Statin-Associated Muscular and Renal Adverse Events: Data Mining of the Public Version of the FDA Adverse Event Reporting System

OBJECTIVE: Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) and to attempt to determine the rank-order of the association. METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. CONCLUSIONS: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events

Correlation versus Causation? Pharmacovigilance of the Analgesic Flupirtine Exemplifies the Need for Refined Spontaneous ADR Reporting

Annually, adverse drug reactions result in more than 2,000,000 hospitalizations and rank among the top 10 causes of death in the United States. Consequently, there is a need to continuously monitor and to improve the safety assessment of marketed drugs. Nonetheless, pharmacovigilance practice frequently lacks causality assessment. Here, we report the case of flupirtine, a centrally acting non-opioid analgesic. We re-evaluated the plausibility and causality of 226 unselected, spontaneously reported hepatobiliary adverse drug reactions according to the adapted Bradford-Hill criteria, CIOMS score and WHO-UMC scales. Thorough re-evaluation showed that only about 20% of the reported cases were probable or likely for flupirtine treatment, suggesting an incidence of flupirtine-related liver injury of 1∶ 100,000 when estimated prescription data are considered, or 0.8 in 10,000 on the basis of all 226 reported adverse drug reactions. Neither daily or cumulative dose nor duration of treatment correlated with markers of liver injury. In the majority of cases (151/226), an average of 3 co-medications with drugs known for their liver liability was observed that may well be causative for adverse drug reactions, but were reported under a suspected flupirtine ADR. Our study highlights the need to improve the quality and standards of ADR reporting. This should be done with utmost care taking into account contributing factors such as concomitant medications including over-the-counter drugs, the medical history and current health conditions, in order to avoid unjustified flagging and drug warnings that may erroneously cause uncertainty among healthcare professionals and patients, and may eventually lead to unjustified safety signals of useful drugs with a reasonable risk to benefit ratio

Inter-expert agreement of seven criteria in causality assessment of adverse drug reactions

What is already known about this subjectIn pharmacovigilance, many methods have been proposed for causality assessment of adverse drug reactions.Expert judgement is commonly used to evaluate the causal relationship between a drug treatment and the occurrence of an adverse event. This form of judgement relies either explicitly or implicitly on causality criteria.What this study addsOur study compares the judgements of five senior experts using global introspection about drug causation and seven causality criteria on a random set of putative adverse drug reactions.Even if previous publications have shown poor agreement between experts using global introspection, few have compared judgements of well trained pharmacologists, familiar with using a standardized causality assessment method

Drug-induced acute myocardial infarction: identifying 'prime suspects' from electronic healthcare records-based surveillance system.

BACKGROUND: Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings. OBJECTIVE: To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network. METHODS: Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible. RESULTS: Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate. LIMITATIONS: Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out. CONCLUSION: A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation