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Record W4416141346 · doi:10.25251/p6a0hd58

C-206 Real-World Prospective Data Assessing the Long-Term Safety of Abrocitinib Treatment in Adults with Moderate to Severe Atopic Dermatitis (AD): Over Two Years of Experience from the CorEvitas AD Registry [1225]

2025· article· W4416141346 on OpenAlexaboutno aff
Sampada Gandhi, Bernice Gershenson, Heather Chubb, Bryan Cherry, Nathan Rubin, Christine Barr, Melissa Moore‐Clingenpeel, Dimitrios Pappas, Haiyun Fan, Cristina Lumpan, Jen Weissert, Pinaki Biswas, Gary Chan

Bibliographic record

VenueSKIN The Journal of Cutaneous Medicine · 2025
Typearticle
Language
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
FundersGenentechSanofiPfizer
KeywordsCohortIncidence (geometry)Prospective cohort studyPoisson regressionCohort studyConfidence intervalAtopic dermatitisObservational study

Abstract

fetched live from OpenAlex

Introduction: Abrocitinib is an oral Janus kinase inhibitor (JAKi) approved in the US for adults with moderate-to-severe atopic dermatitis (AD). A post-approval prospective observational study assessing long-term safety of abrocitinib in AD patients from the US and Canada using the CorEvitas AD Registry from 2022 to 2034 is ongoing. This analysis estimated the incidence rates (IRs) of prespecified delayed (eg, malignancies) and acute-onset (eg, serious infections) safety events separately in AD patients exposed to abrocitinib and in a comparator cohort exposed to biologic and non-biologic (non-JAKi) chronic systemic AD treatments. Procedure/Study: AD patients ≥18 years enrolled in the Registry during 14 Jan 2022 - 3 Jul 2024 who received abrocitinib or a comparator drug within 12 months prior to, at or after Registry enrollment were included in the study. Crude IRs for each safety event [number of first events per 100 patient-years (PY)] and 95% confidence intervals (CI) based on Poisson counts were calculated. Results: Delayed-onset outcomes: Mean (±SD) age of patients was 47.0 (±18.6) and 51.5 (±18.6) years in the abrocitinib (n=100) and comparator cohort (n=1096), respectively. The abrocitinib cohort was comprised of 51.0% female and 65.0% White compared to 57.1% female and 72.5% White in the comparator cohort. The total follow-up exposure time in the abrocitinib and comparator cohorts was 87.3 and 1168.0 PY, respectively. There were 0 and 17 total malignancies in the abrocitinib and comparator cohorts, with an IR per 100 PY of 0.0 (95% CI: 0.0-4.2) and 1.5 (95% CI: 0.9-2.4), respectively. Acute-onset outcomes: Mean (±SD) age of patients was 46.6 (±18.3) and 51.4 (±18.4) years in the abrocitinib (n=88 exposures) and comparator cohort (n=996 exposures), respectively. Of the total abrocitinib exposures, 53.4% were female and 61.4% White compared to 58% female and 72.9% White in the comparator exposures. The total follow-up exposure time in the abrocitinib and comparator cohorts was 58.7 and 833.8 PY, respectively. The number of events and IRs per 100 PY (95% CI) in the abrocitinib cohort were as follows: major adverse cardiac events (MACE), retinal detachment (RD), thrombosis and hepatotoxicity [n=0]; opportunistic infection (OI) [n=1, 1.7 (0.0-9.6)]; serious infections (SI) [n=2, 3.4 (0.4-12.4)]. The number of events and IRs per 100 PY (95% CI) in the comparator cohort were as follows: RD and hepatotoxicity [n=0]; MACE and thrombosis [n=2, 0.2 (0.0-0.9)], OI [n=6, 0.7 (0.3-1.6)]; SI [n=11, 1.3 (0.7-2.4)]. Conclusion: During 2 years, no new safety signals and a low incidence of prespecified safety events was observed in the abrocitinib cohort, aligning with the known safety profile of abrocitinib. Funding/Disclosures: This study was funded by Pfizer.

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)0.015
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.337
Teacher spread0.321 · 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
Published2025
Admission routes1
Has abstractyes

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