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Record W2921587626 · doi:10.1093/jcag/gwz006.141

A142 INFLAMMATORY BOWEL DISEASE PATIENTS REQUIRE AN INCREASED ADALIMUMAB DRUG LEVEL TO SIMULTANEOUSLY ACHIEVE CLINICAL AND BIOLOGICAL REMISSION

2019· article· en· W2921587626 on OpenAlexaffabout
T A Cookson, N Stern, Reed T. Sutton, Richard N. Fedorak, Brendan P. Halloran, Levinus A. Dieleman, Karen Wong, Vivian Huang, Farhad Peerani, Sander van Zanten, Adriana Lazarescu, Karen I. Kroeker

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdalimumabUlcerative colitisCalprotectinMedicineInflammatory bowel diseaseBiomarkerTherapeutic drug monitoringInternal medicineInfliximabYouden's J statisticReceiver operating characteristicFaecal calprotectinCrohn's diseaseGastroenterologyArea under the curveClinical significanceDiseasePharmacokinetics

Abstract

fetched live from OpenAlex

Physicians use therapeutic drug monitoring of adalimumab (ADA) as an optimization tool to guide patient therapy with inflammatory bowel disease (IBD). IBD consists primarily of Crohn’s disease (CD) and ulcerative colitis (UC). Presently, the literature on ADA therapeutic boundaries recommend a broad 5–20μg/mL range. Due to limited treatment options for moderate-to-severe IBD and the high loss of response risk with biologics, optimization to sustain clinical and biological remission is imperative. Clinical indices, including the Harvey-Bradshaw index (HBI) for CD and partial Mayo (PM) for UC, are used to assess clinical disease activity. Fecal calprotectin (FCP) is a biomarker that is used to assess intestinal inflammation. An FCP<100μg/g is correlated with mucosal remission. Currently, there is no model describing a concise therapeutic range for both clinical and biological remission in ADA patients. To assess the optimal ADA drug level (DL) that can simultaneously predict both clinical and biological remission. This is a retrospective, cross-sectional chart review of CD and UC patients, ≥18 years old, at the University of Alberta IBD Clinic, with at least one DL measured between May 2015 and May 2017. Receiver-operating characteristic (ROC) curves were used to evaluate when FCP levels were able to predict clinical disease activity, using HBI and PM scores, and the ability of DLs to predict an FCP<100μg/g. Area under the curve (AUC) is presented with a 95% CI and p-value. Youden’s method was used to determine the best cut-off. Significance is evaluated at α=0.05. There were 506 DLs collected from 305 patients. Demographics included: a mean age of 44 (15.0), 48% males, 79% CD, 30% had previous biologic exposure, and 46% on concomitant IMM. Therapy was escalated in 41% of DLs between 5–10μg/mL compared to 15% between 10–15μg/mL. Using ROC analysis, AUC for FCP to predict clinical disease activity was 0.733 (CI: 0.578–0.888, p=0.019) with an optimal cut-off of >99.5μg/g. This is comparable to the currently accepted FCP level of 100μg/g as a cut-off for biological remission. The AUC for DLs to predict an FCP<100μg/g was 0.586 (CI: 0.525–0.647, p=0.007) with an optimal cut-off of >12μg/mL. Figure 1 illustrates the ROC curves for (A) FCP to predict clinical disease activity, and (B) DLs to predict an FCP<100μg/g. A drug level of 12–20μg/mL is strongly correlated with simultaneously attaining both clinical and biological remission in adalimumab patients. Figure 1. ROC for: A, FCP to predict clinical disease activity; B, DLs to predict biological remission. The y-axis illustrates the Sensitivity (or true positives) and the x-axis is 1 – Specificity (or 1 – true negatives). None

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)0.002
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 score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

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