Dataset Information

BRIDG: a domain information model for translational and clinical protocol-driven research.

ABSTRACT: Background:It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. Objective:To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. Methods:BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. Results:With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. Interpretation:The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

SUBMITTER: Becnel LB

PROVIDER: S-EPMC6259662 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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BRIDG: a domain information model for translational and clinical protocol-driven research.

Becnel Lauren B LB Hastak Smita S Ver Hoef Wendy W Milius Robert P RP Slack MaryAnn M Wold Diane D Glickman Michael L ML Brodsky Boris B Jaffe Charles C Kush Rebecca R Helton Edward E

Journal of the American Medical Informatics Association : JAMIA 20170901 5

<h4>Background</h4>It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data.<h4>Objective</h4>To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integra ...[more]

PMID: 28339791

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Project description:BackgroundGlomerulonephritis (GN) is a leading cause of kidney failure and accounts for 20% of incident cases of end-stage kidney disease (ESKD) in Canada annually. Reversal of kidney injury and prevention of progression to kidney failure is possible; however, limited knowledge of underlying disease mechanisms and lack of noninvasive biomarkers and therapeutic targets are major barriers to successful therapeutic intervention. Multicenter approaches that link longitudinal clinical and outcomes data with serial biologic specimen collection would help bridge this gap.ObjectiveTo establish a national, patient-centered, multidimensional web-based clinical database and federated virtual biobank to conduct human-based molecular and clinical research in GN in Canada.DesignMulticenter, prospective observational registry, starting in 2019.SettingNine participating Canadian tertiary care centers.PatientsAdult patients with a histopathologic pattern of injury consistent with IgA nephropathy, focal and segmental glomerulosclerosis, minimal change disease, membranous nephropathy, C3 glomerulopathy, and membranoproliferative GN recruited within 24 months of biopsy.MeasurementsInitial visits include detailed clinical, histopathological, and laboratory data collection, blood, urine, and tonsil swab biospecimen collection, and a self-administered quality of life questionnaire. Follow-up clinical and laboratory data collection, biospecimen collection, and questionnaires are obtained every 6 months thereafter.MethodsPatients receive care as defined by their physician, with study visits scheduled every 6 months. Patients are followed until death, dialysis, transplantation, or withdrawal from the study. Key outcomes include a composite of ESKD or a 40% decline in estimated glomerular filtration rate (eGFR) at 2 years, rate of kidney function decline, and remission of proteinuria. Clinical and molecular phenotypical data will be analyzed by GN subtype to identify disease predictors and discover therapeutic targets.LimitationsGiven the relative rarity of individual glomerular diseases, one of the major challenges is patient recruitment. Initial registry studies may be underpowered to detect small differences in clinically meaningful outcomes such as ESKD or death due to small sample sizes and short duration of follow-up in the initial 2-year phase of the study.ConclusionsThe Canadian Glomerulonephritis Registry (CGNR) supports national collaborative efforts to study glomerular disease patients and their outcomes.Trial registrationNCT03460054.

Project description:ObjectiveClinicians often have difficulty translating information needs into effective search strategies to find appropriate answers. Information retrieval systems employing an intelligent search agent that generates adaptive search strategies based on human search expertise could be helpful in meeting clinician information needs. A prerequisite for creating such systems is an information seeking model that facilitates the representation of human search expertise. The purpose of developing such a model is to provide guidance to information seeking system development and to shape an empirical research program.DesignThe information seeking process was modeled as a complex problem-solving activity. After considering how similarly complex activities had been modeled in other domains, we determined that modeling context-initiated information seeking across multiple problem spaces allows the abstraction of search knowledge into functionally consistent layers. The knowledge layers were identified in the information science literature and validated through our observations of searches performed by health science librarians.ResultsA hierarchical multi-level model of context-initiated information seeking is proposed. Each level represents (1) a problem space that is traversed during the online search process, and (2) a distinct layer of knowledge that is required to execute a successful search. Grand strategy determines what information resources will be searched, for what purpose, and in what order. The strategy level represents an overall approach for searching a single resource. Tactics are individual moves made to further a strategy. Operations are mappings of abstract intentions to information resource-specific concrete input. Assessment is the basis of interaction within the strategic hierarchy, influencing the direction of the search.ConclusionThe described multi-level model provides a framework for future research and the foundation for development of an automated information retrieval system that uses an intelligent search agent to bridge clinician information needs and human search expertise.

Project description:BACKGROUND:Understanding how brain circuit dysfunctions relate to specific symptoms offers promise for developing a brain-based taxonomy for classifying psychopathology, identifying targets for mechanistic studies and ultimately for guiding treatment choice. The goal of the Research Domain Criteria (RDoC) initiative of the National Institute of Mental Health is to accelerate the development of such neurobiological models of mental disorder independent of traditional diagnostic criteria. In our RDoC Anxiety and Depression ("RAD") project we focus trans-diagnostically on the spectrum of depression and anxiety psychopathology. Our aims are a) to use brain imaging to define cohesive dimensions defined by dysfunction of circuits involved in reactivity to and regulation of negatively valenced emotional stimulation and in cognitive control, b) to assess the relationships between these dimension and specific symptoms, behavioral performance and the real world capacity to function socially and at work and c) to assess the stability of brain-symptom-behavior-function relationships over time. METHODS AND DESIGN:Here we present the protocol for the "RAD" project, one of the first RDoC studies to use brain circuit functioning to define new dimensions of psychopathology. The RAD project follows baseline-follow up design. In line with RDoC principles we use a strategy for recruiting all clients who "walk through the door" of a large community mental health clinic as well as the surrounding community. The clinic attends to a broad spectrum of anxiety and mood-related symptoms. Participants are unmedicated and studied at baseline using a standardized battery of functional brain imaging, structural brain imaging and behavioral probes that assay constructs of threat reactivity, threat regulation and cognitive control. The battery also includes self-report measures of anxiety and mood symptoms, and social and occupational functioning. After baseline assessments, therapists in the clinic apply treatment planning as usual. Follow-up assessments are undertaken at 3 months, to establish the reliability of brain-based subgroups over time and to assess whether these subgroups predict real-world functional capacity over time. First enrollment was August 2013, and is ongoing. DISCUSSION:This project is designed to advance knowledge toward a neural circuit taxonomy for mental disorder. Data will be shared via the RDoC database for dissemination to the scientific community. The clinical translational neuroscience goals of the project are to develop brain-behavior profile reports for each individual participant and to refine these reports with therapist feedback. Reporting of results is expected from December 2016 onward. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02220309 . Registered: August 13, 2014.

Dataset Information

BRIDG: a domain information model for translational and clinical protocol-driven research.

Publications

BRIDG: a domain information model for translational and clinical protocol-driven research.

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