Drug label changes involve contributions from multiple evidence sources. The findings from this comprehensive review are consistent with previous findings and demonstrate (i) the continued importance of the spontaneous reporting system and complementary evidence sources and (ii) safety-related label changes take place years after postmarket approval, emphasizing the importance of continued drug safety surveillance throughout a product's lifecycle.
Although some of the limitations described are inherent in RCTs, some of the sources of bias highlighted in this article could be minimized by careful RCT design, planned follow-up, and improved collection of information on adverse events. As future research sheds more light on pertinent knowledge gaps and issues, the ability to maximize the use of RCTs and meta-analyses of RCTs to address drug safety questions of interest will be greatly enhanced.
ObjectivesPharmacoepidemiological studies are an important hypothesis-testing tool in the evaluation of postmarketing drug safety. Despite the potential to produce robust value-added data, interpretation of findings can be hindered due to well-recognised methodological limitations of these studies. Therefore, assessment of their quality is essential to evaluating their credibility. The objective of this review was to evaluate the suitability and relevance of available tools for the assessment of pharmacoepidemiological safety studies.DesignWe created an a priori assessment framework consisting of reporting elements (REs) and quality assessment attributes (QAAs). A comprehensive literature search identified distinct assessment tools and the prespecified elements and attributes were evaluated.Primary and secondary outcome measuresThe primary outcome measure was the percentage representation of each domain, RE and QAA for the quality assessment tools.ResultsA total of 61 tools were reviewed. Most tools were not designed to evaluate pharmacoepidemiological safety studies. More than 50% of the reviewed tools considered REs under the research aims, analytical approach, outcome definition and ascertainment, study population and exposure definition and ascertainment domains. REs under the discussion and interpretation, results and study team domains were considered in less than 40% of the tools. Except for the data source domain, quality attributes were considered in less than 50% of the tools.ConclusionsMany tools failed to include critical assessment elements relevant to observational pharmacoepidemiological safety studies and did not distinguish between REs and QAAs. Further, there is a lack of considerations on the relative weights of different domains and elements. The development of a quality assessment tool would facilitate consistent, objective and evidence-based assessments of pharmacoepidemiological safety studies.
March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies.
For this simulation, we generated 124,150 synthetic patients, with 88,166 infections and 18,177 hospitalized patients. Patient symptoms, disease severity, and morbidity outcomes were calibrated using clinical data from the peer-reviewed publications. 4.1% of all simulated infected patients died and 20.6% were hospitalized. At peak observation, 548 dialysis machines and 209 mechanical ventilators were needed. This simulation and the resulting data have been used for the development of algorithms and prototypes designed to address the current or future pandemics, and the model can continue to be refined to incorporate emerging COVID-19 knowledge, variations in patterns of care, and improvement in clinical outcomes. The resulting model, data, and analysis are available as open-source code on GitHub and an open-access data set is available for download.
The US Food and Drug Administration emphasizes the role of regulatory science in the fulfillment of its mission to promote and protect public health and foster innovation. With respect to the evaluation of drug effects in the real world, regulatory science plays an important role in drug risk assessment and management. This article discusses opportunities and challenges with population-based drug risk assessment as well as related regulatory science knowledge gaps in the following areas: (i) population-based data sources and methods to evaluate drug safety issues; (ii) evidence-based thresholds to account for uncertainty in postmarket data; (iii) approaches to optimize the integration and interpretation of evidence from different sources; and (iv) approaches to evaluate the real-world impact of regulatory decisions. Regulators should continue the ongoing dialogue with multiple stakeholders to strengthen regulatory safety science and address these and other critical knowledge gaps.
While the majority of PRISMA elements were addressed by most studies reviewed, the majority of studies did not address most of the additional safety-related elements. These findings highlight the need for the development and validation of a drug safety reporting framework and the importance of the current initiative by the Council for International Organizations of Medical Sciences (CIOMS) to create a guidance document for drug safety information synthesis/meta-analysis, which may improve reporting, conduct, and evaluation of meta-analyses of drug safety and inform clinical and regulatory decision making.
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