Dataset Information

Correlation versus causation? Pharmacovigilance of the analgesic flupirtine exemplifies the need for refined spontaneous ADR reporting.

ABSTRACT: 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.

SUBMITTER: Anderson N

PROVIDER: S-EPMC3191146 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Correlation versus causation? Pharmacovigilance of the analgesic flupirtine exemplifies the need for refined spontaneous ADR reporting.

Anderson Nora N Borlak Juergen J

PloS one 20111011 10

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 report ...[more]

PMID: 22022383

Similar Datasets

Project description:IntroductionEvidence is lacking on withdrawal syndrome related to individual antidepressants and relevant risk factors for severe reactions.ObjectiveTo ascertain whether antidepressants are associated with an increased reporting of withdrawal syndrome as compared with other medications, and to investigate risk factors for severe reactions.MethodsThis is a case/non-case pharmacovigilance study, based on the VigiBase®, the WHO global database of individual case safety reports of suspected adverse drug reactions. We performed a disproportionality analysis of reports of antidepressant-related withdrawal syndrome (calculating reporting odds ratio [ROR] and Bayesian information component [IC]). We compared antidepressants to all other drugs, to buprenorphine (positive control), and to each other within each class of antidepressants (selective serotonin reuptake inhibitors [SSRIs], tricyclics and other antidepressants). Antidepressants with significant disproportionate reporting were ranked in terms of clinical priority. Serious versus non-serious reactions were compared.ResultsThere were 31,688 reports of antidepressant-related withdrawal syndrome were found. A disproportionate reporting was detected for 23 antidepressants. The estimated ROR for antidepressants altogether, compared to all other drugs, was 14.26 (95% CI 14.08-14.45), 17.01 for other antidepressants (95% CI 16.73-17.29), 13.65 for SSRIs (95% CI 13.41-13.90) and 2.8 for tricyclics (95% CI 2.59-3.02). Based on clinical priority ranking, the strongest disproportionate reporting was found for paroxetine, duloxetine, venlafaxine and desvenlafaxine, being comparable to buprenorphine. Withdrawal syndrome was reported as severe more often in males, adolescents, persons in polypharmacy, and with a longer antidepressant treatment duration (p < 0.05).ConclusionsAntidepressants are associated with an increased reporting of withdrawal syndrome compared with other drug classes. When prescribing and discontinuing antidepressants, clinicians should be aware of the potentially different proclivity of withdrawal syndrome across individual antidepressants, and the liability to experience more severe withdrawal symptoms in relation to specific patient characteristics.

Project description:Classical methods used for signal detection in pharmacovigilance rely on disproportionality analysis of counts aggregating spontaneous reports of a given adverse drug reaction. In recent years, alternative methods have been proposed to analyze individual spontaneous reports such as penalized multiple logistic regression approaches. These approaches address some well-known biases resulting from disproportionality methods. However, while penalization accounts for computational constraints due to high-dimensional data, it raises the issue of determining the regularization parameter and eventually that of an error-controlling decision rule. We present a new automated signal detection strategy for pharmacovigilance systems, based on propensity scores (PS) in high dimension. PSs are increasingly used to assess a given association with high-dimensional observational healthcare databases in accounting for confusion bias. Our main aim was to develop a method having the same advantages as multiple regression approaches in dealing with bias, while relying on the statistical multiple comparison framework as regards decision thresholds, by considering false discovery rate (FDR)-based decision rules. We investigate four PS estimation methods in high dimension: a gradient tree boosting (GTB) algorithm from machine-learning and three variable selection algorithms. For each (drug, adverse event) pair, the PS is then applied as adjustment covariate or by using two kinds of weighting: inverse proportional treatment weighting and matching weights. The different versions of the new approach were compared to a univariate approach, which is a disproportionality method, and to two penalized multiple logistic regression approaches, directly applied on spontaneous reporting data. Performance was assessed through an empirical comparative study conducted on a reference signal set in the French national pharmacovigilance database (2000-2016) that was recently proposed for drug-induced liver injury. Multiple regression approaches performed better in detecting true positives and false positives. Nonetheless, the performances of the PS-based methods using matching weights was very similar to that of multiple regression and better than with the univariate approach. In addition to being able to control FDR statistical errors, the proposed PS-based strategy is an interesting alternative to multiple regression approaches.

Project description:BackgroundAdverse drug reactions (ADRs) and medication errors in children may result from a lack of appropriate drugs, dosages, and pharmaceutical forms. In addition, children may respond differently to drugs than adults. Reporting of ADRs in the pediatric population is therefore of importance in order to increase the amount of safety data. However, different methodological approaches are used to collect ADRs.ObjectiveThe aim of the present study was to analyze whether there were differences in the ADRs collected in the KiDSafe project (845 ADR reports) compared with the spontaneous ADR reports sent to EudraVigilance (697 reports) in the same time period. The strengths and limitations of these two different approaches should be discussed.MethodsThe same inclusion criteria were applied for the systematically collected ADRs in the KiDSafe project and the spontaneous reports from EudraVigilance, and only reports of ADRs coded with hospitalization were considered. In both datasets, the number of reports (related to number of hospitals), their documentation quality (VigiGrade), causal relationship (World Health Organization-Uppsala Monitoring Centre [WHO-UMC] criteria), most frequently reported drugs and ADRs, demographical parameters of the patients, reported medical histories, and the seriousness of ADR reports were analyzed descriptively. The results of the two analyses were compared.ResultsThere was considerable underreporting of ADRs via the spontaneous reports (0.4 reports per hospital; 697/1902) compared with 70.4 reports per hospital (845/12) in the systematically collected KiDSafe reports. Documentation quality assessment yielded similar results in both datasets. Among the 10 most frequently reported drugs, anticonvulsants such as levetiracetam (6.6%), valproic acid (5.6%), oxcarbazepine (3.6%), and lamotrigine (3.4%) were mainly reported in the KiDSafe reports, while in the EudraVigilance reports, mite allergen extract (4.4%) and allergens (3.6%) were preferentially reported. Seizures were the most frequently reported clinically specific ADRs in the KiDSafe reports, whereas anaphylactic reactions and urticaria were prominent in the spontaneous reports from EudraVigilance. Notably, the proportion of reports referring to medication errors and other medication safety related issues were more prominent in KiDSafe than in the spontaneous reports (27.8% vs. 12.6% and 46.0% vs. 29.0%, respectively).ConclusionIn general, reports from both data sources contributed to the identification of ADRs and dedicated issues related to drug therapy. However, these differed by nature and strength of the signal, likely due to the characteristics of the individual method. A combined approach could likely compensate for limitations inherent to the single approaches, but will most likely only be applied to dedicated pharmacovigilance topics or research objectives.

Dataset Information

Correlation versus causation? Pharmacovigilance of the analgesic flupirtine exemplifies the need for refined spontaneous ADR reporting.

Publications

Correlation versus causation? Pharmacovigilance of the analgesic flupirtine exemplifies the need for refined spontaneous ADR reporting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets