Common Data Elements Research Articles

Critical Data for Critical Care: A Primer on Leveraging Electronic Health Record Data for Research From Society of Critical Care Medicine’s Panel on Data Sharing and Harmonization

A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research. This article reviews and illustrates six organizing topics (data domains and common data elements; data harmonization; data quality; data interoperability and digital infrastructure; data access, sharing, and governance; and ethics and equity) as a data science primer for critical care researchers, laying a foundation for future publications from the SCCM Discovery Data Harmonization and Sharing Guiding Principles Panel.

Harmonizing data across the accelerating colorectal cancer screening and follow-up through implementation science (ACCSIS) program to enhance data quality and promote data sharing

BackgroundThe purpose of the Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) Program, a Cancer Moonshot℠ Initiative, is to support research to build the evidence base on multilevel interventions that increase rates of colorectal cancer screening, follow-up, and referral to care to address disparities in colorectal cancer screening. The National Cancer Institute funded eight Research Projects to implement multilevel interventions to improve colorectal cancer screening among communities who traditionally have been medically underserved. To analyze the impact of ACCSIS across Research Projects, the consortium developed a set of common data elements. The purpose of this paper is to describe the process of developing the common data elements to facilitate analysis of ACCSIS data as well as support and inform implementation science research studies.MethodsThe ACCSIS Data, Design, and Analysis Work Group was tasked with designing common data elements through a review of existing data collection instruments, examination of data elements proposed by the ACCSIS Research Projects, and deliberations on the data required to compare across the Research Projects. ACCSIS Consortium members drafted, revised, and finalized a common data elements document consisting of variables to collect and surveys to administer to evaluate ACCSIS implementation activities and outcomes in a standardized manner across the Research Projects.ResultsThe ACCSIS Consortium decided to collect the following categories of common data elements: characteristics across multiple levels of the multicomponent interventions, implementation climate, and determinants; interventions and strategies; implementation outcome constructs and definitions; colorectal cancer screening episodes (screening, diagnostic testing follow-up, cancer detection, and cancer treatment); and cost measures. To assess implementation climate, the consortium prioritized constructs from five domains of the Consolidated Framework for Implementation Research.ConclusionsThe ACCSIS common data elements offer a set of harmonization data for future implementation efforts. The consortium is conducting a systematic assessment using both quantitative and qualitative approaches to assess data quality and approaches to improve and sustain data collection. Lessons learned from these ongoing activities will offer additional insights to tailor the ACCSIS common data elements and support efforts to increase colorectal cancer screening for populations experiencing disparities.

The NIH Pain Common Data Elements: A Great Start but a Long Way to the Finish Line.

The NIH Pain Common Data Elements (CDEs) provide a standardized framework for pain research, but their implementation and interpretation present challenges. To review the NIH CDE Program's selected pain domains, provide best practices for implementing required questions, and offer a checklist for appropriate CDE use in clinical trials and secondary data analysis. This work analyzed the ten core pain research domains selected by the NIH CDE Program and discuss their limitations and considerations for use. The manuscript provides an overview of the ten core pain research domains, including pain intensity, interference, physical function, sleep, catastrophizing, depression, anxiety, global impression of change, substance use screening, and quality of life. It offers sample scenarios for implementing required questions and presents a checklist to guide researchers in using pain CDEs effectively for clinical trials and secondary data analysis. Key challenges identified include contextual variability, lack of validation across all pain conditions and populations, and potential misuse or misinterpretation of measures. This work proposes solutions such as supplementary measures, context-specific guidance, comprehensive training programs, and ongoing refinement of the CDE framework. While NIH Pain CDEs are valuable tools for standardizing pain assessment in research, addressing challenges in their implementation and interpretation is crucial for improving the consistency, validity, and interpretability of pain research data, ultimately advancing the field and enhancing patient care.

Enhancing data standards to advance translation in spinal cord injury

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2015 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been advanced. Implementation of these frameworks require multi-modal data and a shift towards open science and principles such as requiring data to be FAIR (Findable, Accessible, Interoperable and Reusable). Advanced analytics such as artificial intelligence require data to be interoperable so data can be exchanged among different technology systems and software applications. The TBI field has multiple ongoing initiatives to promote sharing and data reuse for both pre-clinical and clinical studies, which is an opportunity for the SCI field given these injuries can often occur concomitantly. The adoption of interoperable standards, data sharing, open science, and the use of advanced analytics in SCI is needed to facilitate translation in research and care. It is critical that people with lived experience are engaged to ensure data are relevant and enhances quality of life.

Validation of the Portuguese MDS-UPDRS: Challenges to Obtain a Scale Applicable to Different Linguistic Cultures.

The MDS-UPDRS has been available in English since 2008, showing satisfactory clinimetric results and being proposed as the new official benchmark scale for Parkinson's disease (PD), being cited as a core instrument for PD in the National Institutes of Neurological Disorders and Stroke Common Data Elements program. For this reason, the MDS created guidelines for development of MDS-UPDRS official, clinimetrically validated translations. This study presents the formal process used to obtain the officially approved Portuguese version of the MDS-UPDRS. The study consisted of three phases: (1) Independent translation by Portuguese and Brazilian teams followed by a challenging consensus process that this article particularly emphasizes; (2) Cognitive pretest involving raters and patients from both Portugal and Brazil; (3) Validation test with a sample of 367 native Portuguese-speaking PD patients. The overall factor structure of the Portuguese version was consistent with the English version based on a comparative fit index ≥0.96 for all four parts of the MDS-UPDRS. This version can be designated as the official Portuguese version of the MDS-UPDRS.

A new AI-assisted data standard accelerates interoperability in biomedical research.

In this paper, we leveraged Large Language Models(LLMs) to accelerate data wrangling and automate labor-intensive aspects of data discovery and harmonization. This work promotes interoperability standards and enhances data discovery, facilitating AI-readiness in biomedical science with the generation of Common Data Elements (CDEs) as key to harmonizing multiple datasets. Thirty-one studies, various ontologies, and medical coding systems served as source material to create CDEs from which available metadata and context was sent as an API request to 4th-generation OpenAI GPT models to populate each metadata field. A human-in-the-loop (HITL) approach was used to assess quality and accuracy of the generated CDEs. To regulate CDE generation, we employed ElasticSearch and HITL to avoid duplicate CDEs and instead, added them as potential aliases for existing CDEs. The generated CDEs are foundational to assess the interoperability potential of datasets by determining how many data set column headers can be correctly mapped to CDEs as well as quantifying compliance with permissible values and data types. Subject matter experts reviewed generated CDEs and determined that 94.0% of generated metadata fields did not require manual revisions. Data tables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Global Parkinson's Genetic Program (GP2) were used as test cases for interoperability assessments. Column headers from all test cases were successfully mapped to generated CDEs at a rate of 32.4% via elastic search.The interoperability score, a metric for dataset compatibility to CDEs and other connected datasets, based on relevant criteria such as data field completeness and compliance with common harmonization standards averaged 53.8 out of 100 for test cases. With this project, we aim to automate the most tedious aspects of data harmonization, enhancing efficiency and scalability in biomedical research while decreasing activation energy for federated research.

Development of a Uniform Apheresis Case Report Form forStandardized Collection of Apheresis Data.

Apheresis is performed worldwide for an increasing number of indications. The development of common data elements (CDE) for apheresis related areas may facilitate conduct of new research, enhance quality initiatives including benchmarking, and improve patient care. This report describes the systematic development of the Uniform Apheresis Case Report Form (UACRF) as part of the Apheresis in the United States (ApheresUS) program. A consensus panel of 17 diverse experts in apheresis, related specialties, and electronic case report form (eCRF), and database development was assembled. The panel met via online conferencing from November 17, 2020 to December 1, 2021. A draft document was posted online for public comment from October 11, 2021 to November 10, 2021. Feedback was collected using an online survey tool. The consensus panel revised the UACRF. This version was converted to an eCRF with additional changes made to improve usability in this format. The final version of the UACRF was created on August 24, 2023. The UACRF contains 16 modules: procedure and subject eligibility, patient demographics, general procedure information, laboratory parameters, vascular access, common procedure elements, eight procedure specific modules (mononuclear cell collection and seven therapeutic modalities), outcomes, and site information. A total of 137 data elements were created, including 57 with one or more subelements. The UACRF is the first systematic attempt to develop CDE for therapeutic apheresis and white blood cell collections. Further validation of the UACRF is necessary to confirm the tool's ability to collect the relevant data elements and determine the usability of the form.

Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data.

The 2024 Mary Tyler Moore Vision Initiative (MTM Vision) Workshop on Data convened to discuss best practices and specific considerations for building a comprehensive, shareable MTM Vision data lake. The workshop aimed to accelerate the development of new indications, therapies, and regulatory pathways for diabetic retinal disease (DRD) by standardizing and harmonizing clinical data and ocular 'omics analyses. Standardization of data collection, the use of common data elements, and data interoperability were emphasized, alongside federated learning approaches to promote data sharing and collaboration while maintaining data privacy and security. The integration of molecular data with other multimodal data types was recognized as a promising strategy for leveraging machine learning and AI approaches to advancing therapeutics development and improving treatment outcomes for DRD patients. Partnerships with entities such as the National Eye Institute, part of the National Institutes of Health, foundations, and industry were deemed vital for the successful implementation of these initiatives.

Correction: Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Outcomes and Endpoints

Correction: Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Outcomes and Endpoints

Methodology used to develop the minimum common data elements for surveillance and Reporting of Musculoskeletal Injuries in the MILitary (ROMMIL) statement

Background The objective was to summarize the methodology used to reach consensus for recommended minimum data elements that should be collected and reported when conducting injury surveillance research in military settings. This paper summarizes the methodology used to develop the international Minimum Data Elements for surveillance and Reporting of Musculoskeletal Injuries in the MILitary (ROMMIL) statement. Methods A Delphi methodology was employed to reach consensus for minimum reporting elements. Preliminary steps included conducting a literature review and surveying a convenience sample of military stakeholders to 1) identify barriers and facilitators of military musculoskeletal injury (MSKI) prevention programs, 2) identify relevant knowledge/information gaps and 3) establish future research priorities. The team then led a sequential three-round Delphi consensus survey, including relevant stakeholders from militaries around the world, and then conducted asynchronous mixed knowledge user meeting to explore level of agreement among subject matter experts. Knowledge users, including former and current military service members, civil servant practitioners, and global-wide subject matter experts having experience with policy, execution, or clinical investigation of MSKI mitigation programs, MSKI diagnoses, and MSKI risk factors in military settings. For each round, participants scored each question on a Likert scale of 1-5. Scores ranged from No Importance (1) to Strong Importance (5). Results Literature review and surveys helped informed the scope of potential variables to vote on. Three rounds were necessary to reach minimum consensus. Ninety-five, 65 and 42 respondents participated in the first, second and third rounds of the Delphi consensus, respectively. Ultimately, consensus recommendations emerged consisting of one data principle and 33 minimum data elements. Conclusions Achieving consensus across relevant stakeholders representing military organizations globally can be challenging. This paper details the methodology employed to reach consensus for a core minimum data elements checklist for conducting MSKI research in military settings and improve data harmonization and scalability efforts. These methods can be used as a resource to assist in future consensus endeavors of similar nature.

Construction of circRNA-mediated ceRNA network and immunoassay for investigating pathogenesis of COPD.

The chronic respiratory condition known as chronic obstructive pulmonary disease (COPD) was one of the main causes of death and disability worldwide. This study aimed to explore and elucidate new targets and molecular mechanisms of COPD by constructing competitive endogenous RNA (ceRNA) networks. GSE38974 and GSE106986 were used to select DEGs in COPD samples and normal samples. Cytoscape software was used to construct and present protein-protein interaction (PPI) network, mRNA-miRNA co-expression network and ceRNA network. The CIBERSORT algorithm and the Lasso model were used to screen the immune infiltrating cells and hub genes associated with COPD, and the correlation between them was analyzed. COPD cell models were constructed in vitro and the expression level of ceRNA network factors mediated by hub gene was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In this study, 852 differentially expressed genes were screened in the GSE38974 dataset, including 439 upregulated genes and 413 downregulated genes. Gene clustering analysis of PPI network results was performed using the Minimum Common Tumor Data Element (MCODE) in Cytoscape, and seven hub genes were screened using five algorithms in cytoHubba. CCL20 was verified as an important hub gene based on mRNA-miRNA co-expression network, GSE106986 database validation and the analysis of ROC curve results. Finally, we successfully constructed the circDTL-hsa-miR-330-3p-CCL20 network by Cytoscape. Immune infiltration analysis suggested that CCL20 can co-regulate immune cell migration and infiltration through chemokines CCL7 and CXCL3. In vitro experiments, the expression of circDTL and CCL20 was increased, while the expression of hsa-miR-330-3p was decreased in the COPD cell model. By constructing the circDTL-hsa-miR-330-3p-CCL20 network, this study contributes to a better understanding of the molecular mechanism of COPD development, which also provides important clues for the development of new therapeutic strategies and drug targets.

Addressing the Evidence Gap in Aneurysmal Subarachnoid Hemorrhage: The Need for a Pragmatic Randomized Trial Platform.

Aneurysmal subarachnoid hemorrhage (aSAH) occurs less often than other stroke types but affects younger patients, imposing a disproportionately high burden of long-term disability. Although management advances have improved outcomes over time, relatively few aSAH treatments have been tested in randomized clinical trials (RCTs). One lesson learned from COVID-19 is that trial platforms can facilitate the efficient execution of multicenter RCTs even in complex diseases during challenging conditions. An aSAH trial platform with standardized eligibility criteria, randomization procedures, and end point definitions would enable the study of multiple targeted interventions in a perpetual manner, with treatments entering and leaving the platform based on predefined decision algorithms. An umbrella institutional review board protocol and clinical trial agreement would allow individual arms to be efficiently added as amendments rather than stand-alone protocols. Standardized case report forms using the National Institutes of Health/National Institute of Neurological Disorders and Stroke common data elements and general protocol standardization across arms would create synergies for data management and monitoring. A Bayesian analysis framework would emphasize frequent interim looks to enable early termination of trial arms for futility, common controls, borrowing of information across arms, and adaptive designs. A protocol development committee would assist investigators and encourage pragmatic designs to maximize generalizability, reduce site burden, and execute trials efficiently and cost-effectively. Despite decades of steady clinical progress in the management of aSAH, poor patient outcomes remain common, and despite the increasing availability of RCT data in other fields, it remains difficult to perform RCTs to guide more effective care for aSAH. The development of a platform for pragmatic RCTs in aSAH would help close the evidence gap between aSAH and other stroke types and improve outcomes for this important disease with its disproportionate public health burden.

Common core variables for childhood cancer data integration

IntroductionData-driven research has improved paediatric cancer outcomes for children. However, challenges in sharing data between institutions prevent the use of artificial intelligence (AI) to address substantial unmet needs in children diagnosed with cancer. Harmonising collected data can enable the application of AI for a greater understanding of paediatric cancers. The main goal of the paper was to analyse the currently used childhood cancer databases to identify a core of variables able to capture the most relevant data on the diagnosis and treatment of children and adolescents with cancer. MethodsWe arbitrarily identified different types of existing databases dedicated to collecting data of patients with solid tumours, Umbrella, FAR-RMS; PARTNER; ERN PAEDCAN Registry; INSTRUCT and INRG; the common data elements for Rare Disease by Joint Research Centre. The different elements of the CRFs were analysed and ranked “essential” and “good to have”. Domains that included a group of variables structurally connected were identified. Each variable was defined by name, data type, description, and permissible values. ResultsWe identified six structural domains: Patient registration, Personal information, Disease History, Diagnosis, Treatment, and Follow-up and Events. For each of them, “essential” and “good to have” variables were defined. DiscussionData harmonisation is essential for enhancing integration and comparability in research. By standardizing data formats and variables, researchers can facilitate data sharing, collaboration, and analysis across multiple studies and datasets. Embracing data harmonization practices will advance application of AI, scientific knowledge, improve research reproducibility, and contribute to evidence-based decision-making in various fields.

Feasibility of structuring electronic health record data to facilitate real-world data research: ICAREdata methods applied to multicenter cancer clinical trials.

The use of electronic health record (EHR) data for research is limited by a lack of structure and a standard data model. The objective of the ICAREdata (Integrating Clinical Trials and Real-World Endpoints Data) project was to structure key research data elements in EHRs using a minimal Common Oncology Data Elements (mCODE) data model to extract and transmit data. The ICAREdata project captured two EHR data elements essential to clinical trials: cancer disease status and treatment plan change. The project was implemented in clinical sites participating in Alliance for Clinical Trials in Oncology trials. Data were extracted from EHRs and sent by secure Fast Healthcare Interoperability Resource messaging (a standard for exchanging EHRs) to a database. Selected elements were compared with corresponding data from the trial's electronic data capture (EDC) system, Medidata Rave. By December 2023, data were extracted and transmitted from 10 sites for 35 patients, involving 367 clinical encounters across 15 clinical trials. Data through March 2023 demonstrated that concordance for the elements treatment plan change and cancer disease status was 79% and 34%, respectively. When disease evaluation was reported by both EHR and EDC (n=15), there was 87% agreement on cancer disease status. Documentation, extraction, and aggregation of structured data elements in EHRs using mCODE and ICAREdata methods is feasible in multi-institutional cancer clinical trials. EDC as a reference data setallowed assessment of the completeness of EHR data capture. Future initiatives will focus on elements with shared definitions in clinical and research environments and efficient workflows. Clinical trials use electronic case report forms to report data, and data must be manually entered on these forms, which is costly and time consuming. ICAREdata methods use structured, organized data from clinical trials that can be more easily shared instead having to enter free text into electronic health records.

Clinical and biobehavioral phenotypic assessments and data harmonization for the RE-JOIN research consortium: Recommendations for common data element selection

BackgroundThe Restoring Joint Health and Function to Reduce Pain (RE-JOIN) Consortium is part of the Helping to End Addiction Long-term® (HEAL) Initiative. HEAL is an ambitious, NIH-wide initiative to speed scientific solutions to stem the national opioid public health crisis. The RE-JOIN consortium’s over-arching goal is to define how chronic joint pain-mediating neurons innervate different articular and peri-articular tissues, with a focus on the knee and temporomandibular joints (TMJ) across species employing the latest neuroscience approaches. The aim of this manuscript is to elucidate the human data gathered by the RE-JOIN consortium, as well as to expound upon its underlying rationale and the methodologies and protocols for harmonization and standardization that have been instituted by the RE-JOIN Consortium. MethodsThe consortium-wide human models working subgroup established the RE-JOIN minimal harmonized data elements that will be collected across all human studies and set the stage to develop parallel pre-clinical data collection standards. Data harmonization considerations included requirements from the HEAL program and recommendations from the consortium’s researchers and experts on informatics, knowledge management, and data curation. ResultsMultidisciplinary experts − including preclinical and clinical researchers, with both clinician-scientists- developed the RE-JOIN’s Minimal Human Data Standard with required domains and outcome measures to be collected across projects and institutions. The RE-JOIN minimal data standard will include HEAL Common Data Elements (CDEs) (e.g., standardized demographics, general pain, psychosocial and functional measures), and RE-JOIN common data elements (R-CDE) (i.e., both general and joint-specific standardized and clinically important self-reported pain and function measures, as well as pressure pain thresholds part of quantitative sensory testing). In addition, discretionary, site-specific measures will be collected by individual institutions (e.g., expanded quantitative sensory testing and gait biomechanical assessments), specific to the knee or TMJ. Research teams will submit datasets of standardized metadata to the RE-JOIN Data Coordinating Center (DCG) via a secure cloud-based central data repository and computing infrastructure for researchers to share and conduct analyses on data collected by or acquired for RE-JOIN. RE-JOIN datasets will have protected health information (PHI) removed and be publicly available on the SPARC portal and accessible through the HEAL Data Ecosystem. ConclusionData Harmonization efforts provide the multidisciplinary consortium with an opportunity to effectively collaborate across decentralized research teams, and data standardization sets the framework for efficient future analyses of RE-JOIN data collected by the consortium. The harmonized phenotypic information obtained will significantly enhance our understanding of the neurobiology of the pain-pathology relationships in humans, providing valuable insights for comparison with pre-clinical models.

A Randomized Trial Evaluating Acceptance and Commitment Therapy and Smart Phone Care Management Application to Augment Buprenorphine Therapy for Opioid Use and Chronic Pain.

There is high comorbidity of opioid use disorder (OUD) and chronic pain (CP), which is often addressed by prescribing buprenorphine (BUP). While BUP is effective in preventing overdose, it does not address the psychological aspects of OUD and CP comorbidity and treatment retention rates are as low as 50%. The Virtual Opioid use disorder Integrated Chronic Pain Treatment (VOICE) study (NCT05039554) is a novel effectiveness-implementation trial to test a 12-week virtual group Acceptance and Commitment Therapy (ACT) protocol and a care management smartphone application (app; Valera Health) on pain and opioid use in patients with OUD and CP receiving BUP. Using a 2 × 2 factorial design, participants (expected N = 280) are randomized into: ACT, Valera app, ACT + Valera, or Treatment as Usual arm. This study is taking place in the Bronx, NY, a racially/ethnically diverse community that faces numerous socioeconomic stressors and is one of the nation's epicenters of the opioid epidemic. We created a culturally responsive ACT group protocol, and Valera psychoeducational material. Outcome measures include NIH HEAL Common Data Elements and ACT and Valera-specific measures. We are conducting a novel 2 × 2 trial investigating augmenting BUP treatment with ACT and Valera, with the goal that improved mental health and access to care will result in decreased and opioid use and pain interference.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Outcomes and Endpoints.

Clinical management of persons with disorders of consciousness (DoC) is dedicated largely to optimizing recovery. However, selecting a measure to evaluate the extent of recovery is challenging because few measures are designed to precisely assess the full range of potential outcomes, from prolonged DoC to return of preinjury functioning. Measures that are designed specifically to assess persons with DoC are often performance-based and only validated for in-person use. Moreover, there are no published recommendations addressing which outcome measures should be used to evaluate DoC recovery. The resulting inconsistency in the measures selected by individual investigators to assess outcome prevents comparison of results across DoC studies. The National Institute of Neurological Disorders and Stroke (NINDS) common data elements (CDEs) is an amalgamation of standardized variables and tools that are recommended for use in studies of neurologic diseases and injuries. The Neurocritical Care Society Curing Coma Campaign launched an initiative to develop CDEs specifically for DoC and invited our group to recommend CDE outcomes and endpoints for persons with DoCs. The Curing Coma Campaign Outcomes and Endpoints CDE Workgroup, consisting of experts in adult and pediatric neurocritical care, neurology, and neuroscience, used a previously established five-step process to identify and select candidate CDEs: (1) review of existing NINDS CDEs, (2) nomination and systematic vetting of new CDEs, (3) CDE classification, (4) iterative review and approval of panel recommendations, and (5) development of case report forms. Among hundreds of existing NINDS outcome and endpoint CDE measures, we identified 20 for adults and 18 for children that can be used to assess the full range of recovery from coma. We also proposed 14 new outcome and endpointCDE measures for adults and 5 for children. The DoC outcome and endpoint CDEs are a starting point in the broader effort to standardize outcome evaluation of persons with DoC. The ultimate goal is to harmonize DoC studies and allow for more precise assessment of outcomes after severe brain injury or illness. An iterative approach is required to modify and adjust these outcome and endpoint CDEs as new evidence emerges.

Exercise and nutrition to improve cancer Treatment-Related outcomes (ENICTO).

Chemotherapy treatment-related side-effects are common and increase the risk of suboptimal outcomes. Exercise interventions during cancer treatment improve self-reported physical functioning, fatigue, anxiety, and depression, but it is unclear whether these interventions improve important clinical outcomes, such as chemotherapy relative dose intensity (RDI). The National Cancer Institute funded the Exercise and Nutrition to Improve Cancer Treatment-Related Outcomes (ENICTO) Consortium, to address this knowledge gap. This paper describes the mechanisms hypothesized to underpin intervention effects on clinically-relevant treatment outcomes, briefly outlines each project's distinct research aims, summarizes the scope and organizational structure of ENICTO, and provides an overview of the integrated common data elements used to pursue research questions collectively. In addition, the paper includes a description of consortium-wide activities and broader research community opportunities for collaborative research. Findings from the ENICTO Consortium have the potential to accelerate a paradigm shift in oncology care such that cancer patients could receive exercise and nutrition programming as the standard of care in tandem with chemotherapy to improve RDI for a curative outcome.

Moving Towards a Medicine of Dance: A Scoping Review of Characteristics of Dance Interventions Targeting Older Adults and a Theoretical Framework.

Dance combines cultural and aesthetic elements with behaviors important for brain health, including physical activity, social engagement, and cognitive challenge. Therefore, dance could positively impact public health given the rapidly aging population, increasing incidence of Alzheimer's disease and related dementias, and lack of uptake of exercise in many older adults. Despite a high volume of literature, existing literature does not support evidence-based guidelines for dance to support healthy aging. To conduct a scoping review of the dance intervention literature in older adults and provide information to facilitate a more consistent approach among scientists in designing dance interventions for older adults that stimulate physical and neurocognitive health adaptations. Study characteristics (sample size, population, study design, outcomes, intervention details) were ascertained from 112 separate studies of dance reported in 127 papers that reported outcomes important for brain health (cardiorespiratory fitness, balance and mobility, cognition, mood, and quality of life). High heterogeneity across studies was evident. Class frequency ranged from < 1 to 5 classes per week, class length from 30-120 minutes, and intervention duration from 2 weeks to 18 months. Studies often did not randomize participants, had small (< 30) sample sizes, and used varied comparator conditions. Over 50 tests of cognition, 40 dance forms, and 30 tests of mobility were identified. Based on these results, important future directions are establishing common data elements, developing intervention mapping and mechanistic modeling, and testing dosing parameters to strengthen and focus trial design of future studies and generate evidence-based guidelines for dance.

Discordant Prenatal Cell-Free DNA Screening vs. Diagnostic Results of Sex Chromosome Aneuploidies: Implications for Newborn Screening and Genetic Counseling.

Sex chromosome aneuploidies (SCAs) collectively occur in 1 in 500 livebirths, and diagnoses in the neonatal period are increasing with advancements in prenatal and early genetic testing. Inevitably, SCA will be identified on either routine prenatal or newborn screening in the near future. Tetrasomy SCAs are rare, manifesting more significant phenotypes compared to trisomies. Prenatal cell-free DNA (cfDNA) screening has been demonstrated to have relatively poor positive predictive values (PPV) in SCAs, directing genetic counseling discussions towards false-positive likelihood rather than thoroughly addressing all possible outcomes and phenotypes, respectively. The eXtraordinarY Babies study is a natural history study of children prenatally identified with SCAs, and it developed a longitudinal data resource and common data elements with the Newborn Screening Translational Research Network (NBSTRN). A review of cfDNA and diagnostic reports from participants identified a higher than anticipated rate of discordance. The aims of this project are to (1) compare our findings to outcomes from a regional clinical cytogenetic laboratory and (2) describe discordant outcomes from both samples. Twenty-one (10%), and seven (8.3%) cases were found to be discordant between cfDNA (result or indication reported to lab) and diagnosis for the Babies Study and regional laboratory, respectively. Discordant results represented six distinct discordance categories when comparing cfDNA to diagnostic results, with the largest groups being Trisomy cfDNA vs. Tetrasomy diagnosis (66.7% of discordance in eXtraordinarY Babies study) and Mosaicism (57.1% in regional laboratory). Traditional genetic counseling for SCA-related cfDNA results is inadequate given a high degree of discordance that jeopardizes the accuracy of the information discussed and informed decision making following prenatal genetic counseling.