Comparison of Online Patient’s Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study (Preprint)

2021 ◽  
Author(s):  
Susan Park ◽  
So-Hyun Choi ◽  
Yun-Kyung Song ◽  
Jin-Won Kwon
Keyword(s):  
Social Media ◽  
Adverse Events ◽  
Signal Detection ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Media Analysis ◽  
Reporting Odds Ratio ◽  
Disproportionality Analysis ◽  
Drug Label

BACKGROUND Tramadol is known to cause fewer adverse events (AE) than other opioids. However, recent research has raised concerns about various safety issues. OBJECTIVE We aimed to explore these new AE related to tramadol using social media and conventional pharmacovigilance data. METHODS This study used two datasets, one from patients’ drug reviews on WebMD and one from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We analyzed 2,062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities (MedDRA). To analyze AE from FAERS, a disproportionality analysis was performed with three measures: the proportional reporting ratio (PRR), the reporting odds ratio (ROR), and the information component (IC). RESULTS From the 869 AE reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisfied all three signal detection criteria. In addition, 20 serious AE were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients’ symptom descriptions, tramadol-induced pain might also be an unexpected AE. CONCLUSIONS This study detected several novel signals related to tramadol use, suggesting newly identified possible AE. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. CLINICALTRIAL N/A

scholarly journals Risk of bronchospasm and coronary arteriospasm with sugammadex use: a post marketing analysis

2019 ◽  
Vol 10 ◽  
pp. 204209861986907 ◽  
Author(s):  
Pushkar Aggarwal
Keyword(s):  
Adverse Event ◽  
Adverse Events ◽  
Signal Analysis ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Odds Ratio ◽  
Potential Association ◽  
Chi Squared ◽  
Post Marketing

Introduction: Sugammadex is used for the reversal of neuromuscular blockade caused by rocuronium bromide and vecuronium bromide. As part of the post licensing phase of drug development, adverse events related to the use of sugammadex are still being uncovered and being reported. The potential association between sugammadex and adverse events bronchospasm and coronary arteriospasm using a retrospective pharmacovigilance signal analysis was carried out. Methods: Food and Drug Administration’s Adverse Event Reporting System database was used to run disproportionality analyses to investigate the potential association of sugammadex with bronchospasm or coronary arteriospasm. In this analysis we report the adverse event signal using frequentist methods of Relative reporting ratio (RRR), proportional reporting ratio (PRR), reporting odds ratio (ROR) and the Bayesian based Information Component metric. Results: A statistically significant disproportionality signal is found between sugammadex and bronchospasm ( n = 44; chi-squared = 2993.87; PRR = 71.95 [95% CI: 54.00–95.85]) and sugammadex and coronary arteriospasm ( n = 6; chi-squared = 209.39; PRR = 43.82 [95% CI: 19.73–97.33]) as per Evans criteria. Both statistically significant disproportionality signals persisted when stratified by gender. Based upon dynamic cumulative PRR graph, the PRR value has steadily increased and the 95% CI narrowed since December 2012. Conclusion: The results of the pharmacovigilance analysis highlight a statistically significant disproportionality signal between sugammadex usage and bronchospasm and coronary arteriospasm adverse events. Physicians need to be aware of these adverse events when using sugammadex. The results of the pharmacovigilance signal analysis highlight a statistically significant disproportionality signal between sugammadex usage and bronchospasm and coronary arteriospasm adverse events. Physicians need to be aware of these adverse events when using sugammadex.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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.

scholarly journals Disproportionality Analysis of Safety with Nafcillin and Oxacillin with the FDA Adverse Event Reporting System (FAERS)

2019 ◽  
Vol 64 (3) ◽  
Author(s):  
Tristan T. Timbrook ◽  
Lydia McKay ◽  
Jesse D. Sutton ◽  
Emily S. Spivak
Keyword(s):  
Adverse Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Lower Proportion ◽  
Reporting Odds Ratio ◽  
Disproportionality Analysis ◽  
Event Reporting ◽  
Content Type ◽  
Failure Reporting

ABSTRACT Antistaphylococcal penicillins such as nafcillin and oxacillin are among the first choices of treatment for severe invasive methicillin-susceptible Staphylococcus aureus (MSSA) infections, although there has been limited safety evaluations between individual agents. Using the FDA Adverse Event Reports System (FAERS), oxacillin was observed to have a lower proportion of reports of acute renal failure (reporting odds ratio [ROR], 5.3 [95% confidence interval {CI}, 3.1 to 9.3] versus 21.3 [95% CI, 15.8 to 28.6], respectively) and hypokalemia (ROR, 0.7 [95% CI, 0.1 to 4.8] versus 11.4 [95% CI, 7.1 to 18.3], respectively) than nafcillin.

scholarly journals SAFETY PROFILE OF MIRTAZAPINE: A REAL-WORLD DISPROPORTIONALITY

2021 ◽  
Vol 10 (2) ◽  
pp. 20-24
Author(s):  
Chenthamarai.G ◽  
Lakshmi Prasanna.T.
Keyword(s):  
Adverse Events ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Antidepressant Drug ◽  
Severe Depression ◽  
Epidemiological Studies ◽  
Reporting Odds Ratio ◽  
Disproportionality Analysis ◽  
Post Marketing Surveillance ◽  
Safety Signals

Introduction: Mirtazapine is an antidepressant drug that produces both noradrenergic and serotonergic activity. It is effective in treating of mild to severe depression. Proper evidence pointing to the safety of Mirtazapine is not established. The need for post-marketing surveillance (PMS) is considered most essential. This study was aimed to generate signal for unreported adverse drug reactions for Mirtazapine.  Materials and Methods: Our study retrospectively analyzed the AEs reported entered in the Adverse Events Reporting System (FAERS) databases in the last 10-years during the period of Jan 2011 to June 2020. Disproportionality analysis was done using Reporting Odds Ratio, Proportional Reporting Ratio, and Information Component with 95% confidence interval. Results: A disproportionality analysis was done for 41 adverse events, out of these, signal for 11 adverse events was found. ROR values 10.17 being the highest for abulia and 2.22 being the lowest for homicidal Ideation. The PRR value was 10.17 being the highest for abulia and 2.22 being the lowest for homicidal ideation. The IC025 value was 1.87 for abulia and 0.27 for homicidal Ideation. Conclusion: The present study using the Adverse Events Reporting System (FAERS) databases maintained by the FDA suggested new safety signals for Mirtazapine. Still more cohort and epidemiological studies are recommended to validate these results. Keywords: Mirtazapine, Disproportionality analysis, Safety Signals.

scholarly journals Real-world Safety Profile of Darunavir and its Boosted Agents: An Analysis of FDA Adverse Event Reporting System Database

2021 ◽  
Author(s):  
Xiaojiang Tian ◽  
Yao Yao ◽  
Guanglin He ◽  
Yuntao Jia ◽  
Kejing Wang ◽  
...  
Keyword(s):  
Adverse Event ◽  
Adverse Drug Events ◽  
Reporting System ◽  
Proportional Reporting Ratio ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Odds Ratio ◽  
Event Reporting ◽  
Exfoliative Dermatitis ◽  
Adverse Pregnancy

Abstract This current investigation was aimed to generate signals for adverse drug events of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All adverse event (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), 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 10756 AE reports were identified commonly observed in cardiovascular, endocrine, musculoskeletal, gastrointestinal, hepatobiliary, 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 associated with multiple adverse pregnancy conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.

scholarly journals Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System

2021 ◽  
Vol 12 ◽  
Author(s):  
Michele Fusaroli ◽  
Emanuel Raschi ◽  
Milo Gatti ◽  
Fabrizio De Ponti ◽  
Elisabetta Poluzzi
Keyword(s):  
Adverse Event ◽  
Signal Detection ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Reporting Odds Ratio ◽  
Event Reporting ◽  
Vein Thrombosis ◽  
Spontaneous Reports ◽  
Effective Signal

Introduction: The analysis of pharmacovigilance databases is crucial for the safety profiling of new and repurposed drugs, especially in the COVID-19 era. Traditional pharmacovigilance analyses–based on disproportionality approaches–cannot usually account for the complexity of spontaneous reports often with multiple concomitant drugs and events. We propose a network-based approach on co-reported events to help assessing disproportionalities and to effectively and timely identify disease-, comorbidity- and drug-related syndromes, especially in a rapidly changing low-resources environment such as that of COVID-19.Materials and Methods: Reports on medications administered for COVID-19 were extracted from the FDA Adverse Event Reporting System quarterly data (January–September 2020) and queried for disproportionalities (Reporting Odds Ratio corrected for multiple comparisons). A network (the Adversome) was estimated considering events as nodes and conditional co-reporting as links. Communities of significantly co-reported events were identified. All data and scripts employed are available in a public repository.Results: Among the 7,082 COVID-19 reports extracted, the seven most frequently suspected drugs (remdesivir, hydroxychloroquine, azithromycin, tocilizumab, lopinavir/ritonavir, sarilumab, and ethanol) have shown disproportionalities with 54 events. Of interest, myasthenia gravis with hydroxychloroquine, and cerebrovascular vein thrombosis with azithromycin. Automatic clustering identified 13 communities, including a methanol-related neurotoxicity associated with alcohol-based hand-sanitizers and a long QT/hepatotoxicity cluster associated with azithromycin, hydroxychloroquine and lopinavir-ritonavir interactions.Conclusion: Findings from the Adversome detect plausible new signals and iatrogenic syndromes. Our network approach complements traditional pharmacovigilance analyses, and may represent a more effective signal detection technique to guide clinical recommendations by regulators and specific follow-up confirmatory studies.

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