From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding.

In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project-Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation-with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population.

Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation.

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.

Caring for data: Value creation in a data-intensive research laboratory.

Drawing upon ethnographic observations of staff working within a research laboratory built around research and clinical data from twins, this article analyzes practices underlying the production and maintenance of a research database. While critical data studies have discussed different forms of 'data work' through which data are produced and turned into effective research resources, in this paper we foreground a specific form of data work, namely the affective and attentive relationships that humans build with data. Building on STS and feminist scholarship that highlights the importance of care in scientific work, we capture this specific form of data work as care. Treating data as relational entities, we discuss a set of caring practices that staff employ to produce and maintain their data, as well as the hierarchical and institutional arrangements within which these caring practices take place. We show that through acts of caring, that is, through affective and attentive engagements, researchers build long-term relationships with the data they help produce, and feel responsible for its flourishing and growth. At the same time, these practices of care - which we found to be gendered and valued differently from other practices within formal and informal reward systems - help to make data valuable for the institution. In this manner, care for data is an important practice of valuation and valorisation within data-intensive research that has so far received little explicit attention in scholarship and professional research practice.

The Firearms Data Gap.

The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun violence and promote productive conversation about gun policy. In the absence of federal leadership in firearms data systems improvement, motivated states may take proactive steps to stitch gaps in data systems. We propose that states evaluate the gaps in their systems, expand data collection, and improve data presentation and availability.

Whose Commons? Data Protection as a Legal Limit of Open Science.

Open science has recently gained traction as establishment institutions have come on-side and thrown their weight behind the movement and initiatives aimed at creation of information commons. At the same time, the movement's traditional insistence on unrestricted dissemination and reuse of all information of scientific value has been challenged by the movement to strengthen protection of personal data. This article assesses tensions between open science and data protection, with a focus on the GDPR.

The use of an electronic health record system reduces errors in the National Hip Fracture Database.

AIM: to compare the validity of data submitted from a UK level 1 trauma centre to the National Hip Fracture Database (NHFD) before and after the introduction of an electronic health record system (EHRS). PATIENTS AND METHODS: a total of 3224 records were reviewed from July 2009 to July 2017. 2,133 were submitted between July 2009 and October 2014 and 1,091 between October 2014 and July 2017, representing data submitted before and after the introduction of the EHRS, respectively. Data submitted to the NHFD were scrutinised against locally held data. RESULTS: use of an EHRS was associated with significant reductions in NHFD errors. The operation coding error rate fell significantly from 23.2% (494/2133) to 7.6% (83/1091); P < 0.001. Prior to EHRS introduction, of the 109 deaths recorded in the NHFD, 64 (59%) were incorrect. In the EHRS dataset, all the 112 recorded deaths were correct (P < 0.001). There was no significant difference in the error rate for fracture coding. In the EHRS dataset, after controlling for sample month, entries utilising an operation note template with mandatory fields relevant to NHFD data were more likely to be error free than those not using the template (OR 2.69; 95% CI 1.92-3.78). CONCLUSION: this study highlights a potential benefit of EHR systems, which offer automated data collection for auditing purposes. However, errors in data submitted to the NHFD remain, particularly in cases where an NHFD-specific operation note template is not used. Clinician engagement with new technologies is vital to avoid human error and ensure database integrity.

The Italian autism network (ITAN): a resource for molecular genetics and biomarker investigations.

BACKGROUND: A substantial genetic component accounts for Autism Spectrum Disorders (ASD) aetiology, with some rare and common genetic risk factors recently identified. Large collections of DNAs from thoroughly characterized ASD families are an essential step to confirm genetic risk factors, identify new variants and investigate genotype-phenotype correlations. The Italian Autism Network aimed at constituting a clinical database and a biorepository of samples derived from ASD subjects and first-degree relatives extensively and consistently characterized by child psychiatry centers in Italy. METHODS: The study was approved by the ethical committee of the University of Verona, the coordinating site, and by the local ethical committees of each recruiting site. Certified staff was specifically trained at each site for the overall study conduct, for clinical protocol administration and handling of biological material. A centralized database was developed to collect clinical assessment and medical records from each recruiting site. Children were eligible for recruitment based on the following inclusion criteria: age 4-18 years, at least one parent or legal guardian giving voluntary written consent, meeting DSM-IV criteria for Autistic Disorder or Asperger's Disorder or Pervasive Developmental Disorder NOS. Affected individuals were assessed by full psychiatric, neurological and physical examination, evaluation with ADI-R and ADOS scales, cognitive assessment with Wechsler Intelligence Scale for Children or Preschool and Primary, Leiter International Performance Scale or Griffiths Mental Developmental Scale. Additional evaluations included language assessment, the Krug Asperger's Disorder Index, and instrumental examination such as EEG and structural MRI. DNA, RNA and plasma were collected from eligible individuals and relatives. A central laboratory was established to host the biorepository, perform DNA and RNA extraction and lymphocytes immortalisation. DISCUSSION: The study has led to an extensive collection of biological samples associated with standardised clinical assessments from a network of expert clinicians and psychologists. Eighteen sites have received ADI/ADOS training, thirteen of which have been actively recruiting. The clinical database currently includes information on 812 individuals from 249 families, and the biorepository has samples for 98% of the subjects. This effort has generated a highly valuable resource for conducting clinical and genetic research of ASD, amenable to further expansion.

Opportunities for Use of Blockchain Technology in Medicine.

Blockchain technology is a decentralized database that stores a registry of assets and transactions across a peer-to-peer computer network, which is secured through cryptography, and over time, its history gets locked in blocks of data that are cryptographically linked together and secured. So far, there have been use cases of this technology for cryptocurrencies, digital contracts, financial and public records, and property ownership. It is expected that future uses will expand into medicine, science, education, intellectual property, and supply chain management. Likely applications in the field of medicine could include electronic health records, health insurance, biomedical research, drug supply and procurement processes, and medical education. Utilization of blockchain is not without its weaknesses and currently, this technology is extremely immature and lacks public or even expert knowledge, making it hard to have a clear strategic vision of its true future potential. Presently, there are issues with scalability, security of smart contracts, and user adoption. Nevertheless, with capital investments into blockchain technology projected to reach US$400 million in 2019, health professionals and decision makers should be aware of the transformative potential that blockchain technology offers for healthcare organizations and medical practice.

Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies.

BACKGROUND: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. METHODS: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. RESULTS: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. CONCLUSIONS: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.

MINDMAP: establishing an integrated database infrastructure for research in ageing, mental well-being, and the urban environment.

BACKGROUND: Urbanization and ageing have important implications for public mental health and well-being. Cities pose major challenges for older citizens, but also offer opportunities to develop, test, and implement policies, services, infrastructure, and interventions that promote mental well-being. The MINDMAP project aims to identify the opportunities and challenges posed by urban environmental characteristics for the promotion and management of mental well-being and cognitive function of older individuals. METHODS: MINDMAP aims to achieve its research objectives by bringing together longitudinal studies from 11 countries covering over 35 cities linked to databases of area-level environmental exposures and social and urban policy indicators. The infrastructure supporting integration of this data will allow multiple MINDMAP investigators to safely and remotely co-analyse individual-level and area-level data. Individual-level data is derived from baseline and follow-up measurements of ten participating cohort studies and provides information on mental well-being outcomes, sociodemographic variables, health behaviour characteristics, social factors, measures of frailty, physical function indicators, and chronic conditions, as well as blood derived clinical biochemistry-based biomarkers and genetic biomarkers. Area-level information on physical environment characteristics (e.g. green spaces, transportation), socioeconomic and sociodemographic characteristics (e.g. neighbourhood income, residential segregation, residential density), and social environment characteristics (e.g. social cohesion, criminality) and national and urban social policies is derived from publically available sources such as geoportals and administrative databases. The linkage, harmonization, and analysis of data from different sources are being carried out using piloted tools to optimize the validity of the research results and transparency of the methodology. DISCUSSION: MINDMAP is a novel research collaboration that is combining population-based cohort data with publicly available datasets not typically used for ageing and mental well-being research. Integration of various data sources and observational units into a single platform will help to explain the differences in ageing-related mental and cognitive disorders both within as well as between cities in Europe, the US, Canada, and Russia and to assess the causal pathways and interactions between the urban environment and the individual determinants of mental well-being and cognitive ageing in older adults.

Protocol for developing a Database of Zoonotic disease Research in India (DoZooRI).

INTRODUCTION: Zoonotic and emerging infectious diseases (EIDs) represent a public health threat that has been acknowledged only recently although they have been on the rise for the past several decades. On an average, every year since the Second World War, one pathogen has emerged or re-emerged on a global scale. Low/middle-income countries such as India bear a significant burden of zoonotic and EIDs. We propose that the creation of a database of published, peer-reviewed research will open up avenues for evidence-based policymaking for targeted prevention and control of zoonoses. METHODS AND ANALYSIS: A large-scale systematic mapping of the published peer-reviewed research conducted in India will be undertaken. All published research will be included in the database, without any prejudice for quality screening, to broaden the scope of included studies. Structured search strategies will be developed for priority zoonotic diseases (leptospirosis, rabies, anthrax, brucellosis, cysticercosis, salmonellosis, bovine tuberculosis, Japanese encephalitis and rickettsial infections), and multiple databases will be searched for studies conducted in India. The database will be managed and hosted on a cloud-based platform called Rayyan. Individual studies will be tagged based on key preidentified parameters (disease, study design, study type, location, randomisation status and interventions, host involvement and others, as applicable). ETHICS AND DISSEMINATION: The database will incorporate already published studies, obviating the need for additional ethical clearances. The database will be made available online, and in collaboration with multisectoral teams, domains of enquiries will be identified and subsequent research questions will be raised. The database will be queried for these and resulting evidence will be analysed and published in peer-reviewed journals.

Japan's initiative on rare and undiagnosed diseases (IRUD): towards an end to the diagnostic odyssey.

Japan has been facing challenges relating to specifically defined rare diseases, called Nan-Byo in Japanese (literally 'difficult'+'illness'), and has already taken measures for them since 1972. This governmental support has surely benefited Nan-Byo patients; however, those suffering from medically unidentified conditions do not fall into this scheme and thus still confront difficulty in obtaining an examination, a diagnosis, and a treatment. To identify such rare and often undiagnosed diseases, we must integrate systematic diagnosis by medical experts with phenotypic and genetic data matching. Thus, in collaboration with Nan-Byo researchers and the Japanese universal healthcare system, the Japan Agency for Medical Research and Development launched the Initiative on Rare and Undiagnosed Diseases (IRUD) in 2015. IRUD is an ambitious challenge to construct a comprehensive medical network and an internationally compatible data-sharing framework. Synergizing with existing next-generation sequencing capabilities and other infrastructure, the nationwide medical research consortium has successfully grown to accept more than 2000 undiagnosed registrants by December 2016. We also aim at expanding the concept of microattribution throughout the initiative; that is, proper credit as collaborators shall be given to local primary care physicians, nurses and paramedics, patients, their family members, and those supporting the affected individuals whenever appropriate. As it shares many challenges among similar global efforts, IRUD's future successes and lessons learned will significantly contribute to ongoing international endeavors, involving players in basic research, applied research, and societal implementation.

Imbalanced target prediction with pattern discovery on clinical data repositories.

BACKGROUND: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. METHODS: We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. RESULTS: We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. CONCLUSIONS: Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.

Architecting the Data Loading Process for an i2b2 Research Data Warehouse: Full Reload versus Incremental Updating.

Research data warehouses integrate research and patient data from one or more sources into a single data model that is designed for research. Typically, institutions update their warehouse by fully reloading it periodically. The alternative is to update the warehouse incrementally with new, changed and/or deleted data. Full reloads avoid having to correct and add to a live system, but they can render the data outdated for clinical trial accrual. They place a substantial burden on source systems, involve intermittent work that is challenging to resource, and may involve tight coordination across IT and informatics units. We have implemented daily incremental updating for our i2b2 data warehouse. Incremental updating requires substantial up-front development, and it can expose provisional data to investigators. However, it may support more use cases, it may be a better fit for academic healthcare IT organizational structures, and ongoing support needs appear to be similar or lower.

Base de datos sistematizada de historias clínicas pertenecientes al I. I. HEMA: informe preliminar / Systematized database of medical records belonging to I. I. HEMA: preliminary report

Se procedió a confeccionar una base de datos de los casos atendidos en Clínica Hematológica del Instituto de Investigaciones Hematológicas. Se registró información sobre: a) Datos demográficos; b) Enfermedad actual; c) Métodos diagnósticos y d) Clasificación de las enfermedades según CIE 10. Sobre un total de 3573 casos registrados entre junio de 2002 y noviembre 2015 se analizaron 1300 (42%) casos. Los principales resultados muestran un predominio de las anemias, y entre ellas las ferropénicas. El mielograma y el frotis de sangre periférica predominaron entre los procedimientos diagnósticos. El tiempo entre primera consulta y diagnóstico muestra que en la mayoría (79,24%) de los casos este fue menor a 3 meses. En un 55,9 % de los casos se inició tratamiento antes del mes. (AU)

A data base was made from cases treated in the Hematological Clinic service. The following information was recorded: a) Demographics; b) Current disease; c) Diagnostic methods and d) Disease classification according to CIE 10. There were analyzed 1300 (42%) out of 3573 cases between June 2002 and November 2015. The main results show predominance of anemia and among them iron deficiency. The myelogram and peripheral blood smear predominated among the diagnostic procedures. Time between first consultation and diagnosis shows that in the majority (79.24%) of cases was less than 3 months. In 55.9% of cases it started treatment before the month. (AU)

Implementing partnership-driven clinical federated electronic health record data sharing networks.

OBJECTIVE: Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results. METHODS: Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages. RESULTS: We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility. CONCLUSION: An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.