Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: Data mining study
ABSTRACT To examine the relation between antipsychotic drugs and myocarditis and cardiomyopathy.
Data mining using bayesian statistics implemented in a neural network architecture.
International database on adverse drug reactions run by the World Health Organization programme for international drug monitoring. Main outcome measures: Reports mentioning antipsychotic drugs, cardiomyopathy, or myocarditis.
A strong signal existed for an association between clozapine and cardiomyopathy and myocarditis. An association was also seen with other antipsychotics as a group. The association was based on sufficient cases with adequate documentation and apparent lack of confounding to constitute a signal. Associations between myocarditis or cardiomyopathy and lithium, chlorpromazine, fluphenazine, haloperidol, and risperidone need further investigation.
Some antipsychotic drugs seem to be linked to cardiomyopathy and myocarditis. The study shows the potential of bayesian neural networks in analysing data on drug safety.
Full-textDOI: · Available from: David M Coulter, Sep 27, 2015
- SourceAvailable from: Paul Wicks
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- "Hypotheses of suspected ARs first highlighted in automated knowledge discovery, which remain after clinical review, are then communicated to industry/regulators and published as appropriate [54, 55]. However, the risk of distortion from undiscovered data quality problems and the difficulty of obtaining complete, detailed information on reported AR incidents mean that signals of suspected ARs often remain tentative, even after clinical review . "
ABSTRACT: The Patient-Reported Outcomes Safety Event Reporting (PROSPER) Consortium was convened to improve safety reporting by better incorporating the perspective of the patient. PROSPER comprises industry, regulatory authority, academic, private sector and patient representatives who are interested in the area of patient-reported outcomes of adverse events (PRO-AEs). It has developed guidance on PRO-AE data, including the benefits of wider use and approaches for data capture and analysis. Patient-reported outcomes (PROs) encompass the full range of self-reporting, rather than only patient reports collected by clinicians using validated instruments. In recent years, PROs have become increasingly important across the spectrum of healthcare and life sciences. Patient-centred models of care are integrating shared decision making and PROs at the point of care; comparative effectiveness research seeks to include patients as participatory stakeholders; and industry is expanding its involvement with patients and patient groups as part of the drug development process and safety monitoring. Additionally, recent pharmacovigilance legislation from regulatory authorities in the EU and the USA calls for the inclusion of patient-reported information in benefit–risk assessment of pharmaceutical products. For patients, technological advancements have made it easier to be an active participant in one’s healthcare. Simplified internet search capabilities, electronic and personal health records, digital mobile devices, and PRO-enabled patient online communities are just a few examples of tools that allow patients to gain increased knowledge about conditions, symptoms, treatment options and side effects. Despite these changes and increased attention on the perceived value of PROs, their full potential has yet to be realised in pharmacovigilance. Current safety reporting and risk assessment processes remain heavily dependent on healthcare professionals, though there are known limitations such as under-reporting and discordant perspectives between patient reports and clinician perceptions of adverse outcomes. PROSPER seeks to support the wider use of PRO-AEs. The scope of this guidance document, which was completed between July 2011 and March 2013, considered a host of domains related to PRO-AEs, including definitions and suitable taxonomies, the range of datasets that could be used, data collection mechanisms, and suitable analytical methodologies. PROSPER offers an innovative framework to differentiate patient populations. This framework considers populations that are prespecified (such as those in clinical trials, prospective observational studies and some registries) and non-prespecified populations (such as those in claims databases, PRO-enabled online patient networks, and social websites in general). While the main focus of this guidance is on post-approval PRO-AEs from both prespecified and non-prespecified population groups, PROSPER has also considered pre-approval, prespecified populations. The ultimate aim of this guidance is to ensure that the patient ‘voice’ and perspective feed appropriately into collection of safety data. The guidance also covers a minimum core dataset for use by industry or regulators to structure PRO-AEs (accessible in the online appendix) and how data, once collected, might be evaluated to better inform on the safe and effective use of medicinal products. Structured collection of such patient data can be considered both a means to an end (improving patient safety) as well as an end in itself (expressing the patient viewpoint). The members of the PROSPER Consortium therefore direct this PRO-AE guidance to multiple stakeholders in drug safety, including industry, regulators, prescribers and patients. The use of this document across the entirety of the drug development life cycle will help to better define the benefit–risk profile of new and existing medicines. Because of the clinical relevance of ‘real-world’ data, PROs have the potential to contribute important new knowledge about the benefits and risks of medicinal products, communicated through the voice of the patient. Electronic supplementary material The online version of this article (doi:10.1007/s40264-013-0113-z) contains supplementary material, which is available to authorized users.Drug Safety 10/2013; 36(12). DOI:10.1007/s40264-013-0113-z · 2.82 Impact Factor
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- "Fluphenazine, a phenothiazine derivative, is a neuroleptic drug used to treat psychoses such as schizophrenia and manic disorders (Iqbal et al., 2005). Fluphenazine has been significantly associated with myocarditis and cardiomyopathy (Coulter et al., 2001). As for the effect of the drug on cardiac rhythmicity, fluphenazine was identified as significant predictor for QT prolongation (Chong et al., 2003; Turbott et al., 1987) and is known to induce torsades de pointes (Crouch et al., 2003). "
ABSTRACT: Fluphenazine is a potent antipsychotic drug that can increase action potential duration and induce QT prolongation in several animal models and in humans. As the block of cardiac human ether-a-go-go-related gene (hERG) channels is one of the leading causes of acquired long QT syndrome, we investigated the acute effects of fluphenazine on hERG channels to determine the electrophysiological basis for its proarrhythmic potential. Fluphenazine at concentrations of 0.1∼1.0μM increased the action potential duration at 90% of repolarization (APD(90)) and action potential duration at 50% of repolarization (APD(50)) in 5min when action potentials were elicited under current-clamp conditions in guinea pig ventricular myocytes. We examined the effects of fluphenazine on hERG channels expressed in Xenopus oocytes and HEK293 cells using two-microelectrode voltage-clamp and patch-clamp techniques. The IC(50) for the fluphenazine -induced block of hERG currents in HEK293 cells at 36°C was 0.102μM at +20mV. Fluphenazine induced a concentration-dependent decrease of the current amplitude at the end of the voltage steps and hERG tail currents. The fluphenazine-dependent hERG block in Xenopus oocytes increased progressively relative to the degree of depolarization. Fluphenazine affected the channels in the activated and inactivated states but not in the closed states, and the S6 domain mutation from tyrosine to alanine at amino acid 652 (Y652A) attenuated the hERG current block. These results suggest that the antipsychotic drug fluphenazine is a potent blocker of hERG channels, providing a molecular mechanism for the drug-induced arrhythmogenic side effects.European journal of pharmacology 02/2013; 702(1-3). DOI:10.1016/j.ejphar.2013.01.039 · 2.53 Impact Factor
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- "Some studies have shown that atypical antipsychotics may be associated with an increased risk of QT prolongation as with typical antipsychotics (Ray et al., 2009). However, it may be that the mechanism underlying the association with SUD is not always related to the QT interval (Coulter et al., 2001; Glassman, 2005; Ha¨gg et al., 2001; Kang et al., 2000; Killian et al., 1999; Titier et al., 2005; Wetterling, 2001). Although treatment with antipsychotic drugs – particularly atypical antipsychotics – is an important therapeutic option, clinicians should carefully weigh up the benefits of drug treatments with the risks to the individual patient. "
ABSTRACT: Clinical characteristics and risk factors associated with sudden unexplained death (SUD) in the psychiatric population are unclear. Psychiatric in-patients (England, Wales) who met criteria for SUD were identified (1 March 1999-31 December 2005). Cases were matched with controls (in-patients alive on the day a SUD occurred). Data were collected via questionnaires. Some 283 cases of SUD were identified (41 annually), with a rate of 2.33/10,000 mental health admissions (in England). Electrocardiograms were not routine, cardiopulmonary resuscitation equipment was sometimes unavailable, attempts to resuscitate patients were carried out on one-half of all patients and post mortems/inquiries were not routine. Restraint and seclusion were uncommon. Risk factors included: benzodiazepines (odds ratio (OR): 1.83); ≥ 2 antipsychotics (OR: 2.35); promazine (OR: 4.02); diazepam (OR: 1.71); clozapine (OR: 2.10); cardiovascular disease (OR: 2.00); respiratory disease (OR: 1.98); diagnosis of dementia (OR: 2.08). Venlafaxine and a diagnosis of affective disorder were associated with reduced ORs (OR: 0.42; OR: 0.65). SUD is relatively rare, although it is more common in older patients and males. Prevention measures may include safer prescribing of antipsychotics and improved physical health care. The contribution of restraint or seclusion to SUD in individual cases is unclear. A uniform definition of SUD may help to identify contributing factors.Journal of Psychopharmacology 10/2010; 25(11):1533-42. DOI:10.1177/0269881110379288 · 3.59 Impact Factor