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Background Despite increased recognition of frailty and its importance, high quality evidence to guide decision-making is lacking. There has been variation in reported data elements and outcomes which makes it challenging to interpret results across studies as well as to generalize research findings. The creation of a frailty core set, consisting of a minimum set of data elements and outcomes to be measured in all frailty studies, would allow for findings from research and translational studies to be collectively analyzed to better inform care and decision-making. To achieve this, the Frailty Outcomes Consensus Project was developed to reach consensus from the international frailty community on a set of common data elements and core outcomes for frailty. Methods An international steering committee developed the methodology and the consensus process to be followed. The committee formulated the initial list of data elements and outcomes. Participants from across the world were invited to take part in the Delphi consensus process. The Delphi consisted of three rounds. Following review of data after three rounds, a final ranking round of data elements and outcomes was conducted. A required retention rate of 80% between rounds was set a priori. Results One hundred and eighty-four panelists from 25 different countries participated in the first round of the Delphi consensus process. This included researchers, clinicians, administrators, older adults, and caregivers. The retention rate between rounds was achieved. Data elements and outcomes forming primary and secondary core sets were identified, within the domains of participant characteristics, physical performance, physical function, physical health, cognition and mental health, socioenvironmental circumstances, frailty measures, and other. Conclusion It is anticipated that implementation and uptake of the frailty core set will enable studies to be collectively analyzed to better inform care for persons living with frailty and ultimately improve their outcomes. Future work will focus on identification of measurement tools to be used in the application of the frailty core set.

To define clinical common data elements (CDEs) and a mandatory minimum data set (MDS) for genomic studies of cerebral palsy (CP).Candidate data elements were collated following a review of the literature and existing CDEs. An online, three-round Delphi survey was used to rate each data element as either 'core', 'recommended', 'exploratory', or 'not required'. Members of the International Cerebral Palsy Genomics Consortium (ICPGC) rated the core CDEs as either mandatory or not, to form the MDS. For both the CDEs and the MDS, a data element was considered to have reached consensus if more than 75% of respondents agreed.Forty-six individuals from around the world formed the Delphi panel: consumers (n=2), scientists/researchers (n=17), medical (n=19), and allied health professionals (n=8). The CDEs include 107 data elements across six categories: demographics, diagnostics, family history, antenatal and neonatal details, clinical traits, and CP-specific assessments. Of these, 10 are mandatory, 42 core, 41 recommended, and 14 are exploratory.The ICPGC CDEs provide a foundation for the standardization of phenotype data captured in CP genomic studies and will benefit international collaborations and pooling of data, particularly in rare conditions.A set of 107 common data elements (CDEs) for genomics studies in cerebral palsy is provided. The CDEs include standard definitions and data values domains. The CDEs will facilitate international data sharing, collaboration, and improved clinical interpretation of findings.Definir elementos de dados clínicos comuns (DCC) e um conjunto mínimo de dados obrigatórios (CMDO) para estudos genômicos de paralisia cerebral (PC). MÉTODO: Os elementos de dados do candidato foram coletados seguindo uma revisão da literatura e através dos DCC existentes. Uma pesquisa on-line de três rodadas Delphi foi usada para classificar cada elemento de dados como 'essencial', 'recomendado', 'exploratório' ou 'não obrigatório'. Os Membros do Consorcio Internacional de Genoma na Paralisia Cerebral (MCIGPC) classificaram os DCC do núcleo como obrigatórios ou não, para formar o CMDO. Tanto para os DCC quanto para o CMDO, um elemento de dados foi considerado como tendo chegado a um consenso se mais de 75% dos respondentes concordassem.Quarenta e seis indivíduos de todo o mundo formaram o painel Delphi: consumidores (n=2), cientistas/pesquisadores (n=17), médicos (n=19) e profissionais de saúde aliados (n=8). Os DCC incluem 107 elementos de dados em seis categorias: demografia, diagnóstico, história familiar, detalhes pré-natais e neonatais, características clínicas e avaliações específicas de PC. Destes, 10 são obrigatórios, 42 essenciais, 41 recomendados e 14 são exploratórios INTERPRETAÇÃO: Os DCC do MCIGPC fornecem uma base para a padronização de dados de fenótipo capturados em estudos genômicos de PC e beneficiarão colaborações internacionais e agrupamento de dados, particularmente em condições raras.

Background The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. Results Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. Conclusions Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally.

Background The Japan Renal Biopsy Registry (J-RBR), the first nation-wide registry of renal biopsies in Japan, was established in 2007, and expanded to include non-biopsy cases as the Japan Kidney Disease Registry (J-KDR) in 2009. The J-RBR/J-KDR is one of the biggest registries for kidney diseases. It has revealed the prevalence and distribution of kidney diseases in Japan. This registry system was meant to be revised after 10 years. Methods In 2017, the Committees of the Japanese Society of Nephrology started a project for the revision of the J-RBR/J-KDR. The revised system was designed in such a way that the diagnoses of the patients could be selected from the Diagnosis Panel, a list covering almost all known kidney diseases, and focusing on their pathogenesis rather than morphological classification. The Diagnosis Panel consists of 22 categories (18 glomerular, 1 tubulointerstitial, 1 congenital/genetical, 1 transplant related, and 1 other) and includes 123 diagnostic names. The items for clinical diagnosis and laboratory data were also renewed, with the addition of the information on immunosuppressive treatment. Results The revised version of J-RBR/J-KDR came into use in January 2018. The number of cases registered under the revised system was 2748 in the first year. The total number of cases has reached to 43,813 since 2007. Conclusion The revised version 2018 J-RBR/J-KDR system attempts to cover all kidney diseases by focusing on their pathogenesis. It will be a new platform for the standardized registration of kidney biopsy cases that provides more systemized data of higher quality.

Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems.

BACKGROUND/OBJECTIVE: To synchronize data collection, the National Institute of Neurological Disorders and Stroke 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 Scales (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)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. CONCLUSIONS: 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 DD5 y, UPDRS-II for early (DD 5).

Objectives Common data elements are a combination of a precisely defined question paired with a specified set of responses. Common data elements contribute to the National Institutes of Health-supported principle of Findable, Accessible, Interoperable, and Reusableness of research data. Routine use of Common data elements and standardized definitions within pediatric critical care research are likely to promote collaboration, improve quality, and consistency of data collection, improve overall efficiency of study or trial setup, and facilitate cross-study comparisons, meta-analysis, and merging of study cohorts. The purpose of this Pediatric Critical Care Medicine Perspective is to establish a road map for the development of multinational, multidisciplinary consensus-based common data elements that could be adapted for use within any pediatric critical care subject area. Methods We describe a multistep process for the creation of "core domains" of research (e.g. patient outcomes, health-related conditions, or aspects of health) and the development of common data elements within each core domain. We define a tiered approach to data collection based on relevance of each common data element to future studies and clinical practice within the field of interest. Additionally, we describe the use of the Delphi methods to achieve consensus of these common data element documents using an international, multidisciplinary panel of experts.

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.

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.

Objective The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis. Materials and Methods The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit. Following a review of initial CDEs, we made selections and further refinements through an iterative process that included live forums, consultations, and surveys completed by the first 69 RADx-UP projects. Results Following a multistep CDE development process, we decreased the number of CDEs, modified the question types, and changed the CDE wording. Most research projects were willing to collect and share demographic NIH Tier 1 CDEs, with the top exception reason being a lack of CDE applicability to the project. The NIH RADx-UP Tier 1 CDE with the lowest frequency of collection and sharing was sexual orientation. Discussion We engaged a wide range of projects and solicited bidirectional input to create CDEs. These RADx-UP CDEs could serve as the foundation for a patient-centered informatics architecture allowing the integration of disease-specific databases to support hypothesis-driven clinical research in underserved populations. Conclusion A community-engaged approach using bidirectional feedback can lead to the better development and implementation of CDEs in underserved populations during public health emergencies.

The Ontario Brain Institute's “Brain-CODE” is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force initiated the TASK3 working group to create common data elements (CDEs) for various aspects of preclinical epilepsy research studies, which could help improve the standardization of experimental designs. This article addresses neuropathological changes associated with seizures and epilepsy in rodent models of epilepsy. We discuss CDEs for histopathological parameters for neurodegeneration, changes in astrocyte morphology and function, mechanisms of inflammation, and changes in the blood-brain barrier and myelin/oligodendrocytes resulting from recurrent seizures in rats and mice. We provide detailed CDE tables and case report forms (CRFs), and with this companion manuscript, we discuss the rationale and methodological aspects of individual neuropathological examinations. The CDEs, CRFs, and companion paper are available to all researchers, and their use will benefit the harmonization and comparability of translational preclinical epilepsy research. The ultimate hope is to facilitate the development of rational therapy concepts for treating epilepsies, seizures, and comorbidities and the development of biomarkers assessing the pathological state of the disease.

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force initiated the TASK3 working group to create common data elements (CDEs) for various aspects of preclinical epilepsy research studies, which could help improve the standardization of experimental designs. This article addresses neuropathological changes associated with seizures and epilepsy in rodent models of epilepsy. We discuss CDEs for histopathological parameters for neurodegeneration, changes in astrocyte morphology and function, mechanisms of inflammation, and changes in the blood-brain barrier and myelin/oligodendrocytes resulting from recurrent seizures in rats and mice. We provide detailed CDE tables and case report forms (CRFs), and with this companion manuscript, we discuss the rationale and methodological aspects of individual neuropathological examinations. The CDEs, CRFs, and companion paper are available to all researchers, and their use will benefit the harmonization and comparability of translational preclinical epilepsy research. The ultimate hope is to facilitate the development of rational therapy concepts for treating epilepsies, seizures, and comorbidities and the development of biomarkers assessing the pathological state of the disease.

ObjectivesDespite significant advances in the science of quality of care measurement over the last decade, approaches to developing quality of care indicators for global health priorities are not clearly defined. We conducted a scoping review of concepts and methods used to develop quality of healthcare indicators to better inform ongoing efforts towards a more harmonised approach to quality of care indicator development in global health.MethodsWe conducted a systematic search of electronic databases, grey literature and references for articles on developing quality of care indicators for routine monitoring in all healthcare settings and populations, published in English between 2010 and 2020. We used well-established methods for article screening and selection, data extraction and management. Results were summarised using a descriptive analysis and a narrative synthesis.ResultsThe 221 selected articles were largely from high-income settings (89%), particularly the USA (46%), Canada (9%), UK (9%) and Europe (17%). Quality of care indicators were developed mainly for healthcare providers (56%), for benchmarking or quality assurance (37%) and quality improvement (29%), in hospitals (32%) and primary care (26%), across many diseases. The terms ‘quality indicator’ and ‘quality measure’ were the most frequently encountered terms (50% and 21%, respectively). Systematic approaches for quality of care indicator development emerged within national quality of care systems or through cross-country collaborations in high-income settings. Maternal, neonatal and child health (33%), mental health (26%) and primary care (57%) studies applied most components of systematic approaches, but not consistently or rigorously.DiscussionThe current evidence shows variations in concepts and approaches to developing quality of care indicators, with development and application mainly in high-income countries.ConclusionAdditional efforts are needed to propose ‘best-practice’ conceptual frameworks and methods for developing quality of care indicators to improve their utility in global health measurement.

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research.

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. The aim of the CDEs is to improve the standardization of experimental designs across a range of epilepsy research-related methods. Here, we have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for genomics, transcriptomics, and epigenomics in rodent models of epilepsy, with a specific focus on adult rats and mice. We discuss the important elements that need to be considered for genomics, transcriptomics, and epigenomics methodologies, providing a rationale for the parameters that should be collected. This is the first in a two-part series of omics papers with the second installment to cover proteomics, lipidomics, and metabolomics in adult rodents.

Objectives: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness.Methods: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.Results: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact.Conclusions: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata ‘common data elements’ using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR. OBJECTIVES: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness. METHODS: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records. RESULTS: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact. CONCLUSIONS: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.

Strong evidence in support of guidelines for traumatic brain injury (TBI) is lacking. Large-scale observational studies may offer a complementary source of evidence to clinical trials to improve the care and outcome for patients with TBI. They are, however, challenging to execute. In this review, we aim to characterize opportunities and challenges of large-scale collaborative research in neurotrauma. We use the setup and conduct of Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) as an illustrative example. We highlight the importance of building a team and of developing a network for younger researchers, thus investing toward the future. We involved investigators early in the design phase and recognized their efforts in a group contributor list on all publications. We found, however, that translation to academic credits often failed, and we suggest that the current system of academic credits be critically appraised. We found substantial variability in consent procedures for participant enrollment within and between countries. Overall, obtaining approvals typically required 4-6 months, with outliers up to 18 months. Research costs varied considerably across Europe and should be defined by center. We substantially underestimated costs of data curation, and we suggest that 15-20% of the budget be reserved for this purpose. Streamlining analyses and accommodating external research proposals demanded a structured approach. We implemented a systematic inventory of study plans and found this effective in maintaining oversight and in promoting collaboration between research groups. Ensuring good use of the data was a prominent feature in the review of external proposals. Multiple interactions occurred with industrial partners, mainly related to biomarkers and neuroimaging, and resulted in various formal collaborations, substantially extending the scope of CENTER-TBI. Overall, CENTER-TBI has been productive, with over 250 international peer-reviewed publications. We have ensured mechanisms to maintain the infrastructure and continued analyses. We see potential for individual patient data meta-analyses in connection to other large-scale projects. Our collaboration with Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) has taught us that although standardized data collection and coding according to common data elements can facilitate such meta-analyses, further data harmonization is required for meaningful results. Both CENTER-TBI and TRACK-TBI have demonstrated the complexity of the conduct of large-scale collaborative studies that produce high-quality science and new insights.

It is important to measure quality of life (QoL) after traumatic brain injury (TBI), yet limited studies have compared QoL inventories. In 2579 TBI patients, orthopedic trauma controls, and healthy friend control participants, we compared the Quality of Life After Brain Injury-Overall Scale (QOLIBRI-OS), developed for TBI patients, to the Satisfaction with Life Scale (SWLS), an index of generic life satisfaction. We tested the hypothesis that group differences (TBI and orthopedic trauma vs. healthy friend controls) would be larger for the QOLIBRI-OS than the SWLS and that the QOLIBRI-OS would manifest more substantial changes over time in the injured groups, demonstrating more relevance of the QOLIBRI-OS to traumatic injury recovery. (1) We compared the group differences (TBI vs. orthopedic trauma control vs. friend control) in QoL as indexed by the SWLS versus the QOLIBRI-OS and (2) characterized changes across time in these two inventories across 1 year in these three groups. Our secondary objective was to characterize the relationship between TBI severity and QoL. As compared with healthy friend controls, the QOLIBRI reflected greater reductions in QoL than the SWLS for both the TBI group (all time points) and the orthopedic trauma control group (2 weeks and 3 months). The QOLIBRI-OS better captured expected improvements in QoL during the injury recovery course in injured groups than the SWLS, which demonstrated smaller changes over time. TBI severity was not consistently or robustly associated with self-reported QoL. The findings imply that, as compared with the SWLS, the QOLIBRI-OS appears to identify QoL issues more specifically relevant to traumatically injured patients and may be a more appropriate primary QoL outcome measure for research focused on the sequelae of traumatic injuries.

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.

Background Medical information management for stroke patients is currently a very time-consuming endeavor. There are clear guidelines and procedures to treat patients having acute stroke, but it is not known how well these established practices are reflected in patient documentation. Objective This study compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview of the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core data set that could be used in potential data interfaces. Methods Medical source documentation forms from different documentation contexts, including hospitals, clinical trials, registries, and international standards, regarding stroke treatment followed by rehabilitation were digitized in the operational data model. Each source data element was semantically annotated using the Unified Medical Language System. The concept codes were analyzed for semantic overlaps. A concept was considered common if it appeared in at least two documentation contexts. The resulting common concepts were extended with implementation details, including data types and permissible values based on frequent patterns of source data elements, using an established expert-based and semiautomatic approach. Results In total, 3287 data elements were identified, and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 9.51% (100/1051) of all concept occurrences in stroke documentation, and the 50 most frequent concepts cover 4.75% (50/1051). A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable reuse. Conclusions Standardization of medical documentation is a prerequisite for data exchange as well as the transferability and reuse of data. In the long run, standardization would save time and money and extend the capabilities for which such data could be used. In the context of this work, a lack of standardization was observed regarding current information management. Free-form text fields and intricate questions complicate automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core data set of the 50 most frequent common data elements, accounting for 34% of the documentation in medical information management. Such a data set offers a starting point for standardized and interoperable data collection in routine care, quality management, and clinical research.

Key Points Question Are different patterns of intracranial injury on head computed tomography associated with prognosis after mild traumatic brain injury (mTBI)? Findings In this cohort study, subarachnoid hemorrhage, subdural hematoma, and contusion often co-occurred and were associated with both incomplete recovery and more severe impairment out to 12 months after injury, while intraventricular and/or petechial hemorrhage co-occurred and were associated with more severe impairment up to 12 months after injury; epidural hematoma was associated with incomplete recovery at some points but not with more severe impairment. Some intracranial hemorrhage patterns were more strongly associated with outcomes than previously validated demographic and clinical variables. Meaning In this study, different pathological features on head computed tomography carried different implications for mild traumatic brain injury prognosis to 1 year., The longitudinal, observational study aims to identify pathological computed tomography features associated with adverse outcomes after mild traumatic brain injury., Importance A head computed tomography (CT) with positive results for acute intracranial hemorrhage is the gold-standard diagnostic biomarker for acute traumatic brain injury (TBI). In moderate to severe TBI (Glasgow Coma Scale [GCS] scores 3-12), some CT features have been shown to be associated with outcomes. In mild TBI (mTBI; GCS scores 13-15), distribution and co-occurrence of pathological CT features and their prognostic importance are not well understood. Objective To identify pathological CT features associated with adverse outcomes after mTBI. Design, Setting, and Participants The longitudinal, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled patients with TBI, including those 17 years and older with GCS scores of 13 to 15 who presented to emergency departments at 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018, and underwent head CT imaging within 24 hours of TBI. Evaluations of CT imaging used TBI Common Data Elements. Glasgow Outcome Scale–Extended (GOSE) scores were assessed at 2 weeks and 3, 6, and 12 months postinjury. External validation of results was performed via the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Data analyses were completed from February 2020 to February 2021. Exposures Acute nonpenetrating head trauma. Main Outcomes and Measures Frequency, co-occurrence, and clustering of CT features; incomplete recovery (GOSE scores

The main objective of this prospective cohort study was to evaluate whether traumatic microbleeds (TMBs) are a significant prognostic factor of return to work (RTW), post-traumatic symptoms, and overall recovery in patients with mild traumatic brain injury (mTBI). One hundred and thirteen patients with mTBI were recruited from the Helsinki University Hospital emergency units. All patients underwent multi-contrast 3T magnetic resonance imaging (MRI) 3-17 days after mTBI. Patients were evaluated in the Traumatic Brain Injury Outpatient Clinic of Helsinki University Hospital 1 month after injury. Post-concussion symptoms were assessed with the Post-Concussion Symptom Questionnaire (RPQ) and overall recovery was assessed with the Glasgow Outcome Scale Extended (GOS-E). Their time to RTW was continuously measured up to 1 year after TBI. Median RTW was 9 days (interquartile range [IQR] 4-30) after mTBI and full RTW rate after 1 year was 98%. Patients with TMBs (n = 22) did not have more post-concussion symptoms (median RPQ 10.0 vs. 7.0, p = 0.217) or worse overall recovery (58% vs. 56% with GOS-E = 8, p = 0.853) than patients without TMBs (n = 91). There was no significant difference in time to RTW (13.5 vs. 7.0 days, p = 0.063). In this study, patients with TMBs did not have delayed RTW or more post-concussion symptoms than other patients with mTBI. TMBs in mTBI do not seem to be a significant prognostic factor of RTW.