Dataset Information

Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study.

ABSTRACT: OBJECTIVES:To examine the relation between antipsychotic drugs and myocarditis and cardiomyopathy. DESIGN:Data mining using bayesian statistics implemented in a neural network architecture. SETTING: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. RESULTS: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. CONCLUSIONS: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.

SUBMITTER: Coulter DM

PROVIDER: S-EPMC31617 | biostudies-literature | 2001 May

REPOSITORIES: biostudies-literature

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Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study.

Coulter D M DM Bate A A Meyboom R H RH Lindquist M M Edwards I R IR

BMJ (Clinical research ed.) 20010501 7296

<h4>Objectives</h4>To examine the relation between antipsychotic drugs and myocarditis and cardiomyopathy.<h4>Design</h4>Data mining using bayesian statistics implemented in a neural network architecture.<h4>Setting</h4>International database on adverse drug reactions run by the World Health Organization programme for international drug monitoring.<h4>Main outcome measures</h4>Reports mentioning antipsychotic drugs, cardiomyopathy, or myocarditis.<h4>Results</h4>A strong signal existed for an as ...[more]

PMID: 11358771

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Project description:BackgroundPharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children.MethodsWe used paediatric safety data from a multi-site, multi-country clinical study conducted in seven African countries (Burkina Faso, Gabon, Nigeria, Rwanda, Uganda, Zambia, and Mozambique). Each site compared three out of four ACTs, namely amodiaquine-artesunate (ASAQ), dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine (AL) or chlorproguanil/dapsone and artesunate (CD+A). We examine two pharmacovigilance signal detection methods, namely proportional reporting ratio and Bayesian Confidence Propagation Neural Network on the clinical safety dataset.ResultsAmong the 4,116 children (6-59 months old) enrolled and followed up for 28 days post treatment, a total of 6,238 adverse events were reported resulting into 346 drug-event combinations. Nine signals were generated both by proportional reporting ratio and Bayesian Confidence Propagation Neural Network. A review of the manufacturer package leaflets, an online Multi-Drug Symptom/Interaction Checker (DoubleCheckMD) and further by therapeutic area experts reduced the number of signals to five. The ranking of some drug-adverse reaction pairs on the basis of their signal index differed between the two methods.ConclusionsOur two data mining methods were equally able to generate suspected signals using the pooled safety data from a phase IIIb/IV clinical trial. This analysis demonstrated the possibility of utilising clinical studies safety data for key pharmacovigilance activities like signal detection and evaluation. This approach can be applied to complement the spontaneous reporting systems which are limited by under reporting.

Project description:Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness. GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.

Project description:Objective: The introduction of Bruton's tyrosine kinase (BTK) inhibitors was a milestone in the treatment of B-cell malignancies in recent years owing to its desired efficacy against chronic lymphocytic leukaemia and small cell lymphocytic lymphoma. However, safety issues have hindered its application in clinical practice. The current study aimed to explore the safety warning signals of BTK inhibitors in a real-world setting using the FDA Adverse Event Reporting System (FAERS) to provide reference for clinical rational drug use. Methods: Owing to the short marketing time of other drugs (zanbrutinib and orelabrutinib), we only analysed ibrutinib and acalabrutinib in this study. All data were obtained from the FAERS database from January 2004 to December 2021. Disproportionality analysis and Bayesian analysis were utilised to detect and assess the adverse event (AE) signals of BTK inhibitors. Results: In total, 43,429 reports of ibrutinib were extracted and 1527 AEs were identified, whereas 1742 reports of acalabrutinib were extracted and 220 AEs were identified by disproportionality analysis and Bayesian analysis. Among reports, males were more prone to develop AEs (58.2% for males vs. 35.6% for females treated with ibrutinib, and 55.9% vs. 31.9%, respectively, for acalabrutinib), and more than 30% of patients that suffered from AEs were over 65 years of age. Subsequently, we investigated the top 20 preferred terms (PTs) associated with the signal strength of ibrutinib and acalabrutinib, and our results identified 25 (13 vs. 12, respectively) novel risk signals. Among the top 20 PTs related to death reports, the terms infectious, pneumonia, pleural effusion, fall, asthenia, diarrhoea, and fatigue were all ranked high for these two BTK inhibitors. Further, cardiac disorders were also an important cause of death with ibrutinib. Conclusion: Patients treated with ibrutinib were more prone to develop AEs than those treated with acalabrutinib. Importantly, infection-related adverse reactions, such as pneumonia and pleural effusion, were the most common risk signals related to high mortality associated with both BTK inhibitors, especially in elderly patients. Moreover, cardiovascular-related adverse reactions, such as atrial fibrillation and cardiac failure, were fatal AEs associated with ibrutinib. Our results provide a rationale for physicians to choose suitable BTK inhibitors for different patients and provide appropriate monitoring to achieve safer therapy and longer survival.

Dataset Information

Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study.

Publications

Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study.

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