FDA Sentinel System's Coronavirus (COVID-19) Activities

Data from COVID-19 randomized controlled trials (RCTs) may be used to develop clinical guidelines. It is important to understand differences between trial-eligible and real-world (RW) patients’ characteristics and outcomes. This objective of this presentation is to compare hospitalized COVID-19 patients who may meet key eligibility requirements for the Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV-4a) trial (RCT-eligible cohort) to patients without these criteria (RW-COVID cohort) and report unadjusted occurrence of invasive mechanical ventilation (IMV).

It was presented as a Lightning Forum at the 37th International Conference on Pharmacoepidemiology and Therapeutic Risk Management. 

SARS-CoV-2 infection is associated with thrombotic complications. However, the risk of thrombotic events (TEs) for outpatient COVID-19 cases is unknown. This presentation aims to describe hospitalized TEs following an outpatient COVID-19 diagnosis among patients aged 40-79 years without known pre-existing risk factors for thrombosis.

It was presented as a Lightning Forum at the 37th International Conference on Pharmacoepidemiology and Therapeutic Risk Management. 

Administrative claims data, which comprise most of the data in the current Sentinel Distributed Database, are limited in their ability to study severely ill hospitalized patients, including those in the intensive care unit. For example, data on duration of mechanical ventilation, symptom severity, oxygen saturation, supplemental oxygenation requirements, ventilator settings, and lung imaging features are not readily available in claims data. 

Information-rich unstructured electronic health record (EHR) data may address these challenges and enhance medical product safety surveillance. COVID-19 is a compelling use case for using unstructured EHR data to improve medical product safety assessment in Sentinel since studying COVID-19 requires accurate information about its symptoms, severity, comorbidities, and treatment outcomes and because patients with COVID-19 disease often present in emergency department and receive care in inpatient settings. 

This project will yield algorithms and methods to better position Sentinel for addressing future pandemics and advance Sentinel’s long-term objective of enhancing medical product safety surveillance by incorporating EHR data into surveillance methods. Specifically, we will use natural language processing (NLP) and text mining methods to identify patients with COVID-19 disease and to extract clinical features from unstructured EHR data. These features will complement structured data already available in the Sentinel Common Data Model (SCDM). This project will also develop processes that would allow us to more rapidly prototype EHR-based information extraction algorithms for new health outcomes of interest (HOIs) in the case of future pandemics and other needs.
 

The Sentinel System can be used to address numerous questions of importance to FDA during the COVID-19 pandemic. One population of significant interest for study is pregnant patients. Evidence suggests that pregnant patients are more likely to experience severe illness related to respiratory infections, including COVID-19, than nonpregnant patients (1,2). Little information is available to support understanding the natural history of COVID-19 disease in pregnant patients, or the impact of COVID-19 treatment upon pregnant patients or the developing fetus.

To address vulnerable populations, including pregnant patients, Sentinel published a COVID-19 Natural History Master Protocol, designed to identify multiple COVID-19 cohorts to support a variety of on-demand queries and subsequent descriptive and inferential studies. The European Medicines Agency (EMA) has also funded a project, called “COVID-19 infectiOn aNd medicineS In pregnancy” (CONSIGN) which has drafted a protocol to study the natural history of COVID-19 disease in pregnant patients. The CONSIGN study was implemented in varied data sources across eight European countries.

This project implemented the following (three) aims of the CONSIGN protocol:

1. To estimate the prevalence of medicines used and compare this among pregnant patients with COVID‐19, pregnant patients without COVID‐19, and nonpregnant patients with COVID‐19.
2. To describe severity and clinical outcomes of COVID‐19 disease in pregnant patients with COVID‐19, according to treatments received during pregnancy, and compare these data with those of nonpregnant patients of reproductive age with COVID‐19.
3. To assess and compare the rates of adverse maternal and neonatal outcomes in cohorts of pregnant patients with COVID-19 diagnosis in the first, second, or third trimester during pregnancy and pregnant patients without COVID-19.

To learn more about the impacts of the analyses, please visit the Drug Study page here.

(1) Allotey J, Stallings E, Bonet M, et al. Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: living systematic review and meta-analysis. BMJ. Published online September 1, 2020:m3320. doi:10.1136/bmj.m3320

(2) Ellington S. Characteristics of Women of Reproductive Age with Laboratory-Confirmed SARS-CoV-2 Infection by Pregnancy Status — United States, January 22–June 7, 2020. MMWR Morb Mortal Wkly Rep. 2020;69. doi:10.15585/mmwr.mm6925a1

Among hospitalized non-pregnant patients with COVID-19, we aimed to describe the number of patients with hospitalized thrombotic events or death through 28 days. We stratified each outcome by d-dimer level and record of early intensive care services. We used electronic health record (EHR) data from the TriNetX Live™ USA Network to identify adults with positive COVID-19 antigen or PCR laboratory test results or documented COVID-19 diagnoses within ±3 days of a hospitalization record. We queried 65 health care organizations’ data from February 20, 2020 through October 16, 2020.

To inform future sample size calculations pertaining to thrombosis in COVID-19-associated coagulopathy, we describe occurrence of thrombotic events and death among patients aged 40-79 years not hospitalized at the time of COVID-19 identification. We used electronic health record (EHR) data from 64 health care organizations (including inpatient and outpatient data), provided through the TriNetX Live™ platform in their USA network to identify COVID-19 cases in the outpatient setting from February 20, 2020 through September 11, 2020. 

The COVID-19 Natural History Master Protocol effort involves the development of a protocol designed to identify multiple COVID-19 cohorts to support a variety of on-demand queries and subsequent inferential studies. The protocol will be adaptable to various data sources with clinically rich data and will include specific populations of interest, such as the pediatric population, pregnant women, and other vulnerable populations identified by FDA. The implementation of the Natural History protocol will prioritize data source capabilities, such as: a) the capacity to form stable population-based cohorts that can be refreshed quickly and reliably over time, b) deep on-site pharmacoepidemiology experience with a research record of both descriptive and inferential studies, and c) demonstrated analytic capability to support rapid on-demand queries which support identification of the natural history of COVID-19. Expert infectious disease specialists and pharmacoepidemiologists will lead development of this protocol and its implementation in various data sources in the Sentinel network.

The purpose of this project is to design and implement a system that enables rapid querying of electronic health record (EHR) data to support FDA's COVID-19 response. The project will partner with TriNetX to provide access to the TriNetX querying platform and TriNetX Live™ data network. More than 80% of the 76 U.S. Health Care Organizations (HCO) in the TriNetX Live™ data network refresh their data in one, two, or four-week intervals.

This activity supports investigators of COVID-19 related drug trials that need a digital platform for patient informed consent when face to face contact is not possible or practical. The COVID MyStudies application allows for secure and remote electronic consent. The end product is a signed consent form emailed to the patient or legally authorized representative and a copy which the investigator can access in a secure and compliant manner through the platform. The investigator can print the form or transfer an electronic copy into another electronic system. Under the FDA-Catalyst program, the Sentinel Operations Center will coordinate the branding and administration of this application. 

Funding for use of this system by Investigators, Healthcare delivery systems, and Sponsors is provided by the FDA. COVID MyStudies is currently available in the Apple App and Google Play Stores. Investigators, Healthcare delivery systems, and Sponsors can contact CDERMedicalPolicy-RealWorldEvidence@fda.hhs.gov to get started.

More information about COVID MyStudies can be found here.