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637 Clinical Prediction Model and Decision Support Tool for Ustekinumab in Crohn's Disease

2019· article· en· W2980249476 on OpenAlexaff
Parambir S. Dulai, Leonard Guizzetti, Tony Ma, Vipul Jairath, Siddharth Singh, Brian G. Feagan, Christopher Gasink, Antônio Carlos Pires, William J. Sandborn

Bibliographic record

VenueThe American Journal of Gastroenterology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsWestern UniversityRobarts Clinical Trials
Fundersnot available
KeywordsMedicineUstekinumabHazard ratioCrohn's diseaseInternal medicineClinical trialVedolizumabProportional hazards modelSurgeryGastroenterologyConfidence intervalDiseaseAdalimumab

Abstract

fetched live from OpenAlex

INTRODUCTION: Identifying predictors of response to treatment with ustekinumab (UST) may improve therapeutic decisions. We created a clinical decision support tool (CDST) for UST therapy in active Crohn's disease (CD). METHODS: Patients from the UNITI trials were included if they received IV UST induction and SQ UST maintenance irrespective of Week 8 response status (derivation set; n = 781). Cox proportional hazard analyses were used to identify baseline factors associated with clinical remission (CDAI 150) by Week 16. The final model was transformed into a CDST and patients were stratified into 3 response probability groups (low, intermediate, high). Secondary analyses were performed to assess the ability of the CDST to predict exposure-efficacy relationships as measured by reduction in CDAI from baseline and differences in trough UST concentrations at weeks 8 and 16. RESULTS: In the derivation analysis, baseline albumin g/L (HR 1.041, 95% CI 1.019–1.063), no prior smoking history (HR 1.233, 95% CI 0.995–1.527), absence of baseline actively draining fistula (HR 1.330, 95% CI 0.906–1.952), absence of prior bowel surgery (HR 1.425, 95% CI 1.140–1.781), and absence of prior exposure to tumor necrosis factor antagonist (HR 1.591, 95% CI 1.214–1.900), were associated with achieving clinical remission by Week 16 of UST therapy. Rates of clinical remission at weeks 3, 6, 8, and 16 were significantly higher in the high probability group versus the intermediate and low probability groups (Table 1). The high probability group had a significantly greater reduction from baseline in CDAI compared to the intermediate and low probability groups at week 8 (high vs. intermediate 94 vs. 54 P < 0.0001; high vs. low 94 vs. 40, P < 0.0001) and week 16 (high vs. intermediate 155 vs. 112 P < 0.0001; high vs. low 155 vs. 84, P < 0.0001). The high probability group had significantly higher trough UST concentrations compared to the intermediate and low probability groups at week 8 (high vs. intermediate 5.2 vs. 3.6 μg/mL P = 0.052; high vs. low 5.2 vs. 2.2 μg/mL, P < 0.0001) and week 16 (high vs. intermediate 2.9 vs. 2.1 μg/mL P = 0.0053; high vs. low 2.9 vs. 1.2 μg/mL, P < 0.0001). CONCLUSION: Predictors of response to UST were identified and successfully modelled into a CDST which can stratify the probability of achieving early clinical remission and rapidity in onset of action in individual CD patients. A statistically significant relationship between UST trough concentrations, probability groups, and efficacy was observed.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0050.001

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 head0.008
GPT teacher head0.278
Teacher spread0.269 · 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 designSimulation or modeling
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

Citations23
Published2019
Admission routes1
Has abstractyes

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