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Record W3169526413 · doi:10.1093/ecco-jcc/jjab073.099

DOP60 Simplified rules to identify bio-naïve patients with Crohn’s Disease with higher likelihood of clinical remission when initiating vedolizumab versus anti-TNFα therapies: Analysis of EVOLVE study data

2021· article· en· W3169526413 on OpenAlexaff
Gerassimos J. Mantzaris, Andrés Yarur, Shu Wang, Shashi Adsul, Pravin Kamble, Michelle Luo, A. Guérin, Emma Billmyer, Ha Nam Nguyen, Brian Bressler

Bibliographic record

VenueJournal of Crohn s and Colitis · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsVedolizumabMedicineDemographicsLogistic regressionEmergency departmentDecision treeInternal medicineDiseaseUnivariate analysisCrohn's diseaseMachine learningMultivariate analysisComputer scienceDemography

Abstract

fetched live from OpenAlex

Abstract Background Previously, we identified subsets of biologic-naïve patients (pts) with Crohn’s disease (CD) from the EVOLVE study who had higher rates of clinical remission (CR) [Fig 1] when initiating vedolizumab (VDZ) vs anti-TNFα treatment, using prediction models based on multiple baseline characteristics.1,2 To aid use in practice, we investigated whether these subsets could be identified using simpler rules based on fewer baseline characteristics. Methods Using data from EVOLVE, we used recursive partitioning and regression tree (RPART) classification to predict membership in the previously identified higher CR subsets for VDZ. The RPART algorithm repeatedly splits data, based on baseline predictors (demographics, prior treatments, clinical characteristics at treatment initiation, Charlson comorbidity index, prior extraintestinal manifestations [EIMs], and prior healthcare resource use). At each split, the predictor and its value as chosen by the algorithm to split the data were those that maximized the number of pts classified correctly. Simplified rules were developed from the resulting RPART decision trees. Analyses of the treatment effect of VDZ vs anti-TNFα were conducted in the subsets of pts identified by each rule. Results Pts with data on CR and candidate predictors were included (VDZ [n=195]; anti-TNFα [n=245]). Three simplified rules (A, B, & C) were identified (Table 1). Pt characteristics included in the rules (exacerbation ongoing at treatment initiation, no emergency department/emergency room (ED/ER) visits prior to treatment initiation, no fistulae at most recent assessment prior to treatment initiation, pre-initiation disease behaviour) were among the main predictors of CR in VDZ pts identified previously. Pts identified by Rule A comprised 32% of the EVOLVE population, and were those who 1) had an exacerbation ongoing at index, 2) did not have ED/ER visits prior to initiation and 3) had pre-initiation disease behaviour classified as other than stricturing with/without perianal disease. Among these pts, median time to CR for VDZ and anti-TNFα pts were 6.7 and 18.1 months, respectively (unadjusted log-rank p<0.001), and the adjusted hazard ratio (HR) of CR for VDZ vs anti-TNFα was 2.9 (95% CI: 1.7, 5.0). Rules B & C identified larger subsets in which VDZ vs anti-TNFα treatment differences were smaller but still statistically significant. Conclusion Simple rules were developed to identify biologic-naïve, CD pts in whom VDZ initiation appeared to have a larger effect on CR relative to anti-TNFα initiation. Validation of these rules in other data sources is important to confirm these findings; if validated, these simplified rules can inform targeting of treatment and optimization of outcomes for pts with CD treated with VDZ.

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)0.011
metaresearch head score (Gemma)0.030
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 score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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