A New Classification Algorithm and a New Oversampling Method of Mapping Common Data Elements to the BRIDG Model

Lack of homogeneous definitions for imaging data and consensus on their relevance in the setting of subarachnoid hemorrhage and unruptured intracranial aneurysms lead to a difficulty of data pooling and lack of robust data. The aim of the National Institute of Health/National Institute of Neurological Disorders and Stroke, Unruptured Intracranial Aneurysm (UIA) and Subarachnoid Hemorrhage (SAH) Common Data Elements (CDE) Project was to standardize data elements to ultimately facilitate data pooling and establish a more robust data quality in future neurovascular research on UIA and SAH. For the subcommittee 'Radiological imaging of SAH,' international cerebrovascular specialists with imaging expertise in the setting of SAH were selected by the steering committee. CDEs were developed after reviewing the literature on neuroradiology and already existing CDEs for other neurological diseases. For prioritization, the CDEs were classified into 'Core,' 'Supplemental-Highly Recommended,' 'Supplemental' and 'Exploratory.' The subcommittee compiled 136 CDEs, 100 out of which were derived from previously established CDEs on ischemic stroke and 36 were newly created. The CDEs were assigned to four main categories (several CDEs were assigned to more than one category): 'Parenchymal imaging' with 42 CDEs, 'Angiography' with 49 CDEs, 'Perfusion imaging' with 20 CDEs, and 'Transcranial doppler' with 55 CDEs. The CDEs were classified into core, supplemental highly recommended, supplemental and exploratory elements. The core CDEs were imaging modality, imaging modality type, imaging modality vessel, angiography type, vessel angiography arterial anatomic site and imaging vessel angiography arterial result. The CDEs were established based on the current literature and consensus across cerebrovascular specialists. The use of these CDEs will facilitate standardization and aggregation of imaging data in the setting of SAH. However, the CDEs may require reevaluation and periodic adjustment based on current research and improved imaging quality and novel modalities.

The huge amounts of biomedical data are stored in various formats and accessed through numerous interfaces. It is a crucial task for Data integration and exchange in cancer research. Data elements play an important role in data integration. The NCI supports a broad initiative to standardize the common data elements (CDEs) used in cancer research data capture and reporting. The Taiwan Cancer Registry (TCR), established in 1979, is organized and funded by the Health Department of the central government. The TCR's primary goal is to survey the incidence of cancer in Taiwan. The aim of the Taiwan Cancer Common Data Element Project (TCCDEP) is to facilitate convergence towards a common metadata standard in Taiwan cancer registry data. The project is implemented using a set of open source software and tools developed by the NCI, such as the caCORE SDK and caGrid. The experience of building, learning and using the open toolkit, Cancer Data Standards Repository (caDSR), developed by the National Cancer Institute's Center for Bioinformatics, NCICB, in the USA is reported. The caDSR is a metadata repository including CDEs used by NCI-sponsored organizations. The object of this work is to develop a database of metadata for medical data elements, referred to as the TCCDEP and to establish a common classification of data elements used in cancer registry. In this manuscript, we will be developing the common data elements using vocabulary standards, ontology and semantic modeling methodology. The CDEs included demographic data, clinical history, pathology data, and clinical outcome data including treatment, recurrence and vital status. These CDEs will be further enhanced to data sets across the participating cancer institutes to facilitate and supplement translational research. The Taiwan Cancer Registry (TCR) model and standard will be use as the basis for an electronic data standard repository to metadata or data descriptors. The TCCDEP developed 40 data elements to annotate the cancer registry data collected. In this project, we describe the process required to develop the model, the caDSR CDEs, and the results of the modeling effort. We address difficulties we encountered and modifications for solution. The caBIG (TM) grid project, gird model of Taiwan Cancer Registry (girdTCR), using the caCORE tools to define data elements for cancer registry has been shown to caBIG(TM)UML model project. Currently, the Taiwan cancer registry CDEs are released and available in CDE browser for reusing. Furthermore, we will extend our CDEs to daily clinical practice and trials, along with how the methods were used to fully implemented in hospitals and cancer research centers in Taiwan.

BackgroundUniversities are increasingly implementing programs to effectively respond to and manage sport-related concussions (SRCs). One such effort is to develop common data elements (CDEs) and standardize data collection methods. The objectives of this study were to describe CDEs currently collected by Big Ten and Ivy League universities for SRC studies, and to compare the data collected with the core CDEs recommended by the National Institute of Neurological Disorders and Stroke (NINDS).MethodsWe conducted an anonymous cross-sectional online survey among medical staff at the 14 Big Ten and 8 Ivy League universities (one per university) between September and October 2015. The survey instrument, including 9 questions corresponding to the concussion data collected before, during, and after a concussion, was developed and pilot-tested before field use. We analyzed patterns of the concussion CDEs being collected, including when, what, and how the data were collected and stored, and compared them with the NINDS' recommended core CDEs.ResultsA total of 19 out of 22 universities were included, with 13 from Big Ten and 6 from Ivy-League universities. All 19 participating universities currently collected concussion data with athletes before, during, and after a concussion. Great similarities in data collection were observed at baseline and acutely post-concussion across participating universities. All 19 universities collected at least one of the ten recommended acute symptoms checklists, and 18 universities collected one of the four recommended core neuropsychological function cognitive measures. However, CDEs in the sub-acute and chronic timeframes were limited, with only 9 (47%) universities collecting post-concussion short to long term outcome data. While over 60% of universities collected and stored concussion data electronically, only 17% to 42% of data collected were readily available for research.ConclusionsSignificant inter-institutional similarities in acute concussion CDEs were found. Further efforts should focus on collecting sub-acute and chronic timeframe core CDEs and creating data access protocols to facilitate evidence-based concussion prevention and treatment for all collegiate athletes.

Extending the tissue microarray data exchange specification for inclusion of data analysis results

ObjectiveThe objective of this study is to develop an approach to evaluate the quality of terminological annotations on the value set (ie, enumerated value domain) components of the common data elements (CDEs) in the context of clinical research using both unified medical language system (UMLS) semantic types and groups.Materials and methodsThe CDEs of the National Cancer Institute (NCI) Cancer Data Standards Repository, the NCI Thesaurus (NCIt) concepts and the UMLS semantic network were integrated using a semantic web-based framework for a SPARQL-enabled evaluation. First, the set of CDE-permissible values with corresponding meanings in external controlled terminologies were isolated. The corresponding value meanings were then evaluated against their NCI- or UMLS-generated semantic network mapping to determine whether all of the meanings fell within the same semantic group.ResultsOf the enumerated CDEs in the Cancer Data Standards Repository, 3093 (26.2%) had elements drawn from more than one UMLS semantic group. A random sample (n=100) of this set of elements indicated that 17% of them were likely to have been misclassified.DiscussionThe use of existing semantic web tools can support a high-throughput mechanism for evaluating the quality of large CDE collections. This study demonstrates that the involvement of multiple semantic groups in an enumerated value domain of a CDE is an effective anchor to trigger an auditing point for quality evaluation activities.ConclusionThis approach produces a useful quality assurance mechanism for a clinical study CDE repository.

BackgroundWe describe development of the Early Childhood Caries (ECC) Basic Research Factors Questionnaire (BRFQ), a battery of measures assessing common potential predictors, mediators, and moderators of ECC. Individual-, family-, and community-level factors that are linked to oral health outcomes across at-risk populations are included. Developing standard measures of factors implicated in ECC has the potential to enhance our ability to understand mechanisms underlying successful prevention and to develop more effective interventions.MethodsThe Early Childhood Caries Collaborating Centers (EC4), funded by National Institute of Dental and Craniofacial Research, developed the BRFQ, which was used across four randomized trials to develop and test interventions for reducing ECC in at-risk populations. Forty-five investigators from across the centers and NIDCR were involved in the development process. Eight “measures working groups” identified relevant constructs and effective measurement approaches, which were then categorized as “essential” or “optional” common data elements (CDEs) for the EC4 projects.ResultsEssential CDEs include 88 items, with an additional 177 measures categorized as optional CDEs. Essential CDEs fell under the following domains: oral health knowledge, oral health behavior, utilization/insurance and cost, parent/caregiver dental self-efficacy, quality of life, caregiver and family characteristics, and child characteristics.ConclusionsThe BRFQ makes available a battery of measures that support efforts to understand population risk factors for ECC and to compare oral health outcomes across populations at risk. The BRFQ development process may be useful to other clinical research networks and consortia developing CDEs in other health research fields.Trial registrationAll the trial that used the BRFQ were registered at Clinicaltrial.gov NCT01116726, April 29, 2010; NCT01116739, May 3, 2010; NCT01129440, May 21, 2010; and NCT01205971, September 19, 2010.

Several years ago, the National Cancer Institute (NCI) embarked on an effort to standardize data collection methods across the cooperative trials groups it funds. These data collection methods included common toxicity criteria (CTC) and common data elements (CDEs). To date, CDEs have been developed for phase III therapeutic trials related to breast, prostate, lung, and colon cancer. Recently, the NCI funded the American College of Radiology Imaging Network (ACRIN, http://www.acrin.org), a cooperative group dedicated to imaging trials. Simultaneously, new imaging tests have shown promise in screening for lung cancer. Thus, several groups within the NCI became keenly interested in developing CDEs for imaging trials. What follows is a description of the development of CDEs for chest CT screening for lung cancer that was undertaken in the last half of 1999.

The aim of this study is to recommend a common data element (CDE) to measure supervisory effectiveness of staff working in LTC homes that can be used in international research. Supervisory effectiveness can serve as a CDE in an effort to establish an international, person-centered LTC research infrastructure in accordance with the aims of the WE-THRIVE group (Worldwide Elements to Harmonize Research in Long Term Care Living Environments). A literature review was completed and then a panel of experts independently reviewed and prioritized appropriateness of the measures with mindfulness of their potential applications to international LTC settings. The selection of a recommended CDE measure was guided by the WE-THRIVE group’s focus on capacity rather than deficits, the expected availability of internationally comparable data and the goal to provide a short, ecologically viable measurement, specifically for low- and middle-income countries. Two measures were considered as the CDE for supervisory effectiveness, Benjamin Rose Relationship Scale and the Supervisory Support Scale; however, given that the latter measure has been translated in Spanish and Chinese and has been tested with nursing assistants in both of these countries with good psychometric properties, our group recommends it as the CDE going forward.

Despite increased attention to traumatic brain injury (TBI), there remains no specific treatment and available interventions focus rather on the prevention of secondary injury. One of the reasons posited for the lack of a successful therapy is the amalgamation of various types of injuries under the same severity category in clinical trials. Informatics approaches have been suggested as a means to develop an improved classification system for TBI. As a result of federal interagency efforts, common data elements (CDEs) for TBI have now been developed. Further, the Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR) has been created and is now available for TBI researchers to both add and retrieve data. This chapter will discuss the goals, development, and evolution of the CDEs and FITBIR and discuss how these tools can be used to support TBI research. A specific exemplar using the CDEs and lessons learned from working with the CDEs and FITBIR are included to aid future researchers.

The National Institute of Health (NIH) launches the RADx Radical research collaboratives (RADx-rad) to advance new, non-traditional approaches for COVID-19 testing. RADx-rad projects are required to adopt common data elements (CDEs) to collect data to increase data interoperability. To overcome the challenges in finding appropriate CDEs for a wide range of study variables, we create a web application - IMI-CDE to ease the burden of mapping study variables to CDEs from researchers. IMI-CDE can automatically recommend CDE candidates for a study variable based on its name and description. Together with interactive mapping interfaces, IMI-CDE allows researchers to perform variable-CDE mapping with one mouse click. In addition, the IMI-CDE application supports users with multiple roles to work collaboratively on the mapping tasks. We have piloted the IMI-CDE with RADx-rad projects. 22 researchers from 8 different projects have started to use the IMI-CDE system for variable-CDE mappings. The beta-testing evaluators reported the system is intuitive, effective, and easy to use.

Electronic health records (EHRs) are essential in improving quality and enhancing efficiency of health-care delivery. By 2015, medical care receiving service reimbursement from US Centers for Medicare and Medicaid Services (CMS) must show 'meaningful use' of EHRs. Substance use disorders (SUD) are grossly under-detected and under-treated in current US medical care settings. Hence, an urgent need exists for improved identification of and clinical intervention for SUD in medical settings. The National Institute on Drug Abuse Clinical Trials Network (NIDA CTN) has leveraged its infrastructure and expertise and brought relevant stakeholders together to develop consensus on brief screening and initial assessment tools for SUD in general medical settings, with the objective of incorporation into US EHRs. Stakeholders were identified and queried for input and consensus on validated screening and assessment for SUD in general medical settings to develop common data elements to serve as shared resources for EHRs on screening, brief intervention and referral to treatment (SBIRT), with the intent of supporting interoperability and data exchange in a developing Nationwide Health Information Network. Through consensus of input from stakeholders, a validated screening and brief assessment instrument, supported by Clinical Decision Support tools, was chosen to be used at out-patient general medical settings. The creation and adoption of a core set of validated common data elements and the inclusion of such consensus-based data elements for general medical settings will enable the integration of SUD treatment within mainstream health care, and support the adoption and 'meaningful use' of the US Office of the National Coordinator for Health Information Technology (ONC)-certified EHRs, as well as CMS reimbursement.

The purpose of this article was to demonstrate the feasibility of using common data elements (CDEs) to search for information on the pediatric patient with multiple sclerosis (MS) and provide recommendations for future quality improvement and research in the use of CDEs for pediatric MS symptom management strategies Methods: The St. Louis Children's Hospital (SLCH), Washington University (WU) pediatrics data network was evaluated for use of CDEs identified from a database to identify variables in pediatric MS, including the key clinical features from the disease course of MS. The algorithms used were based on International Classification of Diseases, Ninth/Tenth Revision, codes and text keywords to identify pediatric patients with MS from a de-identified database. Data from a coordinating center of SLCH/WU pediatrics data network, which houses inpatient and outpatient records consisting of patients (N = 498 000), were identified, and detailed information regarding the clinical course of MS were located from the text of the medical records, including medications, presence of oligoclonal bands, year of diagnosis, and diagnosis code. There were 466 pediatric patients with MS, with a few also having the comorbid diagnosis of anxiety and depression. St. Louis Children's Hospital/WU pediatrics data network is one of the largest databases in the United States of detailed data, with the ability to query and validate clinical data for research on MS. Nurses and other healthcare professionals working with pediatric MS patients will benefit from having common disease identifiers for quality improvement, research, and practice. The increased knowledge of big data from SLCH/WU pediatrics data network has the potential to provide information for intervention and decision-making that can be personalized to the pediatric MS patient.

Clinical registries may facilitate research on myasthenia gravis (MG) in several ways: as a source of demographic, clinical, biological, and immunological data on large numbers of patients with this rare disease; as a source of referrals for clinical trials; and by allowing rapid identification of MG patients with specific features. Physician-derived registries have the added advantage of incorporating diagnostic and treatment data that may allow comparison of outcomes from different therapeutic approaches, which can be supplemented with patient self-reported data. We report the demographic analysis of MG patients in two large physician-derived registries, the Duke MG Patient Registry, at the Duke University Medical Center, and the INNCB MG Registry, at the Istituto Neurologico Carlo Besta, as a preliminary study to assess the consistency of the two data sets. These registries share a common structure, with an inner core of common data elements (CDE) that facilitate data analysis. The CDEs are concordant with the MG-specific CDEs developed under the National Institute of Neurological Disorders and Stroke Common Data Elements Project.

SummaryThe provided companion has been developed by the Behavioral Working Group of the Joint Translational Task Force of the International League Against Epilepsy (ILAE) and the American Epilepsy Society (AES) with the purpose of assisting the implementation of Preclinical Common Data Elements (CDE) for studying and for reporting neurobehavioral comorbidities in rodent models of epilepsy. Case Report Forms (CRFs) are provided, which should be completed on a per animal/per test basis, whereas the CDEs are a compiled list of the elements that should be reported. This companion is not designed as a list of recommendations, or guidelines for how the tests should be run—rather, it describes the different types of assessments, and highlights the importance of rigorous data collection and transparency in this regard. The tests are divided into 7 categories for examining behavioral dysfunction on the syndrome level: deficits in learning and memory; depression; anxiety; autism; attention deficit/hyperactivity disorder; psychosis; and aggression. Correspondence and integration of these categories into the National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) is introduced. Developmental aspects are addressed through the introduction of developmental milestones. Discussion includes complexities, limitations, and biases associated with neurobehavioral testing, especially when performed in animals with epilepsy, as well as the importance of rigorous data collection and of transparent reporting. This represents, to our knowledge, the first such resource dedicated to preclinical CDEs for behavioral testing of rodents.

Connecting currently existing, heterogeneous rare disease (RD) registries would greatly facilitate epidemiological and clinical research. To increase their interoperability, the European Union developed a set of Common Data Elements (CDEs) for RD registries. To implement the CDEs and the FAIR data principles in the Registry of Vascular Anomalies (VASCA). We created a semantic model for the CDE and transformed this into a Resource Description Framework (RDF) template. The electronic case report forms (eCRF) were mapped to the RDF template and published in a FAIR Data Point (FDP). The FAIR VASCA registry was successfully implemented using Castor EDC (Electronic Data Capture) software. FAIR technology allows researchers to query and combine data from different registries in real-time.