Evidence map›Paper›PMID 40792279›Full record

ArticleFrontiers in oncology2025

Disproportionality analysis of adverse events associated with pacritinib: a real-world study based on FDA Adverse Event Reporting System (FAERS) database.

Huiling Zhang, Yaping Huang, Chengjie Ke, Maohua Chen

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Huiling ZhangDepartment of Pharmacy, The First People's Hospital of Nanning, Nanning, China.
Yaping HuangDepartment of Pharmacy, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
Chengjie KeDepartment of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Maohua ChenDepartment of Pharmacy, Pingtan Comprehensive Experimental Area Hospital, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Pacritinib, a selective Janus kinase (JAK) inhibitor, is approved for the treatment of myelofibrosis in adults with severe thrombocytopenia. However, its safety profile in real-world populations remains unclear. The aim of study is provided a comprehensive profile of pacritinib's safety by evaluating the adverse events (AEs) using a real-world pharmacovigilance database. Methods: Data from the FDA Adverse Event Reporting System (FAERS) database, spanning from the first quarter of 2022 to the second quarter of 2024, served as the basis for this analysis. To identify potential AE risk signals, several disproportionality analysis methods were applied, including the reporting odds ratio, the proportional reporting ratio, the multi-item gamma Poisson shrinker, and the Bayesian confidence propagation neural network. Results: A total of 4,304,335 AE reports were collected from the FAERS, with 1,940 reports identifying pacritinib as the primary suspect drug. Significant disproportionality was observed in the following system organ classes: gastrointestinal disorders, investigations, and surgical and medical procedures. Common preferred terms were identified, including diarrhea, fatigue, death, nausea, platelet count decreased, and hemoglobin decreased. Notably, 26 off--label AEs were also identified. Discussion: Our study would provide valuable insights for the post-marketing safety surveillance and assessment of pacritinib, and guide its clinical practice.

Indexed as

adverse eventdisproportionality analysisFAERSpacritinibreal-world study

Identifiers

PMID40792279
PMCPMC12337178

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.