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dc.contributor.authorKibathi, Louis W
dc.date.accessioned2020-10-28T10:51:21Z
dc.date.available2020-10-28T10:51:21Z
dc.date.issued2020
dc.identifier.urihttp://erepository.uonbi.ac.ke/handle/11295/153131
dc.description.abstractBackground The body responsible for monitoring drug safety in Kenya is the Pharmacy and Poisons Board (PPB). One of the methods for tracking the safety of medicines is through information submitted in spontaneous adverse event reports. These reports are technically known as individual case safety reports (ICSRs) and are submitted by health care workers, drug consumers and other interested parties such as lawyers and lobby groups. In order for potential signals of new adverse reactions to be effectively detected, these reports must contain all the relevant patient and drug information as well as a description of the adverse event. One of the potential areas for signal detection is the relationship between antiretroviral therapy and cardiovascular diseases (CVDs). Cardiovascular diseases are some of the major causes of death and morbidity among Human Immunodeficiency Virus (HIV) patients. While various studies have been done to assess the relationship between antiretroviral therapy (ART) and cardiovascular diseases, few have utilized pharmacovigilance databases to evaluate this link. Disproportionality analysis is one of the methods for detecting potential new signals using spontaneous reports databases. Objective: The objective of the study was to identify the trend of quality of Kenyan individual case safety reports (ICSRs) submitted to the World Health Organizations’s (WHO) global spontaneous adverse events database, VigiBase. The study also sought to identify safety signals for cardiac arrhythmias associated with the use of antiretroviral drugs in HIV patients. Moreover, the study aimed to evaluate for potential drug-drug interactions between antiretroviral (ARV) drugs and some selected cardiovascular (CVS) agents. Methods: The study was divided into two parts. The first part encompassed a time series analysis of the completeness score of Kenyan adverse events reports on VigiBase. The average quarterly scores were subjected to interrupted generalized linear regression analysis using R programming software. The second part involved an exploratory analysis of cardiac arrhythmias associated with the use of antiretroviral drugs as well as an evaluation of possible drug interactions between ARVs and selected cardiovascular agents. Disproportionality analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) database was used to identify signals between antiretroviral drugs and arrhythmia. Data for disproportionality analysis was retrieved through customized queries using the AERSMine platform. Assessment of potential drug-drug interactions was done by customizing the search terms to include the Boolean operator “AND”. Addition of this operator enabled the data xix mining tool to capture only the adverse events reported with the concomitant use of the drugs under evaluation. Results: A total of 11,270 Kenyan individual case safety reports (ICSRs) were retrieved from VigiBase. Reports from Nairobi (15.4%), Uasin Gishu (11%), Migori (10.5%), Kisumu (7.4%) and Kiambu (6.2%) constituted the highest proportion of the database. Most of the reports involved antiretroviral drugs (79%), anti-tuberculosis medications (6.6%), antibiotics (5.5%) and anticancer medications (2.2%). There was an initial drop in the quality of reports during early reporting period followed by a big improvement with the completeness score peaking at around 0.66 by the end of 2014. This was followed by a period of declining quality which later took an upward trajectory. The highest score recorded was 0.74 which was achieved in the last quarter of 2017. Interrupted time series analysis of the quality revealed a major change point in the fourth quarter of 2012. In the period following this event, the average quarterly completeness scores increased by 0.055 ± 0.017 (p = 0.003). A total of 11, 919, 342 reports in the FAERS database were evaluated. A strong association was found between some ARV drugs and cardiac arrhythmia. The strongest signals identified were for foetal and neonatal arrhythmias, tachyarrhythmia and ventricular arrhythmias. With regard to bradycardia and bradyarrhythmia in HIV patients on ARVs, the signals were only significant for zalcitabine, lopinavir/ritonavir and nelfinavir. The strongest signal for tachyarrhythmias was recorded in delavirdine (>4). Protease inhibitors produced relatively strong signals for torsade de pointes with indinavir, saquinavir, nelfinavir and fosemprenavir all having signals of more than two. Efavirenz, nelfinavir and raltegravir also exhibited a strong signal for ventricular arrhythmia. For all the ARVs in which sinus node dysfunction was reported, the signal was more than 2. Potentially toxic drug-drug interactions between protease inhibitors, non-nucleoside reverse transcriptase inhibitors (NNRTIs) and selected cardiovascular agents were also observed. The adverse events that exhibited the strongest signals with regard to these suspected interactions include sinus tachycardia and QT interval prolongation. Conclusion and recommendations: The quality of Kenyan adverse event reports has stagnated at a score of about 70%. Continued pharmacovigilance training of health care workers and consumers is therefore required to achieve further improvement in the quality of reporting. The study also established that some antiretroviral drugs may have arrhythmic adverse events. These potential signals require further investigation using more rigorous research methods such as cohort event monitoring.en_US
dc.language.isoenen_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectAntiretroviral Therapy Related Arrhythmiasen_US
dc.titleQuality Of Adverse Event Reports In Kenya And Safety Signals For Antiretroviral Therapy Related Arrhythmiasen_US
dc.typeThesisen_US
dc.description.departmenta Department of Psychiatry, University of Nairobi, ; bDepartment of Mental Health, School of Medicine, Moi University, Eldoret, Kenya


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