Drug safety of rosiglitazone and pioglitazone in France: a study using the French PharmacoVigilance database

International audienceBackgroundThiazolidinediones (TZDs), rosiglitazone (RGZ) and pioglitazone (PGZ) are widely used as hypoglycemic drugs in patients with type 2 diabetes mellitus. The aim of our study was to investigate the profile of adverse drug reactions (ADRs) related to TZDs and to investigate potential risk factors of these ADRs.MethodsType 2 diabetic patients were identified from the French Database of PharmacoVigilance (FPVD) between 2002 and 2006. We investigated ADR related to TZD, focusing on 4 ADR: edema, heart failure, myocardial infarction and hepatitis corresponding to specific WHO-ART terms.ResultsAmong a total of 99,284 adult patients in the FPVD, 2295 reports concerned type 2 diabetic patients (2.3% of the whole database), with 161 (7%) exposed to TZDs. The frequency of edema and cardiac failure was significantly higher with TZDs than in other patients (18% and 7.4% versus 0.8% and 0.1% respectively, p ConclusionsThiazolidinediones exposure is associated with an increased risk of edema and heart failure in patients with type 2 diabetes even when recommendations for use are respected. In contrast, the risk of hepatic reactions and myocardial infarction with this class of drugs seems to be similar to other hypoglycemic agents

Spontaneous Reporting System Modelling for Data Mining Methods Evaluation in Pharmacovigilance

International audienceThe pharmacovigilance aims at detecting adverse effects of marketed drugs. It is based on the spontaneous reporting of events that are supposed to be adverse effects of drugs. The Spontaneous Reporting System (SRS) is supplying huge databases that pharma-covigilance experts cannot exhaustively exploit without any data mining tools. Data mining methods have been proposed in the literature but none of them is the object of a consensus in terms of applicability and efficiency. It is especially due to the difficulties to evaluate the methods on real data. In this context, the aim of this paper is to propose the SRS modelling in order to simulate realistic data that would permit to complete the methods evaluation and comparison , with the perspective to help in defining surveillance strategies. In fact, as the status of the drug-event relations is known in the simulated dataset, the signal generated by the data mining methods can be labelled as " true " or " false ". Spontaneous Reporting process is viewed as a Poisson process depending on the drugs exposure frequency, on the delay from the drugs launch, on the adverse events background incidence and seriousness and on a reporting probability. This reporting probability , quantitatively unknown, is derived from the qualitative knowledge found in literature and expressed by experts. This knowledge is represented and exploited by means of a fuzzy characterisation of variables and a set of fuzzy rules. Simulated data are described and two Bayesian data mining methods are applied to illustrate the kind of information, on methods performances, that can be derived from the SRS modelling and from the data simulation

Revue d'Épidémiologie et de Santé Publique

Introduction L’analyse des données de vie réelle permet d’étudier l’utilisation des médicaments après leur mise sur le marché. Le projet Domino a pour objectif de détecter automatiquement les mésusages médicamenteux mentionnés sur les médias sociaux, tels que la prise de médicaments pour une mauvaise indication ou en présence d’une contre-indication. Méthodes La première étape a consisté à extraire automatiquement les fils de discussion sur les forums en santé au format HTML puis de les transformer au format SIOC, un standard issu du web sémantique pour représenter les discussions en ligne. Une terminologie ouverte du médicament, Romedi, a été développée et utilisée pour filtrer les messages mentionnant au moins un médicament. Une étape de reconnaissance d’entités nommées basée sur le métathésaurus de l’UMLS a permis d’extraire automatiquement les symptômes et les maladies. Ces derniers ont ensuite été comparés aux indications présentes dans les résumés des caractéristiques du produit (RCP). Un signal de mésusage a été généré lorsqu’un symptôme ou une maladie hors indication étaient fréquemment associés à un médicament. Une interface a été développée pour visualiser les signaux générés par l’algorithme afin de les valider ou de les invalider par un expert du domaine. Résultats Parmi 5 millions de messages extraits des réseaux sociaux, environ 189 000 contenaient la mention d’au moins un médicament. Les 1000 premiers signaux générés par l’algorithme ont été revus par un pharmacien. Au total, 26 signaux de mésusage ont été validés manuellement. Le principal mésusage détecté concernait la doxylamine, traitement contre les insomnies occasionnelles utilisé comme antiémétique. Les nombreux faux positifs reflètent la difficulté de comparer le langage médical utilisé dans les RCP et le langage patient, imprécis, utilisé sur les forums. Discussion/conclusion L’analyse des messages publiés sur les réseaux sociaux est capable d’identifier des mésusages médicamenteux. La principale difficulté pour la détection du mésusage réside dans l’identification du motif de la prise d’un médicament dans le langage patient. D’autres approches devront être développées pour améliorer ces résultats

Mining clinical big data for drug safety Detecting inadequate treatment with a DNA sequence alignment algorithm

International audienc

Mining clinical big data for drug safety Detecting inadequate treatment with a DNA sequence alignment algorithm

International audienc

Validation study in four health-care databases: upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk

Objective: To evaluate the accuracy of disease codes and free text in identifying upper gastrointestinal bleeding (UGIB) from electronic health-care records (EHRs). Study Design and Setting: We conducted a validation study in four European electronic health-care record (EHR) databases such as Integrated Primary Care Information (IPCI), Health Search/CSD Patient Database (HSD), ARS, and Aarhus, in which we identified UGIB cases using free text or disease codes: (1) International Classification of Disease (ICD)-9 (HSD, ARS); (2) ICD-10 (Aarhus); and (3) International Classification of Primary Care (ICPC) (IPCI). From each database, we randomly selected and manually reviewed 200 cases to calculate positive predictive values (PPVs). We employed different case definitions to assess the effect of outcome misclassification on estimation of risk of drug-related UGIB. Results: PPV was 22% [95% confidence interval (CI): 16, 28] and 21% (95% CI: 16, 28) in IPCI for free text and ICPC codes, respectively. PPV was 91% (95% CI: 86, 95) for ICD-9 codes and 47% (95% CI: 35, 59) for free text in HSD. PPV for ICD-9 codes in ARS was 72% (95% CI: 65, 78) and 77% (95% CI: 69, 83) for ICD-10 codes (Aarhus). More specific definitions did not have significant impact on risk estimation of drug-related UGIB, except for wider CIs. Conclusions: ICD-9-CM and ICD-10 disease codes have good PPV in identifying UGIB from EHR; less granular terminology (ICPC) may require additional strategies. Use of more specific UGIB definitions affects precision, but not magnitude, of risk estimates. (C) 2014 Elsevier Inc. All rights reserved