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P-012 YI Decisions to Dose Optimize Infliximab Using Pre-adjustment Therapeutic Drug Monitoring Result in Higher Trough Concentrations and Improved Endoscopic Outcomes

2016· article· en· W2319299013 on OpenAlexaff
Orlaith Kelly, OʼDonnell Sarah, Stempak Joanne, Steinhart Hillary, S Meyer Mark

Bibliographic record

VenueInflammatory Bowel Diseases · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineTherapeutic drug monitoringInfliximabDosingInternal medicineInflammatory bowel diseaseTrough levelRetrospective cohort studyCrohn's diseaseLogistic regressionSurgeryDiseasePharmacokinetics

Abstract

fetched live from OpenAlex

Increasingly, adequate infliximab (IFX) dosing is associated with improved outcomes in inflammatory bowel disease (IBD). Individualized therapeutic drug monitoring (TDM) to guide dose adjustment is cost-effective may offer clinical improvement. However, the effect of TDM-guided dose escalation on subsequent endoscopic outcomes remains unclear. We retrospectively evaluated the impact of TDM-driven dose adjustment on endoscopic remission. Data from primary responders to IFX who underwent dose escalation between 2008 and 2014 (interval reduction, dose increase or both) were reviewed. Patients with documented active endoscopic disease (ileal or colonic) <3 months prior to dose adjustment were included. 2 cohorts were examined: those with TDM-based decision to escalate and those with empiric decision. Outcomes recorded at median 6 months post- adjustment included endoscopic remission (Mayo <1, SES-CD <3), CRP, clinical remission and IBD-specific healthcare utilization (emergency visits, hospitalizations, steroid use, flares). TDM was performed using a homogenous mobility shift assay. Post-adjustment IFX and ATI concentration most discriminant for endoscopic remission were determined by receiver operating characteristic curves. A multivariable regression model evaluated these factors as independent predictors of remission. Three hundred twelve dose optimization events were examined (149 TDM –based decision, 163 empiric). Patient demographics were not significantly different between groups apart from CRP ([mean ± SE] TDM: 23 ± 1.2; non-TDM: 11 ± 0.8 mg/L, P < 0.01). 63% of TDM group attained endoscopic remission compared to 48% non-TDM group (P < 0.05). 69% of the TDM group had significant clinical response compared to 57% non-TDM (P < 0.01) and also had fewer hospitalizations including ED visits (22% TDM versus 35% non-TDM group, P = 0.025). UC patients had a shorter time to optimization from first infusion (10 versus 20 months, P < 0.0001) and in the TDM group, had higher rates of endoscopic healing (64% compared to 46% CD) though this was not statistically significant. Median IFX trough levels increased significantly after TDM- based adjustment (from 1.5(pre) to 11 μg/mL (post); P < 0.0001) and were significantly higher than post-adjustment levels available in the empiric cohort (6.5 μg/mL, P = 0.015). Post-adjustment infliximab concentration of >4.5 μg/mL (area under the curve (AUC) = 0.8; 95% CI 0.71 to 0.88) and ATI concentration of <2.3 U/mL (AUC = 0.70; 95% CI 0.63 to 0.81) were associated with endoscopic remission. Multivariable analysis showed that IFX concentration (OR 1.2; [95% CI 1.1–1.3]; P < 0.0001) remained an independent predictor of endoscopic remission. TDM -guided dose adjustment is associated with higher post-adjustment trough levels, fewer ATI and in turn, higher endoscopic remission rates and fewer relapses.

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)0.001
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 score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.022
GPT teacher head0.283
Teacher spread0.262 · 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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Citations1
Published2016
Admission routes1
Has abstractyes

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