Data Mining and Analysis of Adverse Events of Vedolizumab Based on the FAERS Database
Scientific reports(2025)SCI 3区
Abstract
This study aims to mine and analyze adverse events (AEs) of Vedolizumab based on the FAERS database to better understand its safety and potential risks in the real world. Data from the second quarter of 2014 to the third quarter of 2023 were collected, employing various signal mining methods such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). The study gathered 14,753,012 reports of AEs, of which 46,726 were related to Vedolizumab. Signal mining identified 401 Preferred Terms (PTs) involving 27 System Organ Classes (SOCs). There was an increasing trend in the number of reports, with a slightly higher proportion of reports from women compared to men, and the primary reporting group was adults, especially those aged between 18 and 65 years. New potential AE signals were identified, such as a higher incidence of Pregnancy, Haematochezia, and Clostridium difficile infection. Although less frequent, strong signals were noted for Incisional hernia, Intestinal fistula infection, Anastomotic complication, Drug metabolising enzyme increased, Gingival graft, Intestinal anastomosis complication, Anorectal infection, Perineal rash, and Abdominal hernia obstructive. Despite the positive prospects of Vedolizumab in the treatment of inflammatory bowel diseases, the AEs related to its use identified in this study, particularly the newly identified potential risks, suggest that even targeted therapies can have systemic effects beyond expectations.
MoreTranslated text
Key words
Vedolizumab,Inflammatory bowel disease,Adverse events,FAERS Database,Drug safety
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2001
被引用1269 | 浏览
2020
被引用234 | 浏览
2020
被引用18 | 浏览
2020
被引用38 | 浏览
2020
被引用129 | 浏览
2020
被引用74 | 浏览
2021
被引用6 | 浏览
2021
被引用24 | 浏览
2021
被引用6 | 浏览
2021
被引用45 | 浏览
2023
被引用1 | 浏览
2024
被引用43 | 浏览
2023
被引用12 | 浏览
2023
被引用28 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话