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S802 Persistence Among Patients With Crohn’s Disease Previously Treated With an Anti-tumor Necrosis Factor Inhibitor and Switching or Cycling to Another Biologic Agent

2022· article· en· W4316086120 on OpenAlexaff
Maryia Zhdanava, Sumesh Kachroo, Ameur M. Manceur, Zhijie Ding, Christopher Holiday, Ruizhi Zhao, Bridget Goodwin, Dominic Pilon

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsAdalimumabMedicineVedolizumabInfliximabUstekinumabInternal medicinePersistence (discontinuity)DiscontinuationTumor necrosis factor alphaCrohn's diseaseGastroenterologyDisease

Abstract

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Introduction: Among patients with Crohn’s disease (CD), non-response to an anti-TNF agent can lead to switching to a biologic in a different class (i.e., ustekinumab, vedolizumab) or cycling to another anti-TNF agent (i.e., adalimumab, infliximab, certolizumab). This study compared real-world persistence among patients with CD who switch or cycle from an anti-TNF agent. Methods: Adults with CD treated with an anti-TNF whose first switching or cycling (index date) occurred between 09/23/2016 and 08/01/2019 were selected from the IBM® MarketScan® Commercial Database. Patients had: ≥12 months of continuous insurance eligibility before the first anti-TNF, discontinuation of the first anti-TNF within 12 months (baseline period) of the index date, and no other immune disorders in the 12-month baseline period. Cohorts were balanced on baseline characteristics using inverse probability of treatment weights (IPTW). Persistence to index biologic (i.e., biologic switched or cycled to) was defined as absence of therapy exposure gaps >120 days (ustekinumab, vedolizumab, infliximab) or >60 days (adalimumab, certolizumab) between days of supply. Composite endpoints were: persistence and being corticosteroid-free (no corticosteroids with ≥14 days of supply after day 90 post-index), and persistence while on monotherapy (no immunomodulators/non-index biologics). Weighted Kaplan-Meier and Cox models were used to assess outcomes at 12 months post-index. Results: After IPTW, the sample size was 444 and 441 in the switching and cycling cohorts, and baseline characteristics were well balanced (Table). At 12 months post-index, the proportions of patients persistent to the index agent and patients persistent while on-monotherapy were significantly higher in the switching compared to the cycling cohort (Figure). In the switching compared to the cycling cohort, the rate of being persistent to the index agent was 44% higher (hazard ratio [HR]: 1.44; 95% confidence interval [CI]: 1.11-1.88; P=0.007*), the rate of being persistent and corticosteroid-free 8% higher (HR: 1.08; 95% CI: 0.89-1.32; P=0.426), and the rate of being persistent while on-monotherapy 56% higher (HR: 1.56; 95% CI: 1.28-1.90; P< 0.001*). Conclusion: Following the discontinuation of the first anti-TNF agent, patients with CD who switched to a different class of biologic were more persistent than patients who cycled to another anti-TNF agent. These findings may aid physicians whose patients experience loss of response on the first anti-TNF agent.Figure 1.: CD: Crohn’s disease; Std diff: standardized difference; SD: standard deviation; TNF: tumor necrosis factor; 5-ASA: 5-aminosalicylic acid Table: Selected baseline characteristics in weighted switching and cycling cohorts1,2. Notes: (1) cohorts were weighted on baseline characteristics using inverse probability of treatment weights; characteristics considered well balanced if standardized difference is <10%; (2) patients receiving immunomodulators or corticosteroids (at least one episode of ≥90 days of continuous use), patients with CD-related hospitalizations or CD-related surgeries. Table 1. - CD: Crohn’s disease Mean ± SD or n (%) SwitchingN=444 CyclingN=441 Std diff (%) Age 40.4 ± 14.2 39.5 ± 13.9 6.3 Female 250 (56.3%) 257 (58.4%) 4.3 All-cause costs (US$ 2021) 72,594 ± 52,331 71,643 ± 53,192 1.8 Prescription drug costs 35,134 ± 29,132 34,340 ± 30,674 2.7 Total medical costs 37,459 ± 51,598 37,303 ± 52,046 0.3 Claims-derived CD severity indicator2 263 (59.1%) 266 (60.5%) 2.7 Charlson Comorbidity Index 0.54 ± 0.9 0.52 ± 0.9 1.4 CD-related surgery 38 (8.6%) 37 (8.4%) 0.9 Medication Corticosteroids 343 (77.1%) 331 (75.0%) 4.8 ≥1 episode with ≥60 days of continuous corticosteroid use 137 (30.9%) 144 (32.7%) 3.9 5-ASA 150 (33.7%) 138 (31.3%) 5.1 Immunomodulators 149 (33.5%) 154 (34.9%) 2.9 Antidiarrheals 26 (5.8%) 27 (6.0%) 0.9 Baseline anti-TNF Adalimumab 288 (64.9%) 278 (63.1%) 3.7 Infliximab 140 (31.4%) 148 (33.5%) 4.4 Certolizumab pegol 16 (3.7%) 15 (3.4%) 1.6 Std diff: standardized difference; SD: standard deviation; TNF: tumor necrosis factor; 5-ASA: 5-aminosalicylic acid Table: Selected baseline characteristics in weighted switching and cycling cohorts1,2. Notes: (1) 1. Cohorts were weighted on baseline characteristics using inverse probability of treatment weights; characteristics considered well balanced if standardized difference is <10%; (2) Patients receiving immunomodulators or corticosteroids (at least one episode of ≥90 days of continuous use), patients with CD-related hospitalizations or CD-related surgeries

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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
Published2022
Admission routes1
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