Literature DB >> 28681416

Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network.

Jon D Duke1,2, Patrick B Ryan1,3,4, Marc A Suchard1,5, George Hripcsak1,4, Peng Jin1,4, Christian Reich1,6, Marie-Sophie Schwalm1,6, Yuriy Khoma1,7,8, Yonghui Wu1,9, Hua Xu1,9, Nigam H Shah1,10, Juan M Banda1,10, Martijn J Schuemie1,3.   

Abstract

Recent adverse event reports have raised the question of increased angioedema risk associated with exposure to levetiracetam. To help address this question, the Observational Health Data Sciences and Informatics research network conducted a retrospective observational new-user cohort study of seizure patients exposed to levetiracetam (n = 276,665) across 10 databases. With phenytoin users (n = 74,682) as a comparator group, propensity score-matching was conducted and hazard ratios computed for angioedema events by per-protocol and intent-to-treat analyses. Angioedema events were rare in both the levetiracetam and phenytoin groups (54 vs. 71 in per-protocol and 248 vs. 435 in intent-to-treat). No significant increase in angioedema risk with levetiracetam was seen in any individual database (hazard ratios ranging from 0.43 to 1.31). Meta-analysis showed a summary hazard ratio of 0.72 (95% confidence interval [CI] 0.39-1.31) and 0.64 (95% CI 0.52-0.79) for the per-protocol and intent-to-treat analyses, respectively. The results suggest that levetiracetam has the same or lower risk for angioedema than phenytoin, which does not currently carry a labeled warning for angioedema. Further studies are warranted to evaluate angioedema risk across all antiepileptic drugs. Wiley Periodicals, Inc.
© 2017 International League Against Epilepsy.

Entities:  

Keywords:  Adverse drug reactions; Angioedema; Anticonvulsant hypersensitivity syndrome; Levetiracetam; Observational research; Pharmacovigilance

Mesh:

Substances:

Year:  2017        PMID: 28681416      PMCID: PMC6632067          DOI: 10.1111/epi.13828

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  16 in total

1.  How Confident Are We about Observational Findings in Healthcare: A Benchmark Study.

Authors:  Martijn J Schuemie; M Soledad Cepeda; Marc A Suchard; Jianxiao Yang; Yuxi Tian; Alejandro Schuler; Patrick B Ryan; David Madigan; George Hripcsak
Journal:  Harv Data Sci Rev       Date:  2020-01-31

2.  Expanding transplant outcomes research opportunities through the use of a common data model.

Authors:  Sylvia Cho; Sumit Mohan; Syed Ali Husain; Karthik Natarajan
Journal:  Am J Transplant       Date:  2018-05-22       Impact factor: 8.086

3.  Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm.

Authors:  Rui Duan; Mary Regina Boland; Zixuan Liu; Yue Liu; Howard H Chang; Hua Xu; Haitao Chu; Christopher H Schmid; Christopher B Forrest; John H Holmes; Martijn J Schuemie; Jesse A Berlin; Jason H Moore; Yong Chen
Journal:  J Am Med Inform Assoc       Date:  2020-03-01       Impact factor: 4.497

4.  Data Consult Service: Can we use observational data to address immediate clinical needs?

Authors:  Anna Ostropolets; Philip Zachariah; Patrick Ryan; Ruijun Chen; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2021-09-18       Impact factor: 7.942

5.  Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting.

Authors:  Yoon Gi Chung; Yonghoon Jeon; Sooyoung Yoo; Hunmin Kim; Hee Hwang
Journal:  Clin Exp Pediatr       Date:  2021-11-26

6.  Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model.

Authors:  Hunmin Kim; Sooyoung Yoo; Yonghoon Jeon; Soyoung Yi; Seok Kim; Sun Ah Choi; Hee Hwang; Ki Joong Kim
Journal:  Front Neurol       Date:  2020-05-12       Impact factor: 4.003

7.  Low-density lipoprotein cholesterol outcomes post-non-PCSK9i lipid-lowering therapies in atherosclerotic cardiovascular disease and probable heterozygous familial hypercholesterolemia patients.

Authors:  Chi-Chang Chen; Pallavi B Rane; Dionne M Hines; Jeetvan Patel; David J Harrison; Rolin L Wade
Journal:  Ther Clin Risk Manag       Date:  2018-12-13       Impact factor: 2.423

8.  Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.

Authors:  Rui Duan; Chongliang Luo; Martijn J Schuemie; Jiayi Tong; C Jason Liang; Howard H Chang; Mary Regina Boland; Jiang Bian; Hua Xu; John H Holmes; Christopher B Forrest; Sally C Morton; Jesse A Berlin; Jason H Moore; Kevin B Mahoney; Yong Chen
Journal:  J Am Med Inform Assoc       Date:  2020-07-01       Impact factor: 4.497

9.  Comparative First-Line Effectiveness and Safety of ACE (Angiotensin-Converting Enzyme) Inhibitors and Angiotensin Receptor Blockers: A Multinational Cohort Study.

Authors:  RuiJun Chen; Marc A Suchard; Harlan M Krumholz; Martijn J Schuemie; Steven Shea; Jon Duke; Nicole Pratt; Christian G Reich; David Madigan; Seng Chan You; Patrick B Ryan; George Hripcsak
Journal:  Hypertension       Date:  2021-07-26       Impact factor: 9.897

10.  Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI).

Authors:  Brian E Dixon; Chen Wen; Tony French; Jennifer L Williams; Jon D Duke; Shaun J Grannis
Journal:  BMJ Health Care Inform       Date:  2020-03
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.