Standardizing, harmonizing, and protecting data collection to broaden the impact of COVID-19 research: the rapid acceleration of diagnostics-underserved populations (RADx-UP) initiative.

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

Assessing seizure burden in pediatric epilepsy using an electronic medical record-based tool through a common data element approach.

OBJECTIVE: Improvement in epilepsy care requires standardized methods to assess disease severity. We report the results of implementing common data elements (CDEs) to document epilepsy history data in the electronic medical record (EMR) after 12 months of clinical use in outpatient encounters. METHODS: Data regarding seizure frequency were collected during routine clinical encounters using a CDE-based form within our EMR. We extracted CDE data from the EMR and developed measurements for seizure severity and seizure improvement scores. Seizure burden and improvement was evaluated by patient demographic and encounter variables for in-person and telemedicine encounters. RESULTS: We assessed a total of 1696 encounters in 1038 individuals with childhood epilepsies between September 6, 2019 and September 11, 2020 contributed by 32 distinct providers. Childhood absence epilepsy (n = 121), Lennox-Gastaut syndrome (n = 86), and Dravet syndrome (n = 42) were the most common epilepsy syndromes. Overall, 43% (737/1696) of individuals had at least monthly seizures, 17% (296/1696) had a least daily seizures, and 18% (311/1696) were seizure-free for >12 months. Quantification of absolute seizure burden and changes in seizure burden over time differed between epilepsy syndromes, including high and persistent seizure burden in patients with Lennox-Gastaut syndrome. Individuals seen via telemedicine or in-person encounters had comparable seizure frequencies. Individuals identifying as Hispanic/Latino, particularly from postal codes with lower median household incomes, were more likely to have ongoing seizures that worsened over time. SIGNIFICANCE: Standardized documentation of clinical data in childhood epilepsies through CDE can be implemented in routine clinical care at scale and enables assessment of disease burden, including characterization of seizure burden over time. Our data provide insights into heterogeneous patterns of seizure control in common pediatric epilepsy syndromes and will inform future initiatives focusing on patient-centered outcomes in childhood epilepsies, including the impact of telemedicine and health care disparities.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research.

OBJECTIVE: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. METHODS: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. RESULTS: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. CONCLUSIONS: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

The Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID): Development of Case Report Forms for Global Use.

Since its original report in January 2020, the coronavirus disease 2019 (COVID-19) due to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly become one of the deadliest global pandemics. Early reports indicate possible neurological manifestations associated with COVID-19, with symptoms ranging from mild to severe, highly variable prevalence rates, and uncertainty regarding causal or coincidental occurrence of symptoms. As neurological involvement of any systemic disease is frequently associated with adverse effects on morbidity and mortality, obtaining accurate and consistent global data on the extent to which COVID-19 may impact the nervous system is urgently needed. To address this need, investigators from the Neurocritical Care Society launched the Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID). The GCS-NeuroCOVID consortium rapidly implemented a Tier 1, pragmatic study to establish phenotypes and prevalence of neurological manifestations of COVID-19. A key component of this global collaboration is development and application of common data elements (CDEs) and definitions to facilitate rigorous and systematic data collection across resource settings. Integration of these elements is critical to reduce heterogeneity of data and allow for future high-quality meta-analyses. The GCS-NeuroCOVID consortium specifically designed these elements to be feasible for clinician investigators during a global pandemic when healthcare systems are likely overwhelmed and resources for research may be limited. Elements include pediatric components and translated versions to facilitate collaboration and data capture in Latin America, one of the epicenters of this global outbreak. In this manuscript, we share the specific data elements, definitions, and rationale for the adult and pediatric CDEs for Tier 1 of the GCS-NeuroCOVID consortium, as well as the translated versions adapted for use in Latin America. Global efforts are underway to further harmonize CDEs with other large consortia studying neurological and general aspects of COVID-19 infections. Ultimately, the GCS-NeuroCOVID consortium network provides a critical infrastructure to systematically capture data in current and future unanticipated disasters and disease outbreaks.