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.

Self-management interventions in systemic sclerosis: A systematic review.

Systemic sclerosis is a rare and incurable autoimmune disease in which patients are challenged with self-managing their disease. Systematic evaluation of the essential self-management intervention components and self-management outcomes is necessary to assess the state of the science of self-management for patients with systemic sclerosis. As such, the purpose of this systematic review was to identify and describe self-management interventions and their impact on self-management outcomes in adults with systemic sclerosis. Studies were included if they contained a self-management intervention, incorporated at least one self-management outcome identified as a common data element by the National Institute of Nursing Research (i.e., patient activation, self-efficacy, self-regulation, global health), and were conducted in adults with systemic sclerosis. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched from the inception of each database to March 2020, yielding a total of 215 studies. Of these reports, 16 met the inclusion criteria for this review. There was notable variability in the types of self-management interventions and their effects on key self-management outcomes. Self-management interventions focused on improving physical function through rehabilitation programs were the most common interventions (n = 7). The poor methodological quality of the studies included in this systematic review, however, limited the synthesis of and recommendations for self-management interventions in adults with systemic sclerosis. Future research in systemic sclerosis should include key self-management outcomes in larger, more rigorously designed studies to allow for comparisons across studies and to advance the science of self-management.

Transforming Research and Clinical Knowledge in Spinal Cord Injury (TRACK-SCI): an overview of initial enrollment and demographics.

OBJECTIVE: Traumatic spinal cord injury (SCI) is a dreaded condition that can lead to paralysis and severe disability. With few treatment options available for patients who have suffered from SCI, it is important to develop prospective databases to standardize data collection in order to develop new therapeutic approaches and guidelines. Here, the authors present an overview of their multicenter, prospective, observational patient registry, Transforming Research and Clinical Knowledge in SCI (TRACK-SCI). METHODS: Data were collected using the National Institute of Neurological Disorders and Stroke (NINDS) common data elements (CDEs). Highly granular clinical information, in addition to standardized imaging, biospecimen, and follow-up data, were included in the registry. Surgical approaches were determined by the surgeon treating each patient; however, they were carefully documented and compared within and across study sites. Follow-up visits were scheduled for 6 and 12 months after injury. RESULTS: One hundred sixty patients were enrolled in the TRACK-SCI study. In this overview, basic clinical, imaging, neurological severity, and follow-up data on these patients are presented. Overall, 78.8% of the patients were determined to be surgical candidates and underwent spinal decompression and/or stabilization. Follow-up rates to date at 6 and 12 months are 45% and 36.3%, respectively. Overall resources required for clinical research coordination are also discussed. CONCLUSIONS: The authors established the feasibility of SCI CDE implementation in a multicenter, prospective observational study. Through the application of standardized SCI CDEs and expansion of future multicenter collaborations, they hope to advance SCI research and improve treatment.

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.

Harmonized outcome measures for use in asthma patient registries and clinical practice.

BACKGROUND: Asthma, a common chronic airway disorder, affects an estimated 25 million persons in the United States and 330 million persons worldwide. Although many asthma patient registries exist, the ability to link and compare data across registries is hindered by a lack of harmonization in the outcome measures collected by each registry. OBJECTIVES: The purpose of this project was to develop a minimum set of patient- and provider-relevant standardized outcome measures that could be collected in asthma patient registries and clinical practice. METHODS: Asthma registries were identified through multiple sources and invited to join the workgroup and submit outcome measures. Additional measures were identified through literature searches and reviews of quality measures and consensus statements. Outcome measures were categorized by using the Agency for Healthcare Research and Quality's supported Outcome Measures Framework. A minimum set of broadly relevant measures was identified. Measure definitions were harmonized through in-person and virtual meetings. RESULTS: Forty-six outcome measures, including those identified from 13 registries, were curated and harmonized into a minimum set of 21 measures in the Outcome Measures Framework categories of survival, clinical response, events of interest, patient-reported outcomes, resource utilization, and experience of care. The harmonized definitions build on existing consensus statements and are appropriate for adult and pediatric patients. CONCLUSIONS: The harmonized measures represent a minimum set of outcomes that are relevant in asthma research and clinical practice. Routine and consistent collection of these measures in registries and other systems would support creation of a national research infrastructure to efficiently address new questions and improve patient management and outcomes.

Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics.

BACKGROUND: The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents. METHODS: CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO. RESULTS: We created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice. CONCLUSIONS: CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.

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.

In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury-Lessons Learned.

We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro (p < 0.05) and (d) target engagement of Nrf2 target genes (p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore (p < 0.05). Weight loss (p < 0.05) and prolonged upregulation of plasma cytokines (p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

Successful harmonization in EpiBioS4Rx biomarker study on post-traumatic epilepsy paves the way towards powered preclinical multicenter studies.

OBJECTIVE: Project 1 of the Preclinical Multicenter Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) consortium aims to identify preclinical biomarkers for antiepileptogenic therapies following traumatic brain injury (TBI). The international participating centers in Finland, Australia, and the United States have made a concerted effort to ensure protocol harmonization. Here, we evaluate the success of harmonization process by assessing the timing, coverage, and performance between the study sites. METHOD: We collected data on animal housing conditions, lateral fluid-percussion injury model production, postoperative care, mortality, post-TBI physiological monitoring, timing of blood sampling and quality, MR imaging timing and protocols, and duration of video-electroencephalography (EEG) follow-up using common data elements. Learning effect in harmonization was assessed by comparing procedural accuracy between the early and late stages of the project. RESULTS: The animal housing conditions were comparable between the study sites but the postoperative care procedures varied. Impact pressure, duration of apnea, righting reflex, and acute mortality differed between the study sites (p < 0.001). The severity of TBI on D2 post TBI assessed using the composite neuroscore test was similar between the sites, but recovery of acute somato-motor deficits varied (p < 0.001). A total of 99% of rats included in the final cohort in UEF, 100% in Monash, and 79% in UCLA had blood samples taken at all time points. The timing of sampling differed on day (D)2 (p < 0.05) but not D9 (p > 0.05). Plasma quality was poor in 4% of the samples in UEF, 1% in Monash and 14% in UCLA. More than 97% of the final cohort were MR imaged at all timepoints in all study sites. The timing of imaging did not differ on D2 and D9 (p > 0.05), but varied at D30, 5 months, and ex vivo timepoints (p < 0.001). The percentage of rats that completed the monthly high-density video-EEG follow-up and the duration of video-EEG recording on the 7th post-injury month used for seizure detection for diagnosis of post-traumatic epilepsy differed between the sites (p < 0.001), yet the prevalence of PTE (UEF 21%, Monash 22%, UCLA 23%) was comparable between the sites (p > 0.05). A decrease in acute mortality and increase in plasma quality across time reflected a learning effect in the TBI production and blood sampling protocols. SIGNIFICANCE: Our study is the first demonstration of the feasibility of protocol harmonization for performing powered preclinical multi-center trials for biomarker and therapy discovery of post-traumatic epilepsy.

Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model.

OBJECTIVES: Since protecting patients' privacy is a major concern in clinical research, there has been a growing need for privacy-preserving data analysis platforms. For this purpose, a federated learning (FL) method based on the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) was implemented, and its feasibility was demonstrated. METHODS: We implemented an FL platform on FeederNet, which is a distributed clinical data analysis platform based on the OMOP CDM in Korea. We trained it through an artificial neural network (ANN) using data from patients who received steroid prescriptions or injections, with the aim of predicting the occurrence of side effects depending on the prescribed dose. The ANN was trained using the FL platform with the OMOP CDMs of Kyung Hee University Medical Center (KHMC) and Ajou University Hospital (AUH). RESULTS: The area under the receiver operating characteristic curves (AUROCs) for predicting bone fracture, osteonecrosis, and osteoporosis using only data from each hospital were 0.8426, 0.6920, and 0.7727 for KHMC and 0.7891, 0.7049, and 0.7544 for AUH, respectively. In contrast, when using FL, the corresponding AUROCs were 0.8260, 0.7001, and 0.7928 for KHMC and 0.7912, 0.8076, and 0.7441 for AUH, respectively. In particular, FL led to a 14% improvement in performance for osteonecrosis at AUH. CONCLUSIONS: FL can be performed with the OMOP CDM, and FL often shows better performance than using only a single institution's data. Therefore, research using OMOP CDM has been expanded from statistical analysis to machine learning so that researchers can conduct more diverse research.

Preclinical common data elements for general pharmacological studies (pharmacokinetic sample collection, tolerability, and drug administration). A report of the TASK3-WG1A General Pharmacology Working Group of the ILAE/AES Joint Translational Task Force.

Growing concerns over rigor and reproducibility of preclinical studies, including consistency across laboratories and translation to clinical populations, have triggered efforts to harmonize methodologies. This includes the first set of preclinical common data elements (CDEs) for epilepsy research studies, as well as Case Report Forms (CRFs) for widespread use in epilepsy research. The General Pharmacology Working Group of the ILAE/AES Task Force (TASK3-WG1A) has continued in this effort by adapting and refining CDEs/CRFs to address specific study design areas as they relate to preclinical drug screening: general pharmacology, pharmacokinetics (PK) and pharmacodynamics (PD), and tolerability. This work has expanded general pharmacology studies to include dose records, PK/PD, tolerability, and elements of rigor and reproducibility. Tolerability testing CRFs included rotarod and Irwin/Functional Observation Battery (FOB) assays. The material provided in the form of CRFs can be delivered for widespread use within the epilepsy research community.

Toward Shared Decision-Making in Degenerative Cervical Myelopathy: Protocol for a Mixed Methods Study.

BACKGROUND: Health care decisions are a critical determinant in the evolution of chronic illness. In shared decision-making (SDM), patients and clinicians work collaboratively to reach evidence-based health decisions that align with individual circumstances, values, and preferences. This personalized approach to clinical care likely has substantial benefits in the oversight of degenerative cervical myelopathy (DCM), a type of nontraumatic spinal cord injury. Its chronicity, heterogeneous clinical presentation, complex management, and variable disease course engenders an imperative for a patient-centric approach that accounts for each patient's unique needs and priorities. Inadequate patient knowledge about the condition and an incomplete understanding of the critical decision points that arise during the course of care currently hinder the fruitful participation of health care providers and patients in SDM. This study protocol presents the rationale for deploying SDM for DCM and delineates the groundwork required to achieve this. OBJECTIVE: The study's primary outcome is the development of a comprehensive checklist to be implemented upon diagnosis that provides patients with essential information necessary to support their informed decision-making. This is known as a core information set (CIS). The secondary outcome is the creation of a detailed process map that provides a diagrammatic representation of the global care workflows and cognitive processes involved in DCM care. Characterizing the critical decision points along a patient's journey will allow for an effective exploration of SDM tools for routine clinical practice to enhance patient-centered care and improve clinical outcomes. METHODS: Both CISs and process maps are coproduced iteratively through a collaborative process involving the input and consensus of key stakeholders. This will be facilitated by Myelopathy.org, a global DCM charity, through its Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy community. To develop the CIS, a 3-round, web-based Delphi process will be used, starting with a baseline list of information items derived from a recent scoping review of educational materials in DCM, patient interviews, and a qualitative survey of professionals. A priori criteria for achieving consensus are specified. The process map will be developed iteratively using semistructured interviews with patients and professionals and validated by key stakeholders. RESULTS: Recruitment for the Delphi consensus study began in April 2023. The pilot-testing of process map interview participants started simultaneously, with the formulation of an initial baseline map underway. CONCLUSIONS: This protocol marks the first attempt to provide a starting point for investigating SDM in DCM. The primary work centers on developing an educational tool for use in diagnosis to enable enhanced onward decision-making. The wider objective is to aid stakeholders in developing SDM tools by identifying critical decision junctures in DCM care. Through these approaches, we aim to provide an exhaustive launchpad for formulating SDM tools in the wider DCM community. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46809.

Development and implementation of the National Heart, Lung, and Blood Institute COVID-19 common data elements.

Background: Coronavirus Disease 2019 (COVID-19) instigated a flurry of clinical research activity. The unprecedented pace with which trials were launched left an early void in data standardization, limiting the potential for subsequent data pooling. To facilitate data standardization across emerging studies, the National Heart, Lung, and Blood Institute (NHLBI) charged two groups with harmonizing data collection, and these groups collaborated to create a concise set of COVID-19 Common Data Elements (CDEs) for clinical research. Methods: Our iterative approach followed three guiding principles: 1) draw from existing multi-center COVID-19 clinical trials as precedents, 2) incorporate existing data elements and data standards whenever possible, and 3) alignment to data standards that facilitate data sharing and regulatory submission. We also supported rapid implementation of the CDEs in NHLBI-funded studies and iteratively refined the CDEs based on feedback from those study teams. Results: The NHLBI COVID-19 CDEs are publicly available and being used for current COVID-19 clinical trials. CDEs are organized into domains, and each data element is classified within a three-tiered prioritization system. The CDE manual is hosted publicly at https://nhlbi-connects.org/common_data_elements with an accompanying data dictionary and implementation guidance. Conclusions: The NHLBI COVID-19 CDEs are designed to aid data harmonization across studies to achieve the benefits of pooled analyses. We found that organizing CDE development around our three guiding principles focused our efforts and allowed us to adapt as COVID-19 knowledge advanced. As these CDEs continue to evolve, they could be generalized for use in other acute respiratory illnesses.

A scoping review of semantic integration of health data and information.

OBJECTIVE: We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS: We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS: The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION: SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

Developing a Dietary Lifestyle Ontology to Improve the Interoperability of Dietary Data: Proof-of-Concept Study.

BACKGROUND: Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts. OBJECTIVE: The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON. METHODS: By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy. RESULTS: DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results. CONCLUSIONS: Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies.

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

National Institutes of Health Pathways to Prevention Workshop: Physical Activity and Health for Wheelchair Users.

Health benefits of physical activity are well recognized in the general population for reducing the risk of chronic health conditions. Less is known about the effects of physical activity on people currently using or who may use wheeled mobility devices in the future, specifically individuals with multiple sclerosis, cerebral palsy, and spinal cord injury who are at increased likelihood for use of a wheeled mobility device. On December 1-3, 2020, the National Institutes of Health convened the Pathways to Prevention workshop: "Can Physical Activity Improve the Health of Wheelchair Users?" to consider the available scientific evidence on the clinical benefits and harms of physical activity for people currently using or who may use wheeled mobility devices in the future, with the aim of developing recommendations to fill gaps in the evidence base. A multidisciplinary team of content area experts developed the agenda and an evidence-based practice center prepared the evidence report. An independent panel, selected by the National Institutes of Health, attended the workshop; convened to develop recommendations on the basis of the systematic review, presentations, and public comments received during the workshop; and revised recommendations based on public comments received. This final report summarizes the panel's findings and identifies current gaps in knowledge. The panel made recommendations for new research efforts, including novel methods and new research infrastructure to improve the evidence base about the effects of physical activity on people currently using or who may use wheeled mobility devices in the future.

Exploring a Common Data Element for International Research in Long-Term Care Homes: A Measure for Evaluating Nursing Supervisor Effectiveness.

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

Early Increase in Cortical T2 Relaxation Is a Prognostic Biomarker for the Evolution of Severe Cortical Damage, but Not for Epileptogenesis, after Experimental Traumatic Brain Injury.

Prognostic biomarkers for post-injury outcome are necessary for the development of neuroprotective and antiepileptogenic treatments for traumatic brain injury (TBI). We hypothesized that T2 relaxation magnetic resonance imaging (MRI) predicts the progression of perilesional cortical pathology and epileptogenesis. The EPITARGET animal cohort used for MRI analysis included 120 adult male Sprague-Dawley rats with TBI induced by lateral fluid-percussion injury and 24 sham-operated controls. T2 MRI was performed at days 2, 7, and 21 post-TBI. The lesioned cortex was outlined, and the T2 value of each imaging voxel within the lesion area was scored using a five-grade pathology classification. Analysis of 1-month video-electroencephalography recordings initiated 5 months post-TBI indicated that 27% (31 of 114) of the animals with TBI developed epilepsy. Multiple linear regression analysis indicated that T2-based classification of lesion volume at day 2 and day 7 post-TBI explained the necrotic lesion volume with greatly increased T2 (>102 ms) at day 21 post-TBI (F(13,103) = 52.5; p < 0.001; R2 = 0.87; adjusted R2 = 0.85). The volume of moderately increased (78-102 ms) T2 at day 7 post-TBI predicted the evolution of large (>12 mm3) cortical lesions (area under the curve, 0.92; p < 0.001; cutoff, 1.9 mm3; false positive rate, 0.10; true positive rate, 0.62). Logistic regression analysis, however, showed that the different severities of T2 lesion volumes at days 2, 7, and 21 post-TBI did not explain the development of epilepsy (χ2(18,95) = 18.4; p = 0.427). In addition, the location of the T2 abnormality within the cortex did not correlate with epileptogenesis. A single measurement of T2 relaxation MRI in the acute post-TBI phase is useful for identifying post-TBI subjects at highest risk of developing large cortical lesions, and thus, in the greatest need of neuroprotective therapies after TBI, but not the development of post-traumatic epilepsy.

Cerebral Palsy Research Network Clinical Registry: Methodology and Baseline Report.

OBJECTIVE: To apply practice-based evidence to clinical management of cerebral palsy (CP). The process of establishing purpose, structure, logistics, and elements of a multi-institutional registry and the baseline characteristics of initial enrollees are reported. DESIGN: A consensus-building process among consumers, clinicians, and researchers used a participatory action process. SETTING: Community, hospitals, and universities. PARTICIPANTS: More than 100 clinicians, researchers, and consumers and more than 1858 enrollees in the registry. MAIN OUTCOME MEASURES: Not applicable. RESULTS: Consensus was that the purpose of registry was to (1) quantify practice variation, (2) facilitate quality improvement (QI), and (3) perform comparative effectiveness research (CER). Collecting data during routine clinical care using the electronic medical record was determined to be a sustainable plan for data acquisition and management. Clinicians from multiple disciplines defined salient characteristics of individuals and interventions for the registry elements. The registry was central to the clinical research network, and a leadership structure was created. A leading electronic health record platform adopted the registry elements. Twenty-four sites have initiated the data collection process and agreed to export data to the registry. Currently 12 are collecting data. Number of enrollees and characteristics were similar to other population registers. CONCLUSIONS: This is the first multi-institutional CP registry that contains the patient and treatment characteristics needed for QI and CER. The Cerebral Palsy Research Network registry elements are implemented in a versatile electronic platform and minimize burden to clinicians. The resultant registry is available for any institution to participate and is growing rapidly.