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Introduction: In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives. Methods: The model defines a "data element", the primary focus of the check, and a "function", the qualitative or quantitative measure over a data element. We applied NLP techniques to extract both from 172 checks for Observational Health Data Sciences and Informatics (OHDSI) and 3,434 checks for Kaiser Permanente's Center for Effectiveness and Safety Research (CESR). Results: The model was able to classify all checks. A total of 751 unique data elements and 24 unique functions were extracted. The top five frequent data element-function pairings for OHDSI were Person- Count (55 checks), Insurance-Distribution (17), Medication-Count (16), Condition-Count (14), and Observations-Count (13); for CESR, they were Medication-Variable Type (175), Medication-Missing (172), Medication-Existence (152), Medication-Count (127), and Socioeconomic Factors-Variable Type (114). Conclusions: This study shows the efficacy of the data element-function conceptual model for classifying DQ checks, demonstrates early promise of NLP-assisted knowledge acquisition, and reveals the great heterogeneity in the focus in DQ checks, confirming variation in intrinsic checks and use-case specific "fitness-for-use" checks. [ABSTRACT FROM AUTHOR]

Objective: To compare rule-based data quality (DQ) assessment approaches across multiple national clinical data sharing organizations. Methods: Six organizations with established data quality assessment (DQA) programs provided documentation or source code describing current DQ checks. DQ checks were mapped to the categories within the data verification context of the harmonized DQA terminology. To ensure all DQ checks were consistently mapped, conventions were developed and four iterations of mapping performed. Difficult-to-map DQ checks were discussed with research team members until consensus was achieved. Results: Participating organizations provided 11,026 DQ checks, of which 99.97 percent were successfully mapped to a DQA category. Of the mapped DQ checks (N=11,023), 214 (1.94 percent) mapped to multiple DQA categories. The majority of DQ checks mapped to Atemporal Plausibility (49.60 percent), Value Conformance (17.84 percent), and Atemporal Completeness (12.98 percent) categories. Discussion: Using the common DQA terminology, near-complete (99.97 percent) coverage across a wide range of DQA programs and specifications was reached. Comparing the distributions of mapped DQ checks revealed important differences between participating organizations. This variation may be related to the organization's stakeholder requirements, primary analytical focus, or maturity of their DQA program. Not within scope, mapping checks within the data validation context of the terminology may provide additional insights into DQA practice differences. Conclusion: A common DQA terminology provides a means to help organizations and researchers understand the coverage of their current DQA efforts as well as highlight potential areas for additional DQA development. Sharing DQ checks between organizations could help expand the scope of DQA across clinical data networks. [ABSTRACT FROM AUTHOR]

Objective: Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is 'fit' for specific uses. Materials and Methods: DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework's inclusiveness was evaluated against ten published DQ terminologies. Results: Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies. Discussion: Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data. Conclusion: A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods. [ABSTRACT FROM AUTHOR]

Background: Studies have shown associations between heavy alcohol use and white matter alterations in adolescence. Youth involved with the juvenile justice system engage in high levels of risk behavior generally and alcohol use in particular as compared to their non-justice-involved peers. Objectives: This study explored white matter integrity among justice-involved adolescents. Analyses examined fractional anisotropy (FA) and mean diffusivity (MD) between adolescents with low and high levels of problematic alcohol use as assessed by the Alcohol Use Disorders Identification Test (AUDIT). Methods: Participants ( N = 125; 80% male; 14-18 years) completed measures assessing psychological status and substance use followed by diffusion tensor imaging (DTI). DTI data for low ( n = 51) and high AUDIT ( n = 74) adolescents were subjected to cluster-based group comparisons on skeletonized FA and MD data. Results: Whole-brain analyses revealed significantly lower FA in clusters in the right and left posterior corona radiata (PCR) and right superior longitudinal fasciculus (SLF) in the high AUDIT group, as well as one cluster in the right anterior corona radiata that showed higher FA in the high AUDIT group. No differences in MD were identified. Exploratory analyses correlated cluster FA with measures of additional risk factors. FA in the right SLF and left PCR was negatively associated with impulsivity. Conclusion: Justice-involved adolescents with alcohol use problems generally showed poorer FA than their low problematic alcohol use peers. Future research should aim to better understand the nature of the relationship between white matter development and alcohol use specifically as well as risk behavior more generally. [ABSTRACT FROM AUTHOR]

When considering toxic chemicals in the environment, a mechanistic, causal explanation of toxicity may be preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This mechanistic inference framework is an advantageous tool for predictive toxicology, and the first of its kind to produce a mechanistic explanation for each prediction. MechSpy can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology. • Several mechanisms of cellular toxicity can be predicted using a vector representation of semantic knowledge graph concepts. • We produce putative mechanistic explanations as an ordered list of gene expression changes related to each mechanism step. • Mechanisms of toxicity can be represented using ontology concepts, and enriched accounting for the sequential order of events. [ABSTRACT FROM AUTHOR]

Motivation Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Results Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. Availability and implementation https://kghub.org. [ABSTRACT FROM AUTHOR]

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. Methods: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. Results: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53–0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47–0.56), invasive ventilation (OR: 0.59 95% CI: 0.55–0.64), AKI (OR: 0.67 95% CI: 0.63–0.72), or ECMO (OR: 0.51 95% CI: 0.36–0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. Conclusions: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database. [ABSTRACT FROM AUTHOR]

Differential neural development of structures associated with reward and control systems may underlie risky behavior in adolescence. The nucleus accumbens and orbitofrontal cortex (OFC) have been implicated in substance use behavior, although structural studies have yet to explore specific relationships between nucleus accumbens and OFC volumes and alcohol use in adolescence. High resolution structural MRI scans and assessments of recent alcohol use and lifetime substance use were collected in a sample of 168 juvenile justice-involved adolescents to explore whether gray matter volumes were associated with past 3-month quantity and frequency of alcohol use. Gray matter volumes were not associated with average quantity of alcohol use. Accumbens volume was positively associated with past 3-month frequency of drinking, and OFC volume was negatively associated with drinking frequency. Results may suggest that structural differences in regions related to reward and control processing may contribute to risk behavior in adolescence. [ABSTRACT FROM AUTHOR]

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL : http://w3id.org/sssom/spec [ABSTRACT FROM AUTHOR]

BACKGROUND: A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist. OBJECTIVES: Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement. DISCUSSION: There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. [ABSTRACT FROM AUTHOR]

Motivation Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users. Results This article introduces the concept of Agile text mining , and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes. Availability and implementation The three tools that make up the ecosystem, PubAnnotation , PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively. [ABSTRACT FROM AUTHOR]

The role of extracellular vesicles (EVs), specifically exosomes, in intercellular communication likely plays a key role in placental orchestration of pregnancy and maternal immune sensing of the fetus. While murine models are powerful tools to study pregnancy and maternal-fetal immune interactions, in contrast to human placental exosomes, the content of murine placental and pregnancy exosomes remains largely understudied. Using a recently developed in vitro culture technique, murine trophoblast stem cells derived from B6 mice were differentiated into syncytial-like cells. EVs from the conditioned media, as well as from pregnant and non-pregnant sera, were enriched for exosomes. The RNA composition of these murine trophoblast-derived and pregnancy-associated exosome-enriched-EVs (ExoE-EVs) was determined using RNA-sequencing analysis and expression levels confirmed by qRT-PCR. Differentially abundant miRNAs were detected in syncytial differentiated ExoE-EVs, particularly from the X chromosome cluster (mmu-miR-322-3p, mmu-miR-322-5p, mmu-miR-503-5p, mmu-miR-542-3p, and mmu-miR-450a-5p). These were confirmed to be increased in pregnant mouse sera ExoE-EVs by qRT-PCR analysis. Interestingly, fifteen miRNAs were only present within the pregnancy-derived ExoE-EVs compared to non-pregnant controls. Mmu-miR-292-3p and mmu-miR-183-5p were noted to be some of the most abundant miRNAs in syncytial ExoE-EVs and were also present at higher levels in pregnant versus non-pregnant sera ExoE-EVs. The bioinformatics tool, MultiMir, was employed to query publicly available databases of predicted miRNA-target interactions. This analysis reveals that the X-chromosome miRNAs are predicted to target ubiquitin-mediated proteolysis and intracellular signaling pathways. Knowing the cargo of placental and pregnancy-specific ExoE-EVs as well as the predicted biological targets informs studies using murine models to examine not only maternal-fetal immune interactions but also the physiologic consequences of placental-maternal communication. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To describe the nature and scope of nurse‐midwifery practice in Texas and to determine legislative priorities and practice barriers. Background: Across the globe, midwives are the largest group of maternity care providers despite little known about midwifery practice. With a looming shortage of midwives, there is a pressing need to understand midwives’ work environment and scope of practice. Design: Mixed methods research utilising prospective descriptive survey and interview. Methods: An online survey was administered to nurse‐midwives practicing in the state of Texas (N = 449) with a subset (n = 10) telephone interviewed. Descriptive and inferential statistics and content analysis was performed. Results: The survey was completed by 141 midwives with eight interviewed. Most were older, Caucasian and held a master's degree. A majority worked full‐time, were in clinical practice in larger urban areas and were employed by a hospital or physician group. Care was most commonly provided for Hispanic and White women; approximately a quarter could care for greater numbers of patients. Most did not clinically teach midwifery students. Physician practice agreements were believed unnecessary and prescriptive authority requirements restrictive. Legislative issues were typically followed through the professional organisation or social media sites; most felt a lack of competence to influence health policy decisions. While most were satisfied with current clinical practice, a majority planned a change in the next 3 to 5 years. Conclusions: An ageing midwifery workforce, not representative of the race/ethnicity of the populations served, is underutilised with practice requirements that limit provision of services. Health policy changes are needed to ensure unrestricted practice. Relevance to clinical practice: Robust midwifery workforce data are needed as well as a midwifery board which tracks availability and accessibility of midwives. Educators should consider training models promoting long‐term service in underserved areas, and development of skills crucial for impacting health policy change. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Vascular access in pediatric patients can be challenging even with the currently available technological resources. This nurse‐driven research study explored time, cost, and resources for intravenous access to determine if a biomedical device, VeinViewer® Vision, would facilitate improvements in pediatric access. In addition, this study looked at nurse perceptions of skills and confidence around intravenous insertion and if the use of the VeinViewer® impacted these perceptions. Literature examining pediatric intravenous access success rates compared with nurse perceived skills and confidence is lacking. Design: Nonblinded randomized control trial of pediatric nurses working in an acute care hospital setting. Methods: A preliminary needs assessment solicited feedback from nurses regarding their practice, perceived skills, and confidence with placing peripheral intravenous catheters (PIVs). Due to the results of the preliminary needs assessment, a research study was designed and 40 nurses were recruited to participate. The nurses were randomized into either a VeinViewer® or standard practice group. Nurse participants placed intravenous catheters on hospitalized pediatric patients using established procedures while tracking data for the study. Results: Needs assessment showed a majority of nurses felt a biomedical device would be helpful in building their intravenous insertion skills and their confidence. The study results did not demonstrate any clinically significant differences between VeinViewer® use and standard practice for intravenous catheter insertion in pediatric patients for success of placement, number of attempts, or overall cost. In addition, no difference was noted between nurses in either group on perceived skills or confidence with insertion of PIVs. Practice implications: The ongoing need for resources focused on building nurse skills and confidence for PIV insertion was highlighted and organizations should continue to direct efforts toward developing skills and competency for staff that are responsible for pediatric vascular access. This study illustrates the importance of data‐driven decision‐making for expensive hospital‐funded equipment purchases. This nursing led research study highlights how perceptions do not always align with outcomes. The lessons gleaned from this study may aid in decision‐making around pediatric intravenous access practice. [ABSTRACT FROM AUTHOR]