scholarly journals Three-Component Mixture Model-Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System

2018 ◽  
Vol 7 (8) ◽  
pp. 499-506 ◽  
Author(s):  
Pengyue Zhang ◽  
Meng Li ◽  
Chien-Wei Chiang ◽  
Lei Wang ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Adverse Event ◽  
Signal Detection ◽  
Mixture Model ◽  
Adverse Drug Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Component Mixture ◽  
Event Reporting

scholarly journals Analysis of anticholinergic adverse effects using two large databases: The US Food and Drug Administration Adverse Event Reporting System database and the Japanese Adverse Drug Event Report database

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260980
Author(s):  
Junko Nagai ◽  
Yoichi Ishikawa
Keyword(s):  
Adverse Event ◽  
Adverse Drug Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Beers Criteria ◽  
Event Report ◽  
Event Reporting ◽  
The Us

Introduction Anticholinergic adverse effects (AEs) are a problem for elderly people. This study aimed to answer the following questions. First, is an analysis of anticholinergic AEs using spontaneous adverse drug event databases possible? Second, what is the main drug suspected of inducing anticholinergic AEs in the databases? Third, do database differences yield different results? Methods We used two databases: the US Food and Drug Administration Adverse Event Reporting System database (FAERS) and the Japanese Adverse Drug Event Report database (JADER) recorded from 2004 to 2020. We defined three types of anticholinergic AEs: central nervous system (CNS) AEs, peripheral nervous system (PNS) AEs, and a combination of these AEs. We counted the number of cases and evaluated the ratio of drug–anticholinergic AE pairs between FAERS and JADER. We computed reporting odds ratios (RORs) and assessed the drugs using Beers Criteria®. Results Constipation was the most reported AE in FAERS. The ratio of drug–anticholinergic AE pairs was statistically significantly larger in FAERS than JADER. Overactive bladder agents were suspected drugs common to both databases. Other drugs differed between the two databases. CNS AEs were associated with antidementia drugs in FAERS and opioids in JADER. In the assessment using Beers Criteria®, signals were detected for almost all drugs. Between the two databases, a significantly higher positive correlation was observed for PNS AEs (correlation coefficient 0.85, P = 0.0001). The ROR was significantly greater in JADER. Conclusions There are many methods to investigate AEs. This study shows that the analysis of anticholinergic AEs using spontaneous adverse drug event databases is possible. From this analysis, various suspected drugs were detected. In particular, FAERS had many cases. The differences in the results between the two databases may reflect differences in the reporting countries. Further study of the relationship between drugs and CNS AEs should be conducted.

scholarly journals Assessment of Reye’s syndrome profile with data from the US Food and Drug Administration Adverse Event Reporting System and the Japanese Adverse Drug Event Report databases using the disproportionality analysis

2020 ◽  
Vol 8 ◽  
pp. 205031212097417
Author(s):  
Kiyoka Matsumoto ◽  
Shiori Hasegawa ◽  
Satoshi Nakao ◽  
Kazuyo Shimada ◽  
Ririka Mukai ◽  
...  
Keyword(s):  
Adverse Event ◽  
Adverse Drug Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Event Report ◽  
Reye's Syndrome ◽  
Event Reporting ◽  
The Us

Objectives: Reye’s syndrome is a rare and potentially fatal illness that is defined as encephalopathy accompanied by liver failure. The aim of this study was to assess Reye’s syndrome profiles by analyzing data from the spontaneous reporting system database. Methods: We analyzed reports of Reye’s syndrome using the US Food and Drug Administration Adverse Event Reporting System and the Japanese Adverse Drug Event Report databases. The reporting odds ratio and proportional reporting rate were used to detect the pharmacovigilance signal. Results: The US Food and Drug Administration Adverse Event Reporting System contains 12,201,620 reports from January 2004 to June 2020, of which 186 are on Reye’s syndrome. The Japanese Adverse Drug Event Report contains 646,779 reports from April 2004 to September 2020, of which 30 are on Reye’s syndrome. In the US Food and Drug Administration Adverse Event Reporting System database, the reporting odds ratios (95% confidence interval, number of cases) of aspirin, diclofenac, ibuprofen, acetaminophen, and valproate sodium were 404.6 (302.6–541.0, n = 80), 15.1 (6.7–34.1, n = 6), 26.2 (16.1–42.6, n = 18), 10.7 (5.5–20.9, n = 9), and 47.1 (26.2–84.6, n = 12), respectively. In the Japanese Adverse Drug Event Report database, the reporting odds ratios (95% confidence interval, number of cases) of aspirin, diclofenac, ibuprofen, loxoprofen, acetaminophen, and valproate sodium were 14.1 (5.4–36.8, n = 5), 51.7 (22.2–120.5, n = 7), 135.0 (40.8–446.2, n = 3), 17.6 (6.7–46.0, n = 5), 24.0 (9.2–62.6, n = 5), and 13.8 (3.3–57.9, n = 2), respectively. The reported number of female patients aged 30–39 years was the highest in the Japanese Adverse Drug Event Report. Conclusion: Although the frequency of the occurrence of Reye’s syndrome is low, the possible risk of the disease occurring in adult females should be considered.

scholarly journals 1977. Comparing Acute Kidney Injury Risk among Antibiotic Classes: A Study of the FDA Adverse Event Reporting System (FAERS)

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S662-S662
Author(s):  
Taylor M Patek ◽  
Chengwen Teng ◽  
Kaitlin E Kennedy ◽  
Christopher R Frei
Keyword(s):  
Acute Kidney Injury ◽  
Adverse Event ◽  
Injury Risk ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Event Reporting ◽  
Increased Risk

Abstract Background A recent article published in 2018 studied the FDA Adverse Event Reporting System (FAERS) and listed the most common medications associated with acute kidney injury (AKI) based on number of AKI reports. In regards to antibiotics, the study only ranked vancomycin, fluoroquinolones, penicillin combinations, and trimethoprim–sulfamethoxazole as having a significant association with AKI. The objective of this study was to evaluate those and additional antibiotic classes using FAERS, and to compare their risk associated with this adverse drug event. Methods FAERS reports from January 1, 2015 to December 31, 2017 were included in the study. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify AKI cases. Reporting Odds Ratios (RORs) and corresponding 95% confidence intervals (95% CI) for the association between antibiotics and AKI were calculated. An association was considered statistically significant when the lower limit of the 95% CI was greater than 1.0. Results A total of 2,042,801 reports (including 20,138 acute kidney injury reports) were considered, after inclusion criteria were applied. Colistin had the greatest proportion of AKI reports, representing 25% of all colistin reports. Acute kidney injury RORs (95% CI) for antibiotics were (in descending order): colistin 33.10 (21.24–51.56), aminoglycosides 17.41 (14.49–20.90), vancomycin 15.28 (13.82–16.90), trimethoprim-sulfamethoxazole 13.72 (11.94–15.76), penicillin combinations 7.95 (7.09–8.91), clindamycin 6.46 (5.18–8.04), cephalosporins 6.07 (5.23–7.05), daptomycin 6.07 (4.61–7.99), macrolides 3.60 (3.04–4.26), linezolid 3.48 (2.54–4.77), carbapenems 3.31 (2.58–4.25), metronidazole 2.55 (1.94–3.36), tetracyclines 1.73 (1.26–2.36), and fluoroquinolones 1.71 (1.49–1.97). Conclusion This study found 17 classes of antibiotics and combinations that were significantly associated with AKI compared with four antibiotics that were mentioned in a recently published article looking at drug-associated AKI. While this study confirmed previous literature of certain antibiotics associated with increased risk of AKI, it also compared antibiotics within classes and provided additional insight regarding which antibiotics had the highest associated risk of an AKI. Disclosures All authors: No reported disclosures.

Adverse Drug Events in the Prevention and Treatment of COVID-19: A Data Mining Study on FDA Adverse Event Reporting System (FAERS) (Preprint)

2021 ◽  
Author(s):  
Qiang Guo ◽  
Shaojun Duan ◽  
Yaxi Liu ◽  
Yinxia Yuan
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Drug Events ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Event Reporting ◽  
Prevention And Treatment

BACKGROUND In the emergency situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs so as to help health professionals and patients get rid of these risks. OBJECTIVE This pharmacovigilance study aimed to investigate the ADEs of “Hot Drugs” in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). METHODS FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2021 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, proportional reporting ratio (PRR) and Chi-square (2) test was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. RESULTS 13,178 COVID-19 cases were retrieved with 18 “Hot Drugs” and 312 frequent ADEs on “Preferred Term” (PT) level. 18  312 = 5,616 “Drug & ADE” candidates were formed for further data mining. The algorithm finally produced 219 significant ADE signals associated with 17 “Hot Drugs”and 124 ADEs.Some unexpected ADE signals were observed for chloroquine, ritonavir, tocilizumab, Oxford/AstraZeneca COVID-19 Vaccine and Moderna COVID-19 Vaccine. CONCLUSIONS Data mining is a promising and efficient way to assist pharmacovigilance work and the result of this paper could help timely recognize ADEs in the prevention and treatment of COVID-19.

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