) and for the third Anti-TNF was adverse event (6/9 patients; 66.6%). 49.2% of patients receiving second Anti-TNF achieved clinical remission and 66.7% achieved clinical response at 12 weeks. Multivariable logistic regression identified concurrent steroid use at induction [OR 0.22 (0.09-0.50); p < 0.001] and early discontinuation of first Anti-TNF [OR 1.03 (1.01-1.05); p=0.006] as predictors of second-line Anti-TNF failure. The retention rate on second-line Anti-TNF was 44% at 60 months (Figure 1). Patients who achieved clinical remission at 12 weeks had a longer mean duration of second Anti-TNF therapy (Figure 2). Patients who remained on Adalimumab (ADA) as their second-line Anti-TNF had lower mean ADA levels (6.67) than patients who failed (10.3).Figure 1Figure 2Table 1: Characteristics of 149 Patients Exposed to at Least Two Anti-TNFConclusion: In our cohort, second-line Anti-TNF were effective and safe, and almost half patients remained on therapy at 5 years. Outcomes of third-line Anti-TNF were less favourable, likely due to selection of a refractory patient subgroup.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.230
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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