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Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients—phys...
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Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients—physical measurements, diagnoses, exposures, and markers of health behavior—that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.
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Summary: The insights that real-world data (RWD) can provide, beyond what can be learned within the traditional clinical trial setting, have gained enormous traction in recent years. RWD, which are increasingly available and acces...
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Summary: The insights that real-world data (RWD) can provide, beyond what can be learned within the traditional clinical trial setting, have gained enormous traction in recent years. RWD, which are increasingly available and accessible, can further our understanding of disease, disease progression, and safety and effectiveness of treatments with the speed and accuracy required by the health care environment and patients today. Over the decades since RWD were first recognized, innovation has evolved to take real-world research beyond finding ways to identify, store, and analyze large volumes of data. The research community has developed strong methods to address challenges of using RWD and as a result has increased the acceptance of RWD in research, practice, and policy. Historic concerns about RWD relate to data quality, privacy, and transparency; however, new tools, methods, and approaches mitigate these challenges and expand the utility of RWD to new applications. Specific guidelines for RWD use have been developed and published by numerous groups, including regulatory authorities. These and other efforts have shown that the more RWD are used and understood and the more the tools for handling it are refined, the more useful it will be.
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Background: Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF),...
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Background: Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF), acute coronary syndrome (ACS), and atrial fibrillation (AF). Methods: We conducted a systematic review of publications with RWD pertaining to HF, ACS, and AF (2010–2018), generating a list of unique data sources. Metadata were extracted based on the source type (e.g., electronic health records, genomics, and clinical data), study design, population size, clinical characteristics, follow-up duration, outcomes, and assessment of data availability for future studies and linkage. Results: Overall, 11,889 publications were retrieved for HF, 10,729 for ACS, and 6,262 for AF. From these, 322 (HF), 287 (ACS), and 220 (AF) data sources were selected for detailed review. The majority of data sources had near complete data on demographic variables (HF: 94%, ACS: 99%, and AF: 100%) and considerable data on comorbidities (HF: 77%, ACS: 93%, and AF: 97%). The least reported data categories were drug codes (HF, ACS, and AF: 10%) and caregiver involvement (HF: 6%, ACS: 1%, and AF: 1%). Only a minority of data sources provided information on access to data for other researchers (11%) or whether data could be linked to other data sources to maximize clinical impact (20%). The list and metadata for the RWD sources are publicly available at www.escardio.org/bigdata. Conclusions: This review has created a comprehensive resource of CV data sources, providing new avenues to improve future real-world research and to achieve better patient outcomes.
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Summary: The study methodology and regulatory frameworks for real-world data collection is quickly evolving, opening new avenues to use valid and robust real-world evidence (RWE) to support regulatory decision making. Although the...
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Summary: The study methodology and regulatory frameworks for real-world data collection is quickly evolving, opening new avenues to use valid and robust real-world evidence (RWE) to support regulatory decision making. Although the Food and Drug Administration has historically accepted specific applications of RWE to support regulatory determinations, significant progress has been made in recent years to examine conditions in which this information can be used to support specific types of premarket decisions. Of note, hybrid study designs that incorporate aspects of randomized clinical trials, including randomization and pragmatic outcomes, are expected to be a driving factor to accelerate the adoption of RWE in regulatory contexts. Generation of RWE to better understand effectiveness and safety of orthopaedic- and trauma-related devices requires careful planning, but it is achievable as demonstrated by the Bioventus Observational Non-Interventional EXOGEN Studies (BONES) clinical development program. This article examines the role of RWE in regulatory decision making and reviews key concepts in RWE study design methodology to facilitate creation of valid scientific evidence in support of marketing authorizations.
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Introduction: The evaluation of the post-marketing safety profile of drugs is a continuous monitoring process for approved and marketed medicines and it is crucial for detecting new adverse drug reactions. As such, real-world stud...
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Introduction: The evaluation of the post-marketing safety profile of drugs is a continuous monitoring process for approved and marketed medicines and it is crucial for detecting new adverse drug reactions. As such, real-world studies are essential to complement pre-marketing evidence with information concerning drug risk-benefit profile and use in wider patient populations and they have a great potential to support post-marketing drug safety evaluations. Areas covered: A detailed description of the main limitations of real-world data sources (i.e. claims databases, electronic healthcare records, drug/disease registers and spontaneous reporting system databases) and of the main methodological challenges of real-world studies in generating real-world evidence is provided. Expert opinion: Real-world evidence biases can be ascribed to both the methodological approach and the specific limitations of the different real-world data sources used to carry out the study. As such, it is crucial to characterize the quality of real-world data, by establishing guidelines and best practices for the assessment of data fitness for purpose. On the other hand, it is important that real-world studies are conducted using a rigorous methodology, aimed at minimizing the risk of bias.
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Real-world studies have become increasingly important in providing evidence of treatment effectiveness in clinical practice. While randomized clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of n...
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Real-world studies have become increasingly important in providing evidence of treatment effectiveness in clinical practice. While randomized clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of new therapeutic agents, necessarily strict inclusion and exclusion criteria mean that trial populations are often not representative of the patient populations encountered in clinical practice. Real-world studies may use information from electronic health and claims databases, which provide large datasets from diverse patient populations, and/or may be observational, collecting prospective or retrospective data over a long period of time. They can therefore provide information on the long-term safety, particularly pertaining to rare events, and effectiveness of drugs in large heterogeneous populations, as well as information on utilization patterns and health and economic outcomes. This review focuses on how evidence from real-world studies can be utilized to complement data from RCTs to gain a more complete picture of the advantages and disadvantages of medications as they are used in practice.Funding: Sanofi US, Inc.
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Summary: Real-world data (RWD) play an increasingly important role in orthopaedics as demonstrated by the rapidly growing number of publications using registry, administrative, and other databases. Each type of RWD source has its ...
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Summary: Real-world data (RWD) play an increasingly important role in orthopaedics as demonstrated by the rapidly growing number of publications using registry, administrative, and other databases. Each type of RWD source has its strengths and weaknesses, as does each specific database. Linkages between real-world data sets provide even greater utility and value for research than single data sources. The unique qualities of an RWD data source and all data linkages should be considered before use. Close attention to data quality and use of appropriate analysis methods can help alleviate concerns about validity of orthopaedic studies using RWD. This article describes the main types of RWD used in orthopaedics and provides brief descriptions and a sample listing of publications from selected, key data sources.
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Radically expanding use of real‐world data (RWD) and real‐world evidence (RWE) holds the potential to substantially impact drug development, pharmaceutical regulation, and payment within health care systems. Central to this is t...
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Radically expanding use of real‐world data (RWD) and real‐world evidence (RWE) holds the potential to substantially impact drug development, pharmaceutical regulation, and payment within health care systems. Central to this is the reconfiguration of data gathering and transformation of data to information, which can be used as evidence for decision making. We discuss applications of this paradigm in the light of recent developments in both the United States and Europe on RWD and RWE.
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Background: Despite increasing recognition of the value of real-world data (RWD), consensus on the definition of RWD is lacking. Objectives: To review definitions publicly available for RWD to shed light on similarities and differ...
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Background: Despite increasing recognition of the value of real-world data (RWD), consensus on the definition of RWD is lacking. Objectives: To review definitions publicly available for RWD to shed light on similarities and differences between them. Methods: A literature review and stakeholder interviews were used to compile data from eight groups of stakeholders. Data from documents and interviews were subjected to coding analysis. Definitions identified were classified into four categories: 1) data collected in a non-randomized controlled trial setting, 2) data collected in a non-interventional/non-controlled setting, 3) data collected in a non-experimental setting, and 4) others (i.e., data that do not fit into the other three categories). The frequency of definitions identified per category was recorded. Results: Fifty-three documents and 20 interviews were assessed. Thirty-eight definitions were identified: 20 out of 38 definitions (53%) were category 1 definitions, 9 (24%) were category 2 definitions, 5 (13%) were category 3 definitions, and 4 (11%) were category 4 definitions. Differences were identified between, and within, definition categories. For example, opinions differed on the aspects of intervention with which non-interventional/non-controlled settings should abide. No definitions were provided in two interviews or identified in 33 documents. Conclusions: Most of the definitions defined RWD as data collected in a non-randomized controlled trial setting. A considerable number of definitions, however, diverged from this concept. Moreover, a significant number of authors and stakeholders did not have an official, institutional definition for RWD. Persisting variability in stakeholder definitions of RWD may lead to disparities among different stakeholders when discussing RWD use in decision making.
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