Cerebral palsy pain instruments: Recommended tools for clinical research studies by the National Institute of Neurological Disorders and Stroke Cerebral Palsy Common Data Elements project

AIM

This study describes the process of updating the cerebral palsy (CP) common data elements (CDEs), specifically identifying tools that capture the impact of chronic pain on children's functioning.

METHOD

Through a partnership between the American Academy for Cerebral Palsy and Developmental Medicine and the National Institute of Neurological Disorders and Stroke (NINDS), the CP CDEs were developed as data standards for clinical research in neuroscience. Chronic pain was underrepresented in the NINDS CP CDEs version 1.0. A multi-step methodology was applied by an interdisciplinary professional team. Following an adapted CP chronic pain tools' rating system, and a review of psychometric properties, clinical utility, and compliance with inclusion/exclusion criteria, a set of recommended pain tools was posted online for external public comment in May 2022.

RESULTS

Fifteen chronic pain tools met inclusion criteria, representing constructs across all components of the International Classification of Functioning, Disability and Health.

INTERPRETATION

This paper describes the first condition-specific pain CDEs for a pediatric population. The proposed set of chronic pain tools complement and enhance the applicability of the existing pediatric CP CDEs. The novel CP CDE pain tools harmonize the assessment of chronic pain, addressing not only intensity of chronic pain, but also the functional impact of experiencing it in everyday activities.

Mapping of Alzheimer's disease related data elements and the NIH Common Data Elements

BACKGROUND

Alzheimer's Disease (AD) is a devastating disease that destroys memory and other cognitive functions. There has been an increasing research effort to prevent and treat AD. In the US, two major data sharing resources for AD research are the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI); Additionally, the National Institutes of Health (NIH) Common Data Elements (CDE) Repository has been developed to facilitate data sharing and improve the interoperability among data sets in various disease research areas.

METHOD

To better understand how AD-related data elements in these resources are interoperable with each other, we leverage different representation models to map data elements from different resources: NACC to ADNI, NACC to NIH CDE, and ADNI to NIH CDE. We explore bag-of-words based and word embeddings based models (Word2Vec and BioWordVec) to perform the data element mappings in these resources.

RESULTS

The data dictionaries downloaded on November 23, 2021 contain 1,195 data elements in NACC, 13,918 in ADNI, and 27,213 in NIH CDE Repository. Data element preprocessing reduced the numbers of NACC and ADNI data elements for mapping to 1,099 and 7,584 respectively. Manual evaluation of the mapping results showed that the bag-of-words based approach achieved the best precision, while the BioWordVec based approach attained the best recall. In total, the three approaches mapped 175 out of 1,099 (15.92%) NACC data elements to ADNI; 107 out of 1,099 (9.74%) NACC data elements to NIH CDE; and 171 out of 7,584 (2.25%) ADNI data elements to NIH CDE.

CONCLUSIONS

The bag-of-words based and word embeddings based approaches showed promise in mapping AD-related data elements between different resources. Although the mapping approaches need further improvement, our result indicates that there is a critical need to standardize CDEs across these valuable AD research resources in order to maximize the discoveries regarding AD pathophysiology, diagnosis, and treatment that can be gleaned from them.

Common Data Element for Disorders of Consciousness: Recommendations from the Working Group on Therapeutic Interventions

BACKGROUND

Over the past 30 years, there have been significant advances in the understanding of the mechanisms associated with loss and recovery of consciousness following severe brain injury. This work has provided a strong grounding for the development of novel restorative therapeutic interventions. Although all interventions are aimed at modulating and thereby restoring brain function, the landscape of existing interventions encompasses a very wide scope of techniques and protocols. Despite vigorous research efforts, few approaches have been assessed with rigorous, high-quality randomized controlled trials. As a growing number of exploratory interventions emerge, it is paramount to develop standardized approaches to reporting results. The successful evaluation of novel interventions depends on implementation of shared nomenclature and infrastructure. To address this gap, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups and charged them with developing common data elements (CDEs). Here, we report the work of the Therapeutic Interventions Working Group.

METHODS

The working group reviewed existing CDEs relevant to therapeutic interventions within the National Institutes of Health National Institute of Neurological Disorders and Stroke database and reviewed the literature for assessing key areas of research in the intervention space. CDEs were then proposed, iteratively discussed and reviewed, classified, and organized in a case report form (CRF).

RESULTS

We developed a unified CRF, including CDEs and key design elements (i.e., methodological or protocol parameters), divided into five sections: (1) patient information, (2) general study information, (3) behavioral interventions, (4) pharmacological interventions, and (5) device interventions.

CONCLUSIONS

The newly created CRF enhances systematization of future work by proposing a portfolio of measures that should be collected in the development and implementation of studies assessing novel interventions intended to increase the level of consciousness or rate of recovery of consciousness in patients with disorders of consciousness.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group in the Pediatric Population

BACKGROUND

The fundamental gap obstructing forward progress of evidenced-based care in pediatric and neonatal disorders of consciousness (DoC) is the lack of defining consensus-based terminology to perform comparative research. This lack of shared nomenclature in pediatric DoC stems from the inherently recursive dilemma of the inability to reliably measure consciousness in the very young. However, recent advancements in validated clinical examinations and technologically sophisticated biomarkers of brain activity linked to future abilities are unlocking this previously formidable challenge to understanding the DoC in the developing brain.

METHODS

To address this need, the first of its kind international convergence of an interdisciplinary team of pediatric DoC experts was organized by the Neurocritical Care Society's Curing Coma Campaign. The multidisciplinary panel of pediatric DoC experts proposed pediatric-tailored common data elements (CDEs) covering each of the CDE working groups including behavioral phenotyping, biospecimens, electrophysiology, family and goals of care, neuroimaging, outcome and endpoints, physiology and big Data, therapies, and pediatrics.

RESULTS

We report the working groups' pediatric-focused DoC CDE recommendations and disseminate CDEs to be used in studies of pediatric patients with DoC.

CONCLUSIONS

The CDEs recommended support the vision of progressing collaborative and successful internationally collaborative pediatric coma research.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Biospecimens and Biomarkers

BACKGROUND

In patients with disorders of consciousness (DoC), laboratory and molecular biomarkers may help define endotypes, identify therapeutic targets, prognosticate outcomes, and guide patient selection in clinical trials. We performed a systematic review to identify common data elements (CDEs) and key design elements (KDEs) for future coma and DoC research.

METHODS

The Curing Coma Campaign Biospecimens and Biomarkers work group, composed of seven invited members, reviewed existing biomarker and biospecimens CDEs and conducted a systematic literature review for laboratory and molecular biomarkers using predetermined search words and standardized methodology. Identified CDEs and KDEs were adjudicated into core, basic, supplemental, or experimental CDEs per National Institutes of Health classification based on level of evidence, reproducibility, and generalizability across different diseases through a consensus process.

RESULTS

Among existing National Institutes of Health CDEs, those developed for ischemic stroke, traumatic brain injury, and subarachnoid hemorrhage were most relevant to DoC and included. KDEs were common to all disease states and included biospecimen collection time points, baseline indicator, biological source, anatomical location of collection, collection method, and processing and storage methodology. Additionally, two disease core, nine basic, 24 supplemental, and 59 exploratory biomarker CDEs were identified. Results were summarized and generated into a Laboratory Data and Biospecimens Case Report Form (CRF) and underwent public review. A final CRF version 1.0 is reported here.

CONCLUSIONS

Exponential growth in biomarkers development has generated a growing number of potential experimental biomarkers associated with DoC, but few meet the quality, reproducibility, and generalizability criteria to be classified as core and basic biomarker and biospecimen CDEs. Identification and adaptation of KDEs, however, contribute to standardizing methodology to promote harmonization of future biomarker and biospecimens studies in DoC. Development of this CRF serves as a basic building block for future DoC studies.

ARDaC Common Data Model Facilitates Data Dissemination and Enables Data Commons for Modern Clinical Studies

Modern clinical studies collect longitudinal and multimodal data about participants, treatments and responses, biospecimens, and molecular and multiomics data. Such rich and complex data requires new common data models (CDM) to support data dissemination and research collaboration. We have developed the ARDaC CDM for the Alcoholic Hepatitis Network (AlcHepNet) Research Data Commons (ARDaC) to support clinical studies and translational research in the national AlcHepNet consortium. The ARDaC CDM bridges the gap between the data models used by the AlcHepNet electronic data capture platform (REDCap) and the Genomic Data Commons (GDC) data model used by the Gen3 data commons framework. It extends the GDC data model for clinical studies; facilitates the harmonization of research data across consortia and programs; and supports the development of the ARDaC. ARDaC CDM is designed as a general and extensible CDM for addressing the needs of modern clinical studies. The ARDaC CDM is available at https://dev.ardac.org/DD.

Making Digital Health Equitable

Most agree that the current healthcare system is broken. Fortunately, technology is increasing at an exponential rate and provides a solution for the future. Digital Health is an integrator concept that has the potential to take advantage of technological advantages. Digital Health converges health, healthcare, research, and everyday life. It includes technologies, platforms, and systems that engage consumers in all aspects of life. It makes health and healthcare be people-centered and personalized. Digital health requires total interoperability - standards, common data elements, and the integration of data from all sources. It demands data sharing. Digital Health brings together a wide range of stakeholders for similar goals using the same resources. Digital Health uses mobile devices and wearable sensors and uses Artificial Intelligence and Machine Learning to handle the vast amount of data Digital Health engages. Finally, Digital Health has the potential to open the gap between the different social and economic classes that must be addressed.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Neuroimaging

BACKGROUND

Over the past 5 decades, advances in neuroimaging have yielded insights into the pathophysiologic mechanisms that cause disorders of consciousness (DoC) in patients with severe brain injuries. Structural, functional, metabolic, and perfusion imaging studies have revealed specific neuroanatomic regions, such as the brainstem tegmentum, thalamus, posterior cingulate cortex, medial prefrontal cortex, and occipital cortex, where lesions correlate with the current or future state of consciousness. Advanced imaging modalities, such as diffusion tensor imaging, resting-state functional magnetic resonance imaging (fMRI), and task-based fMRI, have been used to improve the accuracy of diagnosis and long-term prognosis, culminating in the endorsement of fMRI for the clinical evaluation of patients with DoC in the 2018 US (task-based fMRI) and 2020 European (task-based and resting-state fMRI) guidelines. As diverse neuroimaging techniques are increasingly used for patients with DoC in research and clinical settings, the need for a standardized approach to reporting results is clear. The success of future multicenter collaborations and international trials fundamentally depends on the implementation of a shared nomenclature and infrastructure.

METHODS

To address this need, the Neurocritical Care Society's Curing Coma Campaign convened an international panel of DoC neuroimaging experts to propose common data elements (CDEs) for data collection and reporting in this field.

RESULTS

We report the recommendations of this CDE development panel and disseminate CDEs to be used in neuroimaging studies of patients with DoC.

CONCLUSIONS

These CDEs will support progress in the field of DoC neuroimaging and facilitate international collaboration.

Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group

BACKGROUND

Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group.

METHODS

After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs.

RESULTS

After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group.

CONCLUSIONS

Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Physiology and Big Data

BACKGROUND

The implementation of multimodality monitoring in the clinical management of patients with disorders of consciousness (DoC) results in physiological measurements that can be collected in a continuous and regular fashion or even at waveform resolution. Such data are considered part of the "Big Data" available in intensive care units and are potentially suitable for health care-focused artificial intelligence research. Despite the richness in content of the physiological measurements, and the clinical implications shown by derived metrics based on those measurements, they have been largely neglected from previous attempts in harmonizing data collection and standardizing reporting of results as part of common data elements (CDEs) efforts. CDEs aim to provide a framework for unifying data in clinical research and help in implementing a systematic approach that can facilitate reliable comparison of results from clinical studies in DoC as well in international research collaborations.

METHODS

To address this need, the Neurocritical Care Society's Curing Coma Campaign convened a multidisciplinary panel of DoC "Physiology and Big Data" experts to propose CDEs for data collection and reporting in this field.

RESULTS

We report the recommendations of this CDE development panel and disseminate CDEs to be used in physiologic and big data studies of patients with DoC.

CONCLUSIONS

These CDEs will support progress in the field of DoC physiologic and big data and facilitate international collaboration.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Hospital Course, Confounders, and Medications

The convergence of an interdisciplinary team of neurocritical care specialists to organize the Curing Coma Campaign is the first effort of its kind to coordinate national and international research efforts aimed at a deeper understanding of disorders of consciousness (DoC). This process of understanding includes translational research from bench to bedside, descriptions of systems of care delivery, diagnosis, treatment, rehabilitation, and ethical frameworks. The description and measurement of varying confounding factors related to hospital care was thought to be critical in furthering meaningful research in patients with DoC. Interdisciplinary hospital care is inherently varied across geographical areas as well as community and academic medical centers. Access to monitoring technologies, specialist consultation (medical, nursing, pharmacy, respiratory, and rehabilitation), staffing resources, specialty intensive and acute care units, specialty medications and specific surgical, diagnostic and interventional procedures, and imaging is variable, and the impact on patient outcome in terms of DoC is largely unknown. The heterogeneity of causes in DoC is the source of some expected variability in care and treatment of patients, which necessitated the development of a common nomenclature and set of data elements for meaningful measurement across studies. Guideline adherence in hemorrhagic stroke and severe traumatic brain injury may also be variable due to moderate or low levels of evidence for many recommendations. This article outlines the process of the development of common data elements for hospital course, confounders, and medications to streamline definitions and variables to collect for clinical studies of DoC.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Goals-of-Care and Family/Surrogate Decision-Maker Data

BACKGROUND

To facilitate comparative research, it is essential for the fields of neurocritical care and rehabilitation to establish common data elements (CDEs) for disorders of consciousness (DoC). Our objective was to identify CDEs related to goals-of-care decisions and family/surrogate decision-making for patients with DoC.

METHODS

To achieve this, we formed nine CDE working groups as part of the Neurocritical Care Society's Curing Coma Campaign. Our working group focused on goals-of-care decisions and family/surrogate decision-makers created five subgroups: (1) clinical variables of surrogates, (2) psychological distress of surrogates, (3) decision-making quality, (4) quality of communication, and (5) quality of end-of-life care. Each subgroup searched for existing relevant CDEs in the National Institutes of Health/CDE catalog and conducted an extensive literature search for additional relevant study instruments to be recommended. We classified each CDE according to the standard definitions of "core", "basic", "exploratory", or "supplemental", as well as their use for studying the acute or chronic phase of DoC, or both.

RESULTS

We identified 32 relevant preexisting National Institutes of Health CDEs across all subgroups. A total of 34 new instruments were added across all subgroups. Only one CDE was recommended as disease core, the "mode of death" of the patient from the clinical variables subgroup.

CONCLUSIONS

Our findings provide valuable CDEs specific to goals-of-care decisions and family/surrogate decision-making for patients with DoC that can be used to standardize studies to generate high-quality and reproducible research in this area.

A Common Data Element-Based Adjudication Process for mTBI Clinical Profiles: A Targeted Multidomain Clinical Trial Preliminary Study

INTRODUCTION

The primary purpose of this study was to examine the prevalence and percent agreement of clinician-identified mild traumatic brain injury (mTBI) clinical profiles and cutoff scores for selected Federal Interagency Traumatic Brain Injury Research common data elements (CDEs). A secondary purpose was to investigate the predictive value of established CDE assessments in determining clinical profiles in adults with mTBI.

MATERIALS AND METHODS

Seventy-one (23 males; 48 females) participants (M = 29.00, SD = 7.60, range 18-48 years) within 1-5 months (M = 24.20, SD = 25.30, range 8-154 days) of mTBI completed a clinical interview/exam and a multidomain assessment conducted by a licensed clinician with specialized training in concussion, and this information was used to identify mTBI clinical profile(s). A researcher administered CDE assessments to all participants, and scores exceeding CDE cutoffs were used to identify an mTBI clinical profile. The clinician- and CDE-identified clinical profiles were submitted to a multidisciplinary team for adjudication. The prevalence and percent agreement between clinician- and CDE-identified clinical profiles was documented, and a series of logistic regressions with adjusted odds ratios were performed to identify which CDE assessments best predicted clinician-identified mTBI clinical profiles.

RESULTS

Migraine/headache, vestibular, and anxiety/mood mTBI clinical profiles exhibited the highest prevalence and overall percent agreement among CDE and clinician approaches. Participants exceeding cutoff scores for the Global Severity Index and Headache Impact Test-6 assessments were 3.90 and 8.81 times more likely to have anxiety/mood and migraine/headache profiles, respectively. The Vestibular/Ocular Motor Screening vestibular items and the Pittsburgh Sleep Quality Index total score were predictive of clinician-identified vestibular and sleep profiles, respectively.

CONCLUSIONS

The CDEs from migraine/headache, vestibular, and anxiety/mood domains, and to a lesser extent the sleep modifier, may be clinically useful for identifying patients with these profiles following mTBI. However, CDEs for cognitive and ocular may have more limited clinical value for identifying mTBI profiles.

External Validation of Natural Language Processing Algorithms to Extract Common Data Elements in THA Operative Notes

BACKGROUND

Natural language processing (NLP) systems are distinctive in their ability to extract critical information from raw text in electronic health records (EHR). We previously developed three algorithms for total hip arthroplasty (THA) operative notes with rules aimed at capturing (1) operative approach, (2) fixation method, and (3) bearing surface using inputs from a single institution. The purpose of this study was to externally validate and improve these algorithms as a prerequisite for broader adoption in automated registry data curation.

METHODS

The previous NLP algorithms developed at Mayo Clinic were deployed and refined on EHRs from OrthoCarolina, evaluating 39 randomly selected primary THA operative reports from 2018 to 2021. Operative reports were available only in PDF format, requiring conversion to "readable" text with Adobe software. Accuracy statistics were calculated against manual chart review.

RESULTS

The operative approach, fixation technique, and bearing surface algorithms all demonstrated perfect accuracy of 100%. By comparison, validated performance at the developing center yielded an accuracy of 99.2% for operative approach, 90.7% for fixation technique, and 95.8% for bearing surface.

CONCLUSION

NLP algorithms applied to data from an external center demonstrated excellent accuracy in delineating common elements in THA operative notes. Notably, the algorithms had no functional problems evaluating scanned PDFs that were converted to "readable" text by common software. Taken together, these findings provide promise for NLP applied to scanned PDFs as a source to develop large registries by reliably extracting data of interest from very large unstructured data sets in an expeditious and cost-effective manner.

Extraction of Interoperable Data from Healthcare Documents by Identifying Common Data Elements: An Analysis of Radiation Therapy Planning CT Physician Order Entry Records

INTRODUCTION

Documentation as well as IT-based management of medical data is of ever-increasing relevance in modern medicine. As radiation oncology is a rather technical, data-driven discipline, standardization, and data exchange are in principle possible. We examined electronic healthcare documents to extract structured information. Planning CT order entry documents were chosen for the analysis, as this covers a common and structured step in radiation oncology, for which standardized documentation may be achieved. The aim was to examine the extent to which relevant information may be exchanged among different institutions.

MATERIALS AND METHODS

We contacted representatives of nine radiation oncology departments. Departments using standardized electronic documentation for planning CT were asked to provide templates of their records, which were analyzed in terms of form and content. Structured information was extracted by identifying definite common data elements, containing explicit information. Relevant common data elements were identified and classified. A quantitative analysis was performed to evaluate the possibility of data exchange.

RESULTS

We received data of seven documents that were heterogeneous regarding form and content. 181 definite common data elements considered relevant for the planning CT were identified and assorted into five semantic groups. 139 data elements (76.8%) were present in only one document. The other 42 data elements were present in two to six documents, while none was shared among all seven documents.

CONCLUSION

Structured and interoperable documentation of medical information can be achieved using common data elements. Our analysis showed that a lot of information recorded with healthcare documents can be presented with this approach. Yet, in the analyzed cohort of planning CT order entries, only a few common data elements were shared among the majority of documents. A common vocabulary and consensus upon relevant information is required to promote interoperability and standardization.

Myalgic Encephalomyelitis-Chronic Fatigue Syndrome Common Data Element item content analysis

INTRODUCTION

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multisystem chronic disease estimated to affect 836,000-2.5 million individuals in the United States. Persons with ME/CFS have a substantial reduction in their ability to engage in pre-illness levels of activity. Multiple symptoms include profound fatigue, post-exertional malaise, unrefreshing sleep, cognitive impairment, orthostatic intolerance, pain, and other symptoms persisting for more than 6 months. Diagnosis is challenging due to fluctuating and complex symptoms. ME/CFS Common Data Elements (CDEs) were identified in the National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) Common Data Element Repository. This study reviewed ME/CFS CDEs item content.

METHODS

Inclusion criteria for CDEs (measures recommended for ME/CFS) analysis: 1) assesses symptoms; 2) developed for adults; 3) appropriate for patient reported outcome measure (PROM); 4) does not use visual or pictographic responses. Team members independently reviewed CDEs item content using the World Health Organization International Classification of Functioning, Disability and Health (ICF) framework to link meaningful concepts.

RESULTS

119 ME/CFS CDEs (measures) were reviewed and 38 met inclusion criteria, yielding 944 items linked to 1503 ICF meaningful concepts. Most concepts linked to ICF Body Functions component (b-codes; n = 1107, 73.65%) as follows: Fatiguability (n = 220, 14.64%), Energy Level (n = 166, 11.04%), Sleep Functions (n = 137, 9.12%), Emotional Functions (n = 131, 8.72%) and Pain (n = 120, 7.98%). Activities and Participation concepts (d codes) accounted for a smaller percentage of codes (n = 385, 25.62%). Most d codes were linked to the Mobility category (n = 69, 4.59%) and few items linked to Environmental Factors (e codes; n = 11, 0.73%).

DISCUSSION

Relatively few items assess the impact of ME/CFS symptoms on Activities and Participation. Findings support development of ME/CFS-specific PROMs, including items that assess activity limitations and participation restrictions. Development of psychometrically-sound, symptom-based item banks administered as computerized adaptive tests can provide robust assessments to assist primary care providers in the diagnosis and care of patients with ME/CFS.

Learning important common data elements from shared study data: The All of Us program analysis

There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.

NIH HEAL Clinical Data Elements (CDE) implementation: NIH HEAL Initiative IMPOWR network IDEA-CC

OBJECTIVE

The National Institutes of Health (NIH) HEAL Initiative is making data findable, accessible, interoperable, and reusable (FAIR) to maximize the value of the unprecedented federal investment in pain and opioid-use disorder research. This involves standardizing the use of common data elements (CDE) for clinical research.

METHODS

This work describes the process of the selection, processing, harmonization, and design constraints of CDE across a pain and opioid use disorder clinical trials network (NIH HEAL IMPOWR).

RESULTS

The network alignment allowed for incorporation of newer data standards across the clinical trials. Specific advances included geographic coding (RUCA), deidentified patient identifiers (GUID), shareable clinical survey libraries (REDCap), and concept mapping to standardized concepts (UMLS).

CONCLUSIONS

While complex, harmonization across a network of chronic pain and opioid use disorder clinical trials with separate interventions can be optimized through use of CDEs and data standardization processes. This standardization process will support the robust secondary data analyses. Scaling this process could standardize CDE results across interventions or disease state which could help inform insurance companies or government organizations about coverage determinations. The development of the HEAL CDE program supports connecting isolated studies and solutions to each other, but the practical aspects may be challenging for some studies to implement. Leveraging tools and technology to simplify process and create ready to use resources may support wider adoption of consistent data standards.

Common Data Element Collection in Underserved School Communities: Challenges and Recommendations

OBJECTIVES

To provide recommendations for future common data element (CDE) development and collection that increases community partnership, harmonizes data interpretation, and continues to reduce barriers of mistrust between researchers and underserved communities.

METHODS

We conducted a cross-sectional qualitative and quantitative evaluation of mandatory CDE collection among Rapid Acceleration of Diagnostics-Underserved Populations Return to School project teams with various priority populations and geographic locations in the United States to: (1) compare racial and ethnic representativeness of participants completing CDE questions relative to participants enrolled in project-level testing initiatives and (2) identify the amount of missing CDE data by CDE domain. Additionally, we conducted analyses stratified by aim-level variables characterizing CDE collection strategies.

RESULTS

There were 15 study aims reported across the 13 participating Return to School projects, of which 7 (47%) were structured so that CDEs were fully uncoupled from the testing initiative, 4 (27%) were fully coupled, and 4 (27%) were partially coupled. In 9 (60%) study aims, participant incentives were provided in the form of monetary compensation. Most project teams modified CDE questions (8/13; 62%) to fit their population. Across all 13 projects, there was minimal variation in the racial and ethnic distribution of CDE survey participants from those who participated in testing; however, fully uncoupling CDE questions from testing increased the proportion of Black and Hispanic individuals participating in both initiatives.

CONCLUSIONS

Collaboration with underrepresented populations from the early study design process may improve interest and participation in CDE collection efforts.

The Australian and New Zealand brain injury lifespan cohort protocol: Leveraging common data elements to characterise longitudinal outcome and recovery

INTRODUCTION

Cognitive, behavioural, academic, mental health and social impairments are common following paediatric traumatic brain injury (TBI). However, studies are often reliant on small samples of children drawn from narrow age bands, and employ highly variable methodologies, which make it challenging to generalise existing research findings and understand the lifetime history of TBI.

METHOD AND ANALYSIS

This study will synthesise common data sets from national (Victoria, New South Wales, Queensland) and international (New Zealand) collaborators, such that common data elements from multiple cohorts recruited from these four sites will be extracted and harmonised. Participant-level harmonised data will then be pooled to create a single integrated data set of participants including common cognitive, social, academic and mental health outcome variables. The large sample size (n=1816), consisting of participants with mild, moderate and severe TBI, will provide statistical power to answer important questions that cannot be addressed by small, individual cohorts. Complex statistical modelling, such as generalised estimation equation, multilevel and latent growth models, will be conducted.

ETHICS AND DISSEMINATION

Ethics approval was granted by the Human Research Ethics Committee (HREC) of the Royal Children's Hospital (RCH), Melbourne (HREC Reference Number 2019.168). The approved study protocol will be used for all study-related procedures. Findings will be translated into clinical practice, inform policy decisions, guide the appropriate allocation of limited healthcare resources and support the implementation of individualised care.

Interrater Reliability of National Institutes of Health Traumatic Brain Injury Imaging Common Data Elements for Brain Magnetic Resonance Imaging in Mild Traumatic Brain Injury

The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI.

Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations

OBJECTIVE

To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.

MATERIALS AND METHODS

We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.

RESULTS

Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.

DISCUSSION AND CONCLUSIONS

We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Identification of Common Data Elements from Pivotal FDA Trials

It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.

Development and implementation of common data elements for venous thromboembolism research: on behalf of SSC Subcommittee on official Communication from the SSC of the ISTH

Clinical research in venous thromboembolism (VTE) is hindered by variability in the collection and reporting of data and outcomes. A consistent data language facilitates efficiencies, leads to higher quality data, and permits between-study comparisons and evidence synthesis. The International Society on Thrombosis and Haemostasis (ISTH) launched an international task force of more than 50 researchers to develop common data elements for clinical research in venous thromboembolism. The project was organized in seven working groups, each focusing on a topic area: General Core Data Elements; Anticoagulation and Other Therapies; Chronic VTE and Functional Outcomes; Diagnosis of VTE; Malignancy; Perioperative; and Predictors of VTE. The groups met via teleconference to collaboratively identify key data elements and develop definitions and data standards that were structured in a project-specific taxonomy. A Steering Committee met by teleconference and in-person to determine the overall scope of the project and resolve questions arising from the working groups. ISTH held an open public comment period to enable broader stakeholder involvement and feedback. The common data elements were then refined by the working groups to create a set of 512 unique data elements that are publicly available at http://isth.breakthrough.healthcare. The ISTH VTE Common Data Elements are intended to be a living project with ongoing curation, future expansion, and adaptation to meet the needs of the thrombosis and hemostasis research community.

Design and implementation of electronic health record common data elements for pediatric epilepsy: Foundations for a learning health care system

OBJECTIVE

Common data elements (CDEs) are standardized questions and answer choices that allow aggregation, analysis, and comparison of observations from multiple sources. Clinical CDEs are foundational for learning health care systems, a data-driven approach to health care focused on continuous improvement of outcomes. We aimed to create clinical CDEs for pediatric epilepsy.

METHODS

A multiple stakeholder group (clinicians, researchers, parents, caregivers, advocates, and electronic health record [EHR] vendors) developed clinical CDEs for routine care of children with epilepsy. Initial drafts drew from clinical epilepsy note templates, CDEs created for clinical research, items in existing registries, consensus documents and guidelines, quality metrics, and outcomes needed for demonstration projects. The CDEs were refined through discussion and field testing. We describe the development process, rationale for CDE selection, findings from piloting, and the CDEs themselves. We also describe early implementation, including experience with EHR systems and compatibility with the International League Against Epilepsy classification of seizure types.

RESULTS

Common data elements were drafted in August 2017 and finalized in January 2020. Prioritized outcomes included seizure control, seizure freedom, American Academy of Neurology quality measures, presence of common comorbidities, and quality of life. The CDEs were piloted at 224 visits at 10 centers. The final CDEs included 36 questions in nine sections (number of questions): diagnosis (1), seizure frequency (9), quality of life (2), epilepsy history (6), etiology (8), comorbidities (2), treatment (2), process measures (5), and longitudinal history notes (1). Seizures are categorized as generalized tonic-clonic (regardless of onset), motor, nonmotor, and epileptic spasms. Focality is collected as epilepsy type rather than seizure type. Seizure frequency is measured in nine levels (all used during piloting). The CDEs were implemented in three vendor systems. Early clinical adoption included 1294 encounters at one center.

SIGNIFICANCE

We created, piloted, refined, finalized, and implemented a novel set of clinical CDEs for pediatric epilepsy.

The Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID): Development of Case Report Forms for Global Use

Since its original report in January 2020, the coronavirus disease 2019 (COVID-19) due to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly become one of the deadliest global pandemics. Early reports indicate possible neurological manifestations associated with COVID-19, with symptoms ranging from mild to severe, highly variable prevalence rates, and uncertainty regarding causal or coincidental occurrence of symptoms. As neurological involvement of any systemic disease is frequently associated with adverse effects on morbidity and mortality, obtaining accurate and consistent global data on the extent to which COVID-19 may impact the nervous system is urgently needed. To address this need, investigators from the Neurocritical Care Society launched the Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID). The GCS-NeuroCOVID consortium rapidly implemented a Tier 1, pragmatic study to establish phenotypes and prevalence of neurological manifestations of COVID-19. A key component of this global collaboration is development and application of common data elements (CDEs) and definitions to facilitate rigorous and systematic data collection across resource settings. Integration of these elements is critical to reduce heterogeneity of data and allow for future high-quality meta-analyses. The GCS-NeuroCOVID consortium specifically designed these elements to be feasible for clinician investigators during a global pandemic when healthcare systems are likely overwhelmed and resources for research may be limited. Elements include pediatric components and translated versions to facilitate collaboration and data capture in Latin America, one of the epicenters of this global outbreak. In this manuscript, we share the specific data elements, definitions, and rationale for the adult and pediatric CDEs for Tier 1 of the GCS-NeuroCOVID consortium, as well as the translated versions adapted for use in Latin America. Global efforts are underway to further harmonize CDEs with other large consortia studying neurological and general aspects of COVID-19 infections. Ultimately, the GCS-NeuroCOVID consortium network provides a critical infrastructure to systematically capture data in current and future unanticipated disasters and disease outbreaks.

Building a Research Data Repository for Chronic Condition Self-Management Using Harmonized Data

BACKGROUND

Building nursing research data repositories with the goal of comparing and synthesizing results across numerous studies and public sharing of data is still in early stages of development.

OBJECTIVES

We describe the process of using common data elements (CDEs) to build a data repository for research addressing self-management of chronic conditions. Issues in the development of CDEs, lessons learned in the creation of a combined data set across seven studies of different chronic condition populations, and recommendations for creating and sharing harmonized nursing research data sets are provided.

METHODS

In 2014, at initiation of a National Institutes of Health-funded Centers of Excellence in Self-Management Research, our center investigators defined a set of CDEs for use in future center-funded pilot studies consisting of populations having different chronic conditions with the intent to combine the study data sets. Over the next 4 years, center investigators were provided with standardized codebooks and data collection protocols for applying the CDEs and data storage. Data from seven pilot studies were subsequently combined.

RESULTS

Although each pilot study was small-with sample sizes ranging from 18 to 31 participants-our combined data set of 179 participants provides us with a sample size sufficient to conduct analyses that could not be done with the individual small samples alone. The research data repository addressing self-management of chronic conditions will soon be available for public sharing.

DISCUSSION

Our experience demonstrates that, with careful, upfront planning and ongoing vigilant oversight, CDEs can be applied across studies consisting of different chronic condition populations to combine data sets to create research data repositories for public sharing.

Delineating the Nature and Correlates of Social Dysfunction after Childhood Traumatic Brain Injury Using Common Data Elements: Evidence from an International Multi-Cohort Study

Although childhood traumatic brain injury (TBI) has been linked to heightened risk of impaired social skills and behavior, current evidence is weakened by small studies of variable methodological quality. To address these weaknesses, this international multi-cohort study involved synthesis of data from two large observational cohort studies of complicated mild-severe child TBI in Australia and North America. Both studies adopted a unified approach to data collection and coding procedures, providing the opportunity to merge datasets from multiple, well-characterized cohorts for which gold standard measures of social outcomes were collected during the chronic recovery phase. The study involved 218 children, including 33 children with severe TBI, 83 children with complicated mild-moderate TBI, 59 children with orthopedic injury, and 43 age- and sex-matched typically developing control children. All injured children were recruited from academic children's hospitals and underwent direct cognitive assessments including measures of theory of mind (ToM) at least 1-year post- injury. Parents rated their child's social adjustment using standardized measures of social skills, communication and behavior. Results showed a brain-injury specific effect on ToM abilities, such that children with both complicated mild to moderate and severe TBI displayed significantly poorer ToM than children without TBI. In mediator models, poorer ToM predicted poorer parent-rated self-direction and social skills, as well as more frequent behavioral symptoms. The ToM mediated the effect of severe TBI on parent ratings of communication and social skills, as well as on overall behavior symptoms. The findings suggest that deficits in ToM are evident across the spectrum of TBI severity and represent one mechanism linking severe child TBI to long-term social adjustment difficulties. The findings underscore the value of large-scale data harmonization projects to increase the quality of evidence regarding the outcomes of TBI. Clinical and scientific implications are discussed.

Introducing an Ontology-Driven Pipeline for the Identification of Common Data Elements

Common Data Elements (CDEs) are necessary for ensuring data sharing across studies, providing comparability, and enabling aggregation and meta-analyses. The process of developing a set of CDEs for a given clinical research area has typically been arduous and time-consuming. In this work we introduce an automated pipeline that can greatly aid the process by identifying, aggregating, and ranking relevant CDEs from the outcomes of studies registered on clinicaltrials.gov (CTG). The pipeline uses the Medical Subject Headings (MeSH) ontology to group and rank candidate CDEs by specific diseases. The initial CDE pipeline has been tested using an emerging research domain. The resulting CDEs output was aligned with the current recommendations in the corresponding subject area. Further development of automated means for CDE generation based on structured information from CTG and MeSH is warranted.

Sharing of Individual Participant Data from Clinical Trials: General Comparison and HIV Use Case

Sharing of individual participant data is encouraged by the International Committee of Medical Journal Editors. We analyzed clinical trial registry data from ClinicalTrials.gov (CTG) and determined the proportion of trials sharing de-identified Individual Participant Data (IPD). We looked at 3,138 medical conditions (as Medical Subject Heading terms). Overall, 10.8% of trials with first registration date after December 1, 2015 answered 'Yes' to plan to share de-identified IPD data. This sharing rate ranges between 0% (biliary tract neoplasms) to 72.2% (meningitis, meningococcal) when analyzed by disease that is focus of a study. Via a predictive model, we found that studies that deposited basic summary results data to CTG results registry, large studies and phase 3 interventional studies are most likely to declare intent to share IPD data. As part of an HIV common data element analysis project, we further compared a body of HIV trials (24% sharing rate) to other diseases.

Achieving Data Liquidity: Lessons Learned from Analysis of 38 Clinical Registries (The Duke-Pew Data Interoperability Project

BACKGROUND

To assess the current state of clinical data interoperability, we evaluated the use of data standards across 38 large professional society registries.

METHODS

The analysis included 4 primary components: 1) environmental scan, 2) abstraction and cross-tabulation of clinical concepts and corresponding data elements from registry case report forms, dictionaries, and / or data models, 3) cross-tabulation of same across national common data models, and 4) specifying data element metadata to achieve native data interoperability.

RESULTS

The registry analysis identified approximately 50 core clinical concepts. None were captured using the same data representation across all registries, and there was little implementation of data standards. To improve technical implementation, we specified 13 key metadata for each concept to be used to achieve data consistency.

CONCLUSION

The registry community has not benefitted from and does not contribute to interoperability efforts. A common, authoritative process to specify and implement common data elements is greatly needed.

Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms

With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent years, research networks, such as eMERGE, OHDSI and PCORnet, have been able to increase statistical power and population diversity by combining patient cohorts. These networks share phenotype algorithms that are executed at each participating site. Here we observe experiences with phenotype algorithm portability across seven research networks and propose a generalizable framework for phenotype algorithm portability. Several strategies exist to increase the portability of phenotype algorithms, reducing the implementation effort needed by each site. These include using a common data model, standardized representation of the phenotype algorithm logic, and technical solutions to facilitate federated execution of queries. Portability is achieved by tradeoffs across three domains: Data, Authoring and Implementation, and multiple approaches were observed in representing portable phenotype algorithms. Our proposed framework will help guide future research in operationalizing phenotype algorithm portability at scale.

Common Data Element for Unruptured Intracranial Aneurysm and Subarachnoid Hemorrhage: Recommendations from Assessments and Clinical Examination Workgroup/Subcommittee

BACKGROUND

Clinical studies of subarachnoid hemorrhage (SAH) and unruptured cerebral aneurysms lack uniformity in terms of variables used for assessments and clinical examination of patients which has led to difficulty in comparing studies and performing meta-analyses. The overall goal of the National Institute of Health/National Institute of Neurological Disorders and Stroke Unruptured Intracranial Aneurysms (UIA) and subarachnoid hemorrhage (SAH) Common Data Elements (CDE) Project was to provide common definitions and terminology for future unruptured intracranial aneurysm and SAH research.

METHODS

This paper summarizes the recommendations of the subcommittee on SAH Assessments and Clinical Examination. The subcommittee consisted of an international and multidisciplinary panel of experts in UIA and SAH. Consensus recommendations were developed by reviewing previously published CDEs for other neurological diseases including traumatic brain injury, epilepsy and stroke, and the SAH literature. Recommendations for CDEs were classified by priority into "core," "supplemental-highly recommended," "supplemental" and "exploratory."

RESULTS

We identified 248 variables for Assessments and Clinical Examination. Only the World Federation of Neurological Societies grading scale was classified as "Core." The Glasgow Coma Scale was classified as "Supplemental-Highly Recommended." All other Assessments and Clinical Examination variables were categorized as "Supplemental."

CONCLUSION

The recommended Assessments and Clinical Examination variables have been collated from a large number of potentially useful scales, history, clinical presentation, laboratory, and other tests. We hope that adherence to these recommendations will facilitate the comparison of results across studies and meta-analyses of individual patient data.

Common Data Elements for Radiological Imaging of Patients with Subarachnoid Hemorrhage: Proposal of a Multidisciplinary Research Group

INTRODUCTION

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.

METHODS

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.'

RESULTS

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.

CONCLUSIONS

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.

Clinical Progression of Parkinson's Disease: Insights from the NINDS Common Data Elements

BACKGROUND/OBJECTIVE

To synchronize data collection, the National Institute of Neurological Disorders and Stroke (NINDS) recommended Common Data Elements (CDEs) for use in Parkinson's disease (PD) research. This study delineated the progression patterns of these CDEs in a cohort of PD patients.

METHODS

One hundred-twenty-five PD patients participated in the PD Biomarker Program (PDBP) at Penn State. CDEs, including MDS-Unified PD Rating Scale (UPDRS)-total, questionnaire-based non-motor (-I) and motor (-II), and rater-based motor (-III) subscales; Montreal Cognitive Assessment (MoCA); Hamilton Depression Rating Scale (HDRS); University of Pennsylvania Smell Identification Test (UPSIT); and PD Questionnaire (PDQ-39) were obtained at baseline and three annual follow-ups. Annual change was delineated for PD or subgroups [early = PDE, disease duration (DD) <1 y; middle = PDM, DD = 1-5 y; and late = PDL, DD > 5 y] using mixed effects model analyses.

RESULTS

UPDRS-total, -II, and PDQ-39 scores increased significantly, and UPSIT decreased, whereas UPDRS-I, -III, MoCA, and HDRS did not change, over 36 months in the overall PD cohort. In the PDE subgroup, UPDRS-II increased and UPSIT decreased significantly, whereas MoCA and UPSIT decreased significantly in the PDM subgroup. In the PDL subgroup, UPDRS-II and PDQ-39 increased significantly. Other metrics within each individual subgroup did not change. Sensitivity analyses using subjects with complete data confirmed these findings.

CONCLUSION

Among CDEs, UPDRS-total, -II, PDQ-39, and UPSIT all are sensitive metrics to track PD progression. Subgroup analyses revealed that these CDEs have distinct stage-dependent sensitivities, with UPSIT for DD < 5 y, PDQ-39 for DD > 5 y, UPDRS-II for early (DD < 1) or later stages (DD > 5).

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner

BACKGROUND

The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs.

METHODS

In this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong.

RESULTS

For CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved.

DISCUSSION

Our semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples.

CONCLUSIONS

Experimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

Development of Common Data Elements for Use in Chiari Malformation Type I Clinical Research: An NIH/NINDS Project

The management of Chiari I malformation (CMI) is controversial because treatment methods vary and treatment decisions rest on incomplete understanding of its complex symptom patterns, etiologies, and natural history. Validity of studies that attempt to compare treatment of CMI has been limited because of variable terminology and methods used to describe study subjects. The goal of this project was to standardize terminology and methods by developing a comprehensive set of Common Data Elements (CDEs), data definitions, case report forms (CRFs), and outcome measure recommendations for use in CMI clinical research, as part of the CDE project at the National Institute of Neurological Disorders and Stroke (NINDS) of the US National Institutes of Health. A working group, comprising over 30 experts, developed and identified CDEs, template CRFs, data dictionaries, and guidelines to aid investigators starting and conducting CMI clinical research studies. The recommendations were compiled, internally reviewed, and posted online for external public comment. In October 2016, version 1.0 of the CMI CDE recommendations became available on the NINDS CDE website. The recommendations span these domains: Core Demographics/Epidemiology; Presentation/Symptoms; Co-Morbidities/Genetics; Imaging; Treatment; and Outcome. Widespread use of CDEs could facilitate CMI clinical research trial design, data sharing, retrospective analyses, and consistent data sharing between CMI investigators around the world. Updating of CDEs will be necessary to keep them relevant and applicable to evolving research goals for understanding CMI and its treatment.

EMR-integrated minimal core dataset for routine health care and multiple research settings: A case study for neuroinflammatory demyelinating diseases

Although routine health care and clinical trials usually require the documentation of similar information, data collection is performed independently from each other, resulting in redundant documentation efforts. Standardizing routine documentation can enable secondary use for medical research. Neuroinflammatory demyelinating diseases (NIDs) represent a heterogeneous group of diseases requiring further research to improve patient management. The aim of this work is to develop, implement and evaluate a minimal core dataset in routine health care with a focus on secondary use as case study for NIDs. Therefore, a draft minimal core dataset for NIDs was created by analyzing routine, clinical trial, registry, biobank documentation and existing data standards for NIDs. Data elements (DEs) were converted into the standard format Operational Data Model, semantically annotated and analyzed via frequency analysis. The analysis produced 1958 DEs based on 864 distinct medical concepts. After review and finalization by an interdisciplinary team of neurologists, epidemiologists and medical computer scientists, the minimal core dataset (NID CDEs) consists of 46 common DEs capturing disease-specific information for reuse in the discharge letter and other research settings. It covers the areas of diagnosis, laboratory results, disease progress, expanded disability status scale, therapy and magnetic resonance imaging findings. NID CDEs was implemented in two German university hospitals and a usability study in clinical routine was conducted (participants n = 16) showing a good usability (Mean SUS = 75). From May 2017 to February 2018, 755 patients were documented with the NID CDEs, which indicates the feasibility of developing a minimal core dataset for structured documentation based on previously used documentation standards and integrating the dataset into clinical routine. By sharing, translating and reusing the minimal dataset, a transnational harmonized documentation of patients with NIDs might be realized, supporting interoperability in medical research.

How Clinical Trial Data Sharing Platforms Can Advance the Study of Biomarkers

Although data sharing platforms host diverse data types the features of these platforms are well-suited to facilitating biomarker research. Given the current state of biomarker discovery, an innovative paradigm to accelerate biomarker discovery is to utilize platforms such as Vivli to leverage researchers' abilities to integrate certain classes of biomarkers.

Common Data Elements for National Institute of Mental Health-Funded Translational Early Psychosis Research

The National Institutes of Health has established the PhenX Toolkit as a web-based resource containing consensus measures freely available to the research community. The National Institute of Mental Health (NIMH) has introduced the Mental Health Research Core Collection as part of the PhenX Toolkit and recently convened the PhenX Early Psychosis Working Group to generate the PhenX Early Psychosis Specialty Collection. The Working Group consisted of two complementary panels for clinical and translational research. We review the process, deliberations, and products of the translational research panel. The Early Psychosis Specialty Collection rationale for measure selection as well as additional information and protocols for obtaining each measure are available on the PhenX website (https://www.phenxtoolkit.org). The NIMH strongly encourages investigators to use instruments from the PhenX Mental Health Research Collections in NIMH-funded studies and discourages use of alternative measures to collect similar data without justification. We also discuss some of the potential advances that can be achieved by collecting common data elements across large-scale longitudinal studies of early psychosis.

Enhancing the Representational Power of i2b2 through Referent Tracking

The Informatics for Integrating Biology and the Bedside (i2b2) software platform has proven successful in leveraging clinical enterprise data for the identification of cohorts of patients satisfying certain demographic, phenotypic and genetic criteria in support of further studies. An unanswered question thus far is whether i2b2 search criteria could include characteristics of assertions themselves, e.g. diagnoses, rather than what the assertions (observations) are about, e.g. diseases. This would allow, for instance, to find cohorts of patients for which different providers have been in disagreement about what condition the patient is suffering from. Previous research has shown that this requires more explicit detail about, and unique identification of, two sorts of entities: those that directly or indirectly contribute to the coming into existence of such observations and those that are either explicitly mentioned or merely implied in the assertions. Our research here demonstrates that i2b2's modifier system can be used to represent the relationships between observations and their explicit or implied referents on the one hand, and between relevant referents themselves on the other hand, both in combination with the storage of explicit unique instance identifiers for these observations and referents in i2b2's fact table. While this approach adheres to i2b2's base functionality and implementation specifications, it makes explicit ambiguities and confusions that would otherwise remain undetected.

Toward Reporting Support and Quality Assessment for Learning from Reporting: A Necessary Data Elements Model for Narrative Medication Error Reports

To understand and prevent medication errors, spontaneous reporting systems are developed and implemented to aggregate medication error reports for root cause analysis (RCA). Despite of the rich relational information in medication error reports, low quality, especially incompleteness, impedes effective utilization of the reports for analyzing and learning. The lack of a completeness evaluation tool for narrative medication error reports is a barrier to improving the quality of reports. Moreover, no effective mechanisms are integrated in reporting systems for knowledge support upon reporting. In this study, we developed a minimal data model which defines necessary elements in narrative medication error reports and utilized it to evaluate patient safety organization (PSO) medication reports. This study holds promise in bridging the gap between the low quality of narrative reports and the needs of analyzing and learning from medication errors.

Analyzing Real-World Use of Research Common Data Elements

Common Data Elements (CDEs) are defined as "data elements that are common to multiple data sets across different studies" and provide structured, standardized definitions so that data may be collected and used across different datasets. CDE collections are traditionally developed prospectively by subject-matter and domain experts. However, there has been little systematic research and evidence to demonstrate how CDEs are used in real-world datasets and the subsequent impact on data discoverability. Our study builds upon previous mapping work to investigate the number of CDEs that could be identified using a varying level of commonness threshold in a real-world data repository, the Database of Phenotypes and Genotypes (dbGaP). In an analyzed collection of mapped variables from 426 dbGaP studies, only 1,414 PhenX variables (PHENotypes and eXposures; a CDE initiative) are observed out of all 24,938 defined PhenX variables. Results include CDEs that are identified with varying levels of commonness thresholds. After the semantic grouping of 68 PhenX variables collected in at least 15 studies (n=15), we observed 32 truly "common" common data elements. We discuss benefits of post-hoc mapping of study data to a CDE framework for purposes of findability and reuse, as well as the informatics challenges of pre-populating clinical research case report forms with data from Electronic Health Record that are typically coded in terminologies aimed at routine healthcare needs.

The National Sleep Research Resource: towards a sleep data commons

Objective

The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.

Approach

We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.

Results

The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.

Conclusions

The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.

Gap Analysis and Refinement Recommendations of Skin Alteration and Pressure Ulcer Enterprise Reference Models against Nursing Flowsheet Data Elements

Reference models are an essential instrument to provide structure and guidance in the creation and use of data elements within an organizations' electronic health record (EHR). Standardization of data elements is imperative to ensure clinical data is consistently and reliably captured for use in clinical documentation, care communication, and a variety of downstream data uses. Ongoing assessment and refinement of reference models and data elements are necessary to ascertain clinical data capture is applicable and inclusive across a variety of caregivers and domains. We performed a gap analysis on current state nursing data elements against two validated interprofessional reference models: skin alteration and pressure ulcer assessments. We present our findings along with recommendations for reference model refinements. We also highlight additional findings of inconsistencies and redundancies within data elements used for nursing documentation and highlight recommendations for improvement.