Linked Exposures Across Databases: an exposure common data elements aggregation framework to facilitate clinical exposure review.

Understanding the health outcomes of military exposures is of critical importance for Veterans, their health care team, and national leaders. Approximately 43% of Veterans report military exposure concerns to their VA providers. Understanding the causal influences of environmental exposures on health is a complex exposure science task and often requires interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposures from different exposure databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database, and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping.

BACKGROUND: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. OBJECTIVE: This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials. METHODS: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs. RESULTS: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date. CONCLUSIONS: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

Standardizing imaging findings representation: harnessing Common Data Elements semantics and Fast Healthcare Interoperability Resources structures.

OBJECTIVES: Designing a framework representing radiology results in a standards-based data structure using joint Radiological Society of North America/American College of Radiology Common Data Elements (CDEs) as the semantic labels on standard structures. This allows radiologist-created report data to integrate with artificial intelligence-generated results for use throughout downstream systems. MATERIALS AND METHODS: We developed a framework modeling radiology findings as Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) observations using CDE set/element identifiers as standardized semantic labels. This framework deploys CDE identifiers to specify radiology findings and attributes, providing consistent labels for radiology report concepts-diagnoses, recommendations, tabular/quantitative data-with built-in integration with RadLex, SNOMED CT, LOINC, and other ontologies. Observation structures fit within larger HL7 FHIR DiagnosticReport resources, providing output including both nuanced text and structured data. RESULTS: Labeling radiology findings as discrete data for interchange between systems requires two components: structure and semantics. CDE definitions provide semantic identifiers for findings and their component values. The FHIR observation resource specifies a structure for associating identifiers with radiology findings in the context of reports, with CDE-encoded observations referring to definitions for CDE identifiers in a central repository. The discussion includes an example of encoding pulmonary nodules on a chest CT as CDE-labeled observations, demonstrating the application of this framework to exchange findings throughout the imaging workflow, making imaging data available to downstream clinical systems. DISCUSSION: CDE-labeled observations establish a lingua franca for encoding, exchanging, and consuming radiology data at the level of individual findings, facilitating use throughout healthcare systems. IMPORTANCE: CDE-labeled FHIR observation objects can increase the value of radiology results by facilitating their use throughout patient care.

Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.

PURPOSE: The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer. METHODS: Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care. RESULTS: We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures. CONCLUSION: This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

Common Data Elements Repository.

Created by the NIH in 2015, the Common Data Elements (CDE) Repository provides free online access to search and use Common Data Elements. This tool helps to ensure consistent data collection, saves time and resources, and ultimately improves the accuracy of and interoperability among datasets. The purpose of this column is to provide an overview of the database, discuss why it is important for researchers and relevant for health sciences librarians, and review the basic layout of the website, including sample searches that will demonstrate how it can be used.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.