scholarly journals 1862P Atezolizumab and severe cutaneous adverse reactions: Data mining of the FDA Adverse Event Reporting System (FAERS) database

2021 ◽  
Vol 32 ◽  
pp. S1251
Author(s):  
A. Pecere ◽  
G.C. Bisinella
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Reactions ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Event Reporting ◽  
Severe Cutaneous Adverse Reactions ◽  
Cutaneous Adverse Reactions

scholarly journals Systematic analysis of safety profile for darunavir and its boosted agents using data mining in the FDA Adverse Event Reporting System database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaojiang Tian ◽  
Yao Yao ◽  
Guanglin He ◽  
Yuntao Jia ◽  
Kejing Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Odds Ratio ◽  
Systematic Analysis ◽  
Event Reporting ◽  
Exfoliative Dermatitis

AbstractThis current investigation was aimed to generate signals for adverse events (AEs) of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All AE reports for darunavir, darunavir/ritonavir, or darunavir/cobicistat between July 2006 and December 2019 were identified. The reporting Odds Ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to detect the risk signals. A suspicious signal was generated only if the results of the three algorithms were all positive. A total of 10,756 reports were identified commonly observed in hepatobiliary, endocrine, cardiovascular, musculoskeletal, gastrointestinal, metabolic, and nutrition system. 40 suspicious signals were generated, and therein 20 signals were not included in the label. Severe high signals (i.e. progressive extraocular muscle paralysis, acute pancreatitis, exfoliative dermatitis, acquired lipodystrophy and mitochondrial toxicity) were identified. In pregnant women, umbilical cord abnormality, fetal growth restriction, low birth weight, stillbirth, premature rupture of membranes, premature birth and spontaneous abortion showed positive signals. Darunavir and its boosted agents induced AEs in various organs/tissues, and were shown to be possibly associated with multiple adverse pregnant conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.

Linezolid and the risk of lactic acidosis: Data mining and analysis of the FDA Adverse Event Reporting System

2020 ◽  
Vol 45 (6) ◽  
pp. 1422-1426 ◽  
Author(s):  
Ying Dai ◽  
Yingying Wang ◽  
Yifan Zeng ◽  
Chunhong Zhang ◽  
Ziye Zhou ◽  
...  
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Lactic Acidosis ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Event Reporting ◽  
Data Mining And Analysis

Using Data Mining to Predict Safety Actions from FDA Adverse Event Reporting System Data

2007 ◽  
Vol 41 (5) ◽  
pp. 633-643 ◽  
Author(s):  
Alan M. Hochberg ◽  
Stephanie J. Reisinger ◽  
Ronald K. Pearson ◽  
Donald J. O’Hara ◽  
Kevin Hall
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Event Reporting ◽  
System Data ◽  
Using Data

Comparison of Adverse Drug Reactions for Patients Treated with Tyrosine Kinase Inhibitors: Data Mining of the Public Version of the FDA Adverse Event Reporting System (AERS)

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1698-1698
Author(s):  
Tetsuya Tanimoto ◽  
Yasuo Oshima ◽  
Koichiro Yuji ◽  
Masahiro Kami
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Odds Ratio ◽  
Kinase Inhibitors ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Reactions ◽  
Suspected Drug ◽  
Event Reporting

Abstract Abstract 1698 Backgrounds: The consecutive approvals of tyrosine kinase inhibitors (TKIs) have been changing the landscape of treatment strategy for patients with chronic myeloid leukemia (CML). Currently, three TKIs are available worldwide, including imatinib (Glivec/Gleevec; Novartis Pharmaceuticals, East hanover, NJ), nilotinib (Tasigna; Novartis Pharmaceuticals) and dasatinib (Sprycel; Bristol-Myers Squibb, Princeton, NJ). Although second generation TKIs (nilotinib and dasatinib) have shown their efficacy and safety in recent clinical trials, additional data are needed for better understanding and differences in their safety profiles may be helpful when choosing a TKI. We compared the adverse drug reactions (ADRs) for patients treated with three TKIs using spontaneous adverse event reporting after approval to investigate the characteristics of safety profiles. Method: To compare adverse events characteristics among three TKIs, the case/noncase adverse events reports associated with TKIs use were retrieved from the U.S. Food and Drug Administration Adverse Event Reporting System (AERS) between 2004 and 2010. We calculated the reporting odds ratio (ROR), which is known as one of data mining algorithms for signal detection techniques of ADRs, characterized by providing a fast and cost-efficient way of detecting possible ADR signals. All events in the AERS have been coded for data entry in accordance with the standardized terminology, known as Preferred Terms, in the Medical Dictionary for Regulatory Activities. The ROR is similar to the idea of odds ratio, calculating the odds of exposure of the suspected drug in patients who had events divided by the odds of exposure of the suspected drug in those without events. The ROR -1.96 standard error greater than 1 with at least 4 ADR reports was used as a signal criterion in this study. Results: We identified 18,979 ADRs for imatinib, 5,388 ADRs for nilotinib, and 2,482 ADRs for dasatinib. The number of ADRs flagged by our signal criterion was 91 for imatinib, 82 for nilotinib, and 109 for dasatinib. Top 10 lists of ADRs with higher ROR are shown in Table for each TKI. The safety profiles were almost different among TKIs. ADRs related to skin and hepatic function were noted for imatinib, whereas ADRs related to cardiac events were prominent for nilotinib, and ADRs related to lymphocytosis, edema and effusion were noticeable for dasatinib. The different dosing requirements of dasatinib and nilotinib may be an additional factor of ADRs. Conclusions: ADRs reported in the AERS for each TKI were relatively consistent with known characteristics of ADRs reported in previous clinical trials. Our information would be supportive data for choosing a TKI for CML patients based on comorbidities and drug safety profiles. The choice of therapy in a given patient with CML may depend on age, past history and comorbidities as well as disease risk score and mutational analysis. Disclosures: Oshima: Sanofi Aventis: Employment.

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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