Identification of Common Data Elements from Pivotal FDA Trials.
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ABSTRACT:
SUBMITTER: Mayer CS
PROVIDER: S-EPMC8075437 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceAlemtuzumab, a CD52-depleting monoclonal antibody, effectively inhibits relapsing multiple sclerosis (MS) but is associated with a high incidence of secondary B-cell autoimmunities that limit use. These effects may be avoided through control of B-cell hyperproliferation.ObjectiveTo investigate whether the data describing the effect of alemtuzumab on lymphocyte subsets collected during the phase 3 trial program reveal mechanisms explaining efficacy and the risk for secondary autoimmunity with treatment of MS.Design, setting, and participantsLymphocyte reconstitution data from regulatory submissions of the pivotal Comparison of Alemtuzumab and Rebif Efficacy in Multiple Sclerosis I and II (CARE-MS I and II) trials were obtained from the European Medicines Agency via Freedom of Information requests. Data used in this study were reported from June 22 to October 12, 2016.Main outcomes and measuresTabulated data from T- and B-lymphocyte subset analysis and antidrug antibody responses were extracted from the supplied documents.ResultsAlemtuzumab depleted CD4+ T cells by more than 95%, including regulatory cells (-80%) and CD8+ T cells (>80% depletion), which remained well below reference levels throughout the trials. However, although CD19+ B cells were initially also depleted (>85%), marked (180% increase) hyperrepopulation of immature B cells occurred with conversion to mature B cells over time. These lymphocyte kinetics were associated with rapid development of alemtuzumab-binding and -neutralizing antibodies and subsequent occurrence of secondary B-cell autoimmunity. Hyperrepopulation of B cells masked a marked, long-term depletion of CD19+ memory B cells that may underpin efficacy in MS.Conclusions and relevanceAlthough blockade of memory T and B cells may limit MS, rapid CD19+ B-cell subset repopulation in the absence of effective T-cell regulation has implications for the safety and efficacy of alemtuzumab. Controlling B-cell proliferation until T-cell regulation recovers may limit secondary autoimmunity, which does not occur with other B-cell-depleting agents.
| S-EPMC5710323 | biostudies-literature
Project description:OBJECTIVE:Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data. METHODS:Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems. RESULTS:A consensus statement is presented for epilepsy mHealth CDEs. SIGNIFICANCE:Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.
| S-EPMC5934312 | biostudies-literature
Project description:AimsThe Gulf War Illness programs (GWI) of the United States Department of Veteran Affairs and the Department of Defense Congressionally Directed Medical Research Program collaborated with experts to develop Common Data Elements (CDEs) to standardize and systematically collect, analyze, and share data across the (GWI) research community.Main methodsA collective working group of GWI advocates, Veterans, clinicians, and researchers convened to provide consensus on instruments, case report forms, and guidelines for GWI research. A similar initiative, supported by the National Institute of Neurologic Disorders and Stroke (NINDS) was completed for a comparative illness, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and provided the foundation for this undertaking. The GWI working group divided into two sub-groups (symptoms and systems assessment). Both groups reviewed the applicability of instruments and forms recommended by the NINDS ME/CFS CDE to GWI research within specific domains and selected assessments of deployment exposures. The GWI CDE recommendations were finalized in March 2018 after soliciting public comments.Key findingsGWI CDE recommendations are organized in 12 domains that include instruments, case report forms, and guidelines. Recommendations were categorized as core (essential), supplemental-highly recommended (essential for specified conditions, study types, or designs), supplemental (commonly collected, but not required), and exploratory (reasonable to use, but require further validation). Recommendations will continually be updated as GWI research progresses.SignificanceThe GWI CDEs reflect the consensus recommendations of GWI research community stakeholders and will allow studies to standardize data collection, enhance data quality, and facilitate data sharing.
| S-EPMC9267452 | biostudies-literature
Project description:BackgroundSemantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents.MethodsWe addressed these limitations by defining three new types of semantic relationship (dependency, composite, and variable) in our previous studies. The present study created new and further extended existing semantic types (hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered, operated, required, and dependent. For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea, 14 Fast Healthcare Interoperability Resources (FHIR) resources from FHIR bulk sample data, and MIMIC-III (Medical Information Mart for Intensive Care) demo dataset. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints.ResultsAll of the CDEs (n?=?1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 586 CDEs (259 atomic and 20 unique composite CDEs), and all of CDEs (n?=?238) extracted from the 14 FHIR resources of FHIR bulk sample data were successfully integrated with high CDE reuse ration (59.7%) into 96 CDEs (21 atomic and 28 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity.ConclusionMDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics, and improved semantic interoperability without semantic loss.
| S-EPMC7333279 | biostudies-literature
Project description:BackgroundStandardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings.MethodsA preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings.ResultsWe generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information.ConclusionRoutine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.
| S-EPMC8192005 | biostudies-literature
Project description:Developing promising treatments in biomedicine often requires aggregation and analysis of data from disparate sources across the healthcare and research spectrum. To facilitate these approaches, there is a growing focus on supporting interoperation of datasets by standardizing data-capture and reporting requirements. Common Data Elements (CDEs)-precise specifications of questions and the set of allowable answers to each question-are increasingly being adopted to help meet these standardization goals. While CDEs can provide a strong conceptual foundation for interoperation, there are no widely recognized serialization or interchange formats to describe and exchange their definitions. As a result, CDEs defined in one system cannot be easily be reused by other systems. An additional problem is that current CDE-based systems tend to be rather heavyweight and cannot be easily adopted and used by third-parties. To address these problems, we developed extensions to a metadata management system called the CEDAR Workbench to provide a platform to simplify the creation, exchange, and use of CDEs. We show how the resulting system allows users to quickly define and share CDEs and to immediately use these CDEs to build and deploy Web-based forms to acquire conforming metadata. We also show how we incorporated a large CDE library from the National Cancer Institute's caDSR system and made these CDEs publicly available for general use.
| S-EPMC7153094 | biostudies-literature
Project description:PURPOSE:Biomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a "minimum set" of biomarkers for consideration as CDEs in symptom and self-management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance. DESIGN AND METHODS:From May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and self-management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and self-management science are proposed along with implications for future research and use of CDEs in these areas. FINDINGS:The recommended minimum set of biomarker CDEs include pro- and anti-inflammatory cytokines, a hypothalamic-pituitary-adrenal axis marker, cortisol, the neuropeptide brain-derived neurotrophic factor, and DNA polymorphisms. CONCLUSIONS:It is anticipated that this minimum set of biomarker CDEs will be refined as knowledge regarding biologic mechanisms underlying symptom and self-management science further develop. The incorporation of biological CDEs may provide insights into mechanisms of symptoms, effectiveness of proposed interventions, and applicability of chosen theoretical frameworks. Similarly, as for the previously suggested NINR CDEs for behavioral symptoms and self-management of chronic conditions, biological CDEs offer the potential for collaborative efforts that will strengthen symptom and self-management science. CLINICAL RELEVANCE:The use of biomarker CDEs in biobehavioral symptoms research will facilitate the reproducibility and generalizability of research findings and benefit symptom and self-management science.
| S-EPMC5945303 | biostudies-literature
Project description:BackgroundStroke registries contribute to the conduct of clinical research and to the assessment of health care quality control. Efforts to compare clinical outcomes and quality indicators between centers are dependent on standardized data elements, but it is unknown how stroke physicians define common data elements, such as hypertension or diabetes, when collecting data for registries at their centers.MethodsWe conducted an internet-based survey of 91 centers affiliated with a university to assess their definitions of common data elements (CDEs) and compared their responses with standardized definitions, including those from the American College of Cardiology (ACC).ResultsMore than half (52%) of centers completed the survey. There was only modest agreement among respondents regarding definitions of CDEs in the survey and even less agreement on how the respondents' definitions compared to ACC standards.ConclusionsSurveyed respondents do not agree on the definitions of CDEs, making comparisons between centers problematic. Standardized definitions of CDEs are needed to improve data collection for patient care and clinical research.
| S-EPMC3343760 | biostudies-literature
Project description:Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.
| S-EPMC8247461 | biostudies-literature
Project description:The U.S. Food and Drug Administration (FDA) often approves new drugs based on trials that use surrogate markers for endpoints, which involve certain trade-offs and may risk making erroneous inferences about the medical product's actual clinical effect. This study aims to compare the treatment effects among pivotal trials supporting FDA approval of novel therapeutics based on surrogate markers of disease with those observed among postapproval trials for the same indication.We searched Drugs@FDA and PubMed to identify published randomized superiority design pivotal trials for all novel drugs initially approved by the FDA between 2005 and 2012 based on surrogate markers as primary endpoints and published postapproval trials using the same surrogate markers or patient-relevant outcomes as endpoints. Summary ratio of odds ratios (RORs) and difference between standardized mean differences (dSMDs) were used to quantify the average difference in treatment effects between pivotal and matched postapproval trials.Between 2005 and 2012, the FDA approved 88 novel drugs for 90 indications based on one or multiple pivotal trials using surrogate markers of disease. Of these, 27 novel drugs for 27 indications were approved based on pivotal trials using surrogate markers as primary endpoints that could be matched to at least one postapproval trial, for a total of 43 matches. For nine (75.0%) of the 12 matches using the same non-continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than postapproval trials. On average, treatment effects were 50% higher (more beneficial) in the pivotal than the postapproval trials (ROR 1.5; 95% confidence interval CI 1.01-2.23). For 17 (54.8%) of the 31 matches using the same continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than the postapproval trials. On average, there was no difference in treatment effects between pivotal and postapproval trials (dSMDs 0.01; 95% CI -0.15-0.16).Many postapproval drug trials are not directly comparable to previously published pivotal trials, particularly with respect to endpoint selection. Although treatment effects from pivotal trials supporting FDA approval of novel therapeutics based on non-continuous surrogate markers of disease are often larger than those observed among postapproval trials using surrogate markers as trial endpoints, there is no evidence of difference between pivotal and postapproval trials using continuous surrogate markers.
| S-EPMC5863466 | biostudies-other