Drug safety
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Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. ⋯ Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.
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The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from individual case safety reports (ICSRs). To cope with this increase, more healthcare and qualified professionals are required to capture and evaluate the data. To address the evolving landscape, it will be necessary to embrace assistive technologies such as artificial intelligence (AI) at scale. ⋯ Interviewees suggested that AI would allow for pharmacovigilance resources, time, and skills to shift the work from a volume-based to a value-based focus. The results suggest that pharmacovigilance professionals wish to use their qualifications, skillsets and experience in work that provides more value for their efforts. Machine learning algorithms have the potential to enhance DS professionals' decision-making processes and support more efficient and accurate case processing.
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Acute liver injury (ALI) is a major reason for stopping drug development or removing drugs from the market. Hospitalisation for ALI is relatively rare for marketed drugs, justifying studies in large-scale databases such as the nationwide Système National des Données de Santé (SNDS), which covers 99% of the French population. ⋯ This nationwide study describes drugs associated with ALI, according to absolute population burden and per-patient and per-tablet risk. Some of these associations may be spurious, others causal, and others yet were unexpected. Systematic analysis of drug classes will look for outliers within each class that could raise signals of unexpected hepatic toxicity.
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Meta Analysis Comparative Study
The Prevalence of Dose Errors Among Paediatric Patients in Hospital Wards with and without Health Information Technology: A Systematic Review and Meta-Analysis.
The risk of dose errors is high in paediatric inpatient settings. Computerized provider order entry (CPOE) systems with clinical decision support (CDS) may assist in reducing the risk of dosing errors. Although a frequent type of medication error, the prevalence of dose errors is not well described. Dosing error rates in hospitals with or without CPOE have not been compared. ⋯ Dose errors occur in approximately 1 in 20 medication orders. Hospitals using CPOE with or without CDS had a lower rate of dose errors compared with those using paper charts. However, few pre/post studies have been conducted and none reported a significant reduction in dose error rates associated with the introduction of CPOE. Future research employing controlled designs is needed to determine the true impact of CPOE on dosing errors among children, and any associated patient harm.
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Stevens-Johnson syndrome and toxic epidermal necrolysis have been associated with the use of various drugs, but evidence is scarce. We studied the association between new use of outpatient drugs other than anti-epileptic drugs and antibiotics and Stevens-Johnson syndrome and toxic epidermal necrolysis. ⋯ In this observational study, we observed likely causal associations between Stevens-Johnson syndrome/toxic epidermal necrolysis and use of allopurinol, cyclooxygenase-2 inhibitors, and 5-aminosalicylates, and potential associations for proton pump inhibitors, fluoxetine, and mirtazapine.