Evidence map›Paper›PMID 39290866›Full record

ArticleFrontiers in pharmacology2024

Signal mining of adverse events of proteasome inhibitors in multiple myeloma based on FAERS.

Yuan Peng, Yuying Zhou, Kaisen Shu, Xu Jia, Yan Zhong

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

5 authors.

Yuan Peng *Department of Pharmacy, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Yuying Zhou *The First Clinical Medical College, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Kaisen ShuSchool of Pharmacy, North Sichuan Medical College, Nanchong, Sichuan, China.
Xu JiaDepartment of Pharmacy, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Yan ZhongDepartment of Pharmacy, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital.C.T), Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To mine and analyze adverse events (AEs) related to proteasome inhibitors in multiple myeloma based on the FDA Adverse Event Reporting System (FAERS), providing references for rational clinical medication. Methods: AE data related to multiple myeloma proteasome inhibitors were collected from the FAERS from the first quarter of 2010 to the first quarter of 2024. Signal mining of AEs was conducted using the reporting odds ratio method and Bayesian confidence propagation neural network method. Results: A total of 8,805 reports for bortezomib, 5,264 for carfilzomib, and 8,771 for ixazomib were collected, with corresponding AE signals of 474, 279, and 287, respectively, involving 23, 21, and 22 System Organ Classes (SOCs). The report information for the three drugs tended to be consistent: more cases were reported in males than in females; the majority of patients were 65 years and over; AEs mostly occurred within 6 months of medication; the outcomes primarily consisted of hospitalization, prolonged hospital stay, and other serious adverse events; the primary reporting country was the United States. The most affected SOCs were infections and infestations, general disorders and administration site conditions, and blood and lymphatic system disorders. Conclusion: The overall distribution of AEs for the three multiple myeloma proteasome inhibitors was consistent, but there were certain differences in specific AE signal characteristics, which should be noted in clinical applications.

Indexed as

adverse eventadverse event reporting systemmultiple myelomaproteasome inhibitorssignal mining

Identifiers

PMID39290866
PMCPMC11405236

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Registered trials

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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.