Promoting Data Harmonization of COVID-19 Research in Pregnant and Pediatric Populations.

This Viewpoint investigates the use of common data elements to promote data harmonization in COVID-19­related studies of pediatric and pregnant populations.

A common language for Gulf War Illness (GWI) research studies: GWI common data elements.

AIMS: The 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 METHODS: A 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 FINDINGS: GWI 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. SIGNIFICANCE: The 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.

Common data elements for predictors of pediatric sepsis: A framework to standardize data collection.

BACKGROUND: Standardized 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. METHODS: A 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. RESULTS: We 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. CONCLUSION: Routine 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.

Pre-Clinical Common Data Elements for Traumatic Brain Injury Research: Progress and Use Cases.

Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.

tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports.

BACKGROUND: The manual extraction of valuable data from electronic medical records is cumbersome, error-prone, and inconsistent. By automating extraction in conjunction with standardized terminology, the quality and consistency of data utilized for research and clinical purposes would be substantially improved. Here, we set out to develop and validate a framework to extract pertinent clinical conditions for traumatic brain injury (TBI) from computed tomography (CT) reports. METHODS: We developed tbiExtractor, which extends pyConTextNLP, a regular expression algorithm using negation detection and contextual features, to create a framework for extracting TBI common data elements from radiology reports. The algorithm inputs radiology reports and outputs a structured summary containing 27 clinical findings with their respective annotations. Development and validation of the algorithm was completed using two physician annotators as the gold standard. RESULTS: tbiExtractor displayed high sensitivity (0.92-0.94) and specificity (0.99) when compared to the gold standard. The algorithm also demonstrated a high equivalence (94.6%) with the annotators. A majority of clinical findings (85%) had minimal errors (F1 Score ≥ 0.80). When compared to annotators, tbiExtractor extracted information in significantly less time (0.3 sec vs 1.7 min per report). CONCLUSION: tbiExtractor is a validated algorithm for extraction of TBI common data elements from radiology reports. This automation reduces the time spent to extract structured data and improves the consistency of data extracted. Lastly, tbiExtractor can be used to stratify subjects into groups based on visible damage by partitioning the annotations of the pertinent clinical conditions on a radiology report.

Common Data Elements: Critical Assessment of Harmonization between Current Multi-Center Traumatic Brain Injury Studies.

Standardization and harmonization of data collection in studies on traumatic brain injury (TBI) is of paramount importance for meta-analyses across studies. Nearly 10 years ago, the first set of Common Data Elements for TBI (TBI-CDEs v1) were introduced to achieve these goals. The TBI-CDEs version 2 were developed in 2012 to broaden the approach to all ages, injury severity, and phases of recovery. We aimed to quantify the degree of harmonization of these data elements in three large, prospective multi-center studies conducted within the International Initiative for TBI Research (InTBIR). Data variables of the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI; adult and pediatric patients in Europe and Israel), Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI; adult and pediatric patients in the U.S.), and Approaches and Decisions in Acute Pediatric TBI (ADAPT; international study on severe pediatric TBI) studies were indexed and matched to the second version of the TBI CDEs. We focused on the CDE sub-categories of "Acute Hospitalized" (AH) and "Moderate/Severe TBI: Rehabilitation (Rehab). All "Core" and "Basic" level CDEs were considered. Closely related elements were reduced to one variable to prevent over-representation. Categorical elements and text elements for the same variable were likewise merged to one element for analysis. Following reduction and merging of related elements, 21 Core, 46 Basic AH, and 50 Basic Rehab elements were deemed harmonizable across studies. Gaps in global applicability were identified for four of the TBI CDEs and many of the outcome instruments, which are only available in the English language. Agreements of Core and Basic study CDEs for the AH domain with the TBI CDEs were respectively 81% and 91% for CENTER-TBI, 76% and 93% for TRACK-TBI, and 85% in ADAPT for both domains. For the domain Rehab, agreement with Basic TBI CDEs was 84% for CENTER-TBI, 94% for TRACK-TBI, and 71% for ADAPT. Non-harmonization was largely caused by absence of the elements in the studies. For elements present, the compatibility of coding with TBI CDEs was 90-99%. The degree of harmonization was greatest between CENTER-TBI and TRACK-TBI with 81-87% overlap within the TBI CDE sub-categories. The high degree of harmonization of study variables among these studies demonstrates the importance and utility of common data elements in TBI research. It also confirms the potential for future meta-analyses across these large studies, especially for CENTER TBI and TRACK TBI. The global applicability of the TBI CDEs needs to be improved for them to become a global standard for TBI research. CENTER-TBI, TRACK-TBI, and ADAPT, along with other studies within the InTBIR Initiative, provide a platform to inform further refinement and internationalization for the next version of the TBI CDEs.

Prognostic Validation of the NINDS Common Data Elements for the Radiologic Reporting of Acute Traumatic Brain Injuries: A CENTER-TBI Study.

The aim of this study is to investigate the prognostic value of using the National Institute of Neurological Disorders and Stroke (NINDS) standardized imaging-based pathoanatomic descriptors for the evaluation and reporting of acute traumatic brain injury (TBI) lesions. For a total of 3392 patients (2244 males and 1148 females, median age = 51 years) enrolled in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we extracted 96 Common Data Elements (CDEs) from the structured reports, spanning all three levels of pathoanatomic information (i.e., 20 "basic," 60 "descriptive," and 16 "advanced" CDE variables per patient). Six-month clinical outcome scores were dichotomized into favorable (Glasgow Outcome Scale Extended [GOS-E] = 5-8) versus unfavorable (GOS-E = 1-4). Regularized logistic regression models were constructed and compared using the optimism-corrected area under the curve (AUC). An abnormality was reported for the majority of patients (64.51%). In 79.11% of those patients, there was at least one coexisting pathoanatomic lesion or associated finding. An increase in lesion severity, laterality, and volume was associated with more unfavorable outcomes. Compared with the full set of pathoanatomic descriptors (i.e., all three categories of information), reporting "basic" CDE information provides at least equal discrimination between patients with favorable versus unfavorable outcome (AUC = 0.8121 vs. 0.8155, respectively). Addition of a selected subset of "descriptive" detail to the basic CDEs could improve outcome prediction (AUC = 0.8248). Addition of "advanced" or "emerging/exploratory" information had minimal prognostic value. Our results show that the NINDS standardized-imaging based pathoanatomic descriptors can be used in large-scale studies and provide important insights into acute TBI lesion patterns. When used in clinical predictive models, they can provide excellent discrimination between patients with favorable and unfavorable 6-month outcomes. If further validated, our findings could support the development of structured and itemized templates in routine clinical radiology.

A systematic review identifying common data items in neonatal trials and assessing their completeness in routinely recorded United Kingdom national neonatal data.

BACKGROUND: We aimed to test whether a common set of key data items reported across high-impact neonatal clinical trials could be identified, and to quantify their completeness in routinely recorded United Kingdom neonatal data held in the National Neonatal Research Database (NNRD). METHODS: We systematically reviewed neonatal clinical trials published in four high-impact medical journals over 10 years (2006-2015) and extracted baseline characteristics, stratification items and potential confounders used to adjust primary outcomes. Completeness was examined using data held in the NNRD for identified data items, for infants admitted to neonatal units in 2015. The NNRD is a repository of routinely recorded data extracted from neonatal Electronic Patient Records (EPR) of all admissions to National Health Service (NHS) Neonatal Units in England, Wales and Scotland. We defined missing data as an empty field or an implausible value. We reported common data items as frequencies and percentages alongside percentages of completeness. RESULTS: We identified 44 studies involving 32,095 infants and 126 data items. Fourteen data items were reported by more than 20% of studies. Gestational age (95%), sex (93%) and birth weight (91%) were the most common baseline data items. The completeness of data in the NNRD was high for these data with greater than 90% completeness found for 9 of the 14 most common items. CONCLUSION: High-impact neonatal clinical trials share common data items. In the United Kingdom, these items can be obtained at a high level of completeness from routinely recorded data held in the NNRD. The feasibility and efficiency using routinely recorded EPR data, such as that held in the NNRD, for clinical trials, rather than collecting these items anew, should be examined. TRIAL REGISTRATION: PROSPERO registration number CRD42016046138. Registered prospectively on 17 August 2016.

Can we and should we use animal models to study neurobehavioral comorbidities of epilepsy?

Animal systems have been widely used to examine mechanisms of neurobehavioral comorbidities of epilepsy and to help in developing their effective therapies. Despite the progress made in the field, animal studies have their limitations stemming both from issues with modeling neuropsychiatric disorders in the laboratory and from drawbacks of animal models of epilepsy themselves. This review discusses advantages and weaknesses of experimental paradigms and approaches used to model and to analyze neurobehavioral comorbidities of epilepsy, from the perspectives of their needs, interpretation, ways of improvement, and clinical relevance. Developmental studies are required to adequately address age-specific aspects of the comorbidities. The deployment of preclinical Common Data Elements (pCDEs) for epilepsy research should facilitate the standardization and the harmonization of studies in question, while the application of Research Domain Criteria (RDoC) to characterize neurobehavioral disorders in animals with epilepsy should help in closing the bench-to-bedside gap. Special Issue: Epilepsy & Behavior's 20th Anniversary.

Challenges to the Standardization of Trauma Data Collection in Burn, Traumatic Brain Injury, Spinal Cord Injury, and Other Trauma Populations: A Call for Common Data Elements for Acute and Longitudinal Trauma Databases.

OBJECTIVE: Common data elements (CDEs) promote data sharing, standardization, and uniform data collection, which facilitate meta-analyses and comparisons of studies. Currently, there is no set of CDEs for all trauma populations, but their creation would allow researchers to leverage existing databases to maximize research on trauma outcomes. The purpose of this study is to assess the extent of common data collection among 5 trauma databases. DESIGN: The data dictionaries of 5 trauma databases were examined to determine the extent of common data collection. Databases included 2 acute care databases (American Burn Association's National Burn Data Standard and American College of Surgeons' National Trauma Data Standard) and 3 longitudinal trauma databases (Burn, Traumatic Brain Injury, Spinal Cord Injury Model System National Databases). Data elements and data values were compared across the databases. Quantitative and qualitative variations in the data were identified to highlight meaningful differences between datasets. SETTING: N/A. PARTICIPANTS: N/A. INTERVENTIONS: N/A. MAIN OUTCOME MEASURES: N/A. RESULTS: Of the 30 data elements examined, 14 (47%) were present in all 5 databases. Another 9 (30%) elements were present in 4 of the 5 databases. The number of elements present in each database ranged from 23 (77%) to 26 (86%). There were inconsistencies in the data values across the databases. Twelve of the 14 data elements present in all 5 databases exhibited differences in data values. CONCLUSIONS: This study demonstrates inconsistencies in the documentation of data elements in 5 common trauma databases. These discrepancies are a barrier to database harmonization and to maximizing the use of these databases through linking, pooling, and comparing data. A collaborative effort is required to develop a standardized set of elements for trauma research.

Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation.

BACKGROUND: Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements. OBJECTIVE: We used two different machine learning feature selection techniques of minimum redundancy-maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R). METHODS: We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection. APPLICATION: Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set. DISCUSSION: Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs' focus on client-specific and targeted interventions in the delivery of care.

The ASNR-ACR-RSNA Common Data Elements Project: What Will It Do for the House of Neuroradiology?

The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question. As medicine shifts toward value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow peer-to-peer comparisons in all specialties. Many government programs are now focusing on these measures, the most recent being the Merit-Based Incentive Payment System and the Medicare Access Children's Health Insurance Program Reauthorization Act of 2015. Standardized or structured reporting is advocated as one method of assessing radiology report quality, and common data elements are a means for expressing these concepts. Incorporating common data elements into clinical practice fosters a number of very useful downstream processes including establishing benchmarks for quality-assurance programs, ensuring more accurate billing, improving communication to providers and patients, participating in public health initiatives, creating comparative effectiveness research, and providing classifiers for machine learning. Generalized adoption of the recommended common data elements in clinical practice will provide the means to collect and compare imaging report data from multiple institutions locally, regionally, and even nationally, to establish quality benchmarks.

Unleashing the value of Common Data Elements through the CEDAR Workbench.

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.

The Canadian Pediatric Mild Traumatic Brain Injury Common Data Elements Project: Harmonizing Outcomes to Increase Understanding of Pediatric Concussion.

A critical component for accelerating the clinical uptake of research data in the area of pediatric concussion or mild traumatic brain injury (MTBI) pertains to the establishment and utilization of common databases. The objective of the first phase of our CanPedCDE initiative was to agree upon pediatric common data elements (CDEs) that could best characterize children with MTBI over their recovery period. The selection of CDEs for our framework aimed to balance factors such as the comprehensiveness of outcomes collected, their applicability to diverse settings, as well as the costs associated with their use. Selection began by identifying relevant domains of functioning (e.g., post-concussion symptoms, attention, and balance). Two sources were used to make this process more efficient: 1) the World Health Organization International Classification of Functioning (ICF) Traumatic Brain Injury Core Set, and the U.S. National Institute of Neurological Disorders and Stroke Traumatic Brain Injury Common Data Elements, both of which had already suggested relevant domains to include in TBI research. The process was completed in two phases: 1) using an online survey of experts and 2) through an in-person consensus meeting. Measurement tools were also proposed that were best felt to capture these domains. Forty experts in MTBI in children from multiple health-related perspectives (e.g., emergency medicine, pediatrics, neurosurgery, nursing, physiotherapy, and neuroscience), as well as knowledge users, participated in the selection process. The final list of CDEs included 77 distinct areas of functioning, covering all categories of the ICF model. Outcome measures were attached to each element, when applicable. The CanPedCDE initiative addresses a significant limitation in MTBI research to date and may help both researchers and clinicians to organize and standardize their assessment of children and youth post-MTBI in order to move the field in promising directions.

Common data elements for epilepsy mobile health systems.

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.

A common data language for clinical research studies: the National Institute of Neurological Disorders and Stroke and American Academy for Cerebral Palsy and Developmental Medicine Cerebral Palsy Common Data Elements Version 1.0 recommendations.

To increase the efficiency and effectiveness of clinical research studies, cerebral palsy (CP) specific Common Data Elements (CDEs) were developed through a partnership between the National Institute of Neurological Disorders and Stroke (NINDS) and the American Academy of Cerebral Palsy and Developmental Medicine (AACPDM). International experts reviewed existing NINDS CDEs and tools used in studies of children and young people with CP. CDEs were compiled, subjected to internal review, and posted online for external public comment in September 2016. Guided by the International Classification of Functioning, Disability and Health framework, CDEs were categorized into six domains: (1) participant characteristics; (2) health, growth, and genetics; (3) neuroimaging; (4) neuromotor skills and functional assessments; (5) neurocognitive, social, and emotional assessments; and (6) engagement and quality of life. Version 1.0 of the NINDS/AACPDM CDEs for CP is publicly available on the NINDS CDE and AACPDM websites. Global use of CDEs for CP will standardize data collection, improve data quality, and facilitate comparisons across studies. Ongoing collaboration with international colleagues, industry, and people with CP and their families will provide meaningful feedback and updates as additional evidence is obtained. These CDEs are recommended for NINDS-funded research for CP. WHAT THIS PAPER ADDS: This is the first comprehensive Common Data Elements (CDEs) for children and young people with CP for clinical research. The CDEs for children and young people with CP include common definitions, the standardization of case report forms, and measures. The CDE guides the standardization for data collection and outcome evaluation in all types of studies with children and young people with CP. The CDE ultimately improves data quality and data sharing.

Common data elements for preclinical epilepsy research: Standards for data collection and reporting. A TASK3 report of the AES/ILAE Translational Task Force of the ILAE.

The major objective of preclinical translational epilepsy research is to advance laboratory findings toward clinical application by testing potential treatments in animal models of seizures and epilepsy. Recently there has been a focus on the failure of preclinical discoveries to translate reliably, or even to be reproduced in different laboratories. One potential cause is a lack of standardization in preclinical data collection. The resulting difficulties in comparing data across studies have led to high cost and missed opportunity, which in turn impede clinical trials and advances in medical care. Preclinical epilepsy research has successfully brought numerous antiseizure treatments into the clinical practice, yet the unmet clinical needs have prompted the reconsideration of research strategies to optimize epilepsy therapy development. In the field of clinical epilepsy there have been successful steps to improve such problems, such as generation of common data elements (CDEs) and case report forms (CRFs and standards of data collection and reporting) by a team of leaders in the field. Therefore, the Translational Task Force was appointed by the International League Against Epilepsy (ILAE) and the American Epilepsy Society (AES), in partnership with the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institutes of Health (NIH) to define CDEs for animal epilepsy research studies and prepare guidelines for data collection and experimental procedures. If adopted, the preclinical CDEs could facilitate collaborative epilepsy research, comparisons of data across different laboratories, and promote rigor, transparency, and impact, particularly in therapy development.

Scoping review to develop common data elements for lumbar spinal stenosis.

BACKGROUND CONTEXT: Common data elements (CDE) represent an important tool for understanding and classifying health outcomes across settings. Although CDEs have been developed for a number of disorders, to date CDEs for lumbar spinal stenosis (LSS) have not been fully developed. To facilitate the identification of CDEs and measures to assess them, this technical study leverages the International Classification of Functioning, Disability and Health (ICF), peer-reviewed research, and a panel of experts to identify CDEs specific to LSS. PURPOSE: The study aimed to define CDEs for disease characteristics and outcomes of LSS using the World Health Organization's ICF taxonomy, and to facilitate the selection of assessment instruments for research and clinical care. DESIGN: This is a scoping review using a modified Delphi approach with a technical expert panel composed of clinicians and scientists representing the academia, policy and advocacy stakeholders, and professional associations with expertise in LSS. METHODS: This is a scoping review to identify measures that assess LSS symptoms. Thirty-one subject matter experts (SMEs) prioritized ICF codes and evaluated instruments measuring specific domains. We used a modified Delphi technique to evaluate item-level content and achieve consensus. RESULTS: SMEs prioritized 53 ICF codes; 3 received 100% endorsement, 27 received ≥90% endorsement, whereas the remaining 23 received ≥80% endorsement. Prioritized ICF codes represent diverse domains, including pain, activities and participation, and emotional well-being. The review yielded 58 instruments; we retained 24 for content analysis. CONCLUSIONS: The retained instruments adequately represent the ICFs activities and participation, and body function domains. Body structure and environmental factors were assessed infrequently. Adoption of these CDEs may guide clinical decision making and facilitate comparative effectiveness trials for interventions focused on LSS.

Common data elements for clinical research in mitochondrial disease: a National Institute for Neurological Disorders and Stroke project.

OBJECTIVES: The common data elements (CDE) project was developed by the National Institute of Neurological Disorders and Stroke (NINDS) to provide clinical researchers with tools to improve data quality and allow for harmonization of data collected in different research studies. CDEs have been created for several neurological diseases; the aim of this project was to develop CDEs specifically curated for mitochondrial disease (Mito) to enhance clinical research. METHODS: Nine working groups (WGs), composed of international mitochondrial disease experts, provided recommendations for Mito clinical research. They initially reviewed existing NINDS CDEs and instruments, and developed new data elements or instruments when needed. Recommendations were organized, internally reviewed by the Mito WGs, and posted online for external public comment for a period of eight weeks. The final version was again reviewed by all WGs and the NINDS CDE team prior to posting for public use. RESULTS: The NINDS Mito CDEs and supporting documents are publicly available on the NINDS CDE website ( https://commondataelements.ninds.nih.gov/ ), organized into domain categories such as Participant/Subject Characteristics, Assessments, and Examinations. CONCLUSION: We developed a comprehensive set of CDE recommendations, data definitions, case report forms (CRFs), and guidelines for use in Mito clinical research. The widespread use of CDEs is intended to enhance Mito clinical research endeavors, including natural history studies, clinical trial design, and data sharing. Ongoing international collaboration will facilitate regular review, updates and online publication of Mito CDEs, and support improved consistency of data collection and reporting.