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

Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis.

We assessed the validity of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six FDA Sentinel System Data Partners (two large national insurers, four integrated delivery systems) from February 20 - October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20 - March 31 (Time A), April 1 - April 30 (Time B), May 1 - October 17 (Time C).

Epidemiologic data^1-4 indicate that communities of color continue to be disproportionally impacted by the ongoing COVID-19 pandemic. We sought to determine the association between race and COVID-19 outcomes among persons diagnosed or hospitalized with COVID-19, after adjusting for relevant clinical and demographic risk factors.

Our specific aims were:

• To evaluate the association between race and hospitalization with COVID-19 among patients diagnosed with COVID-19 after adjusting for demographic and clinical risk factors.

• To evaluate the association between race and critical COVID-19 disease among patients hospitalized with COVID-19 after adjusting for demographic and clinical risk factors.

• To evaluate the association between race and mortality among patients hospitalized with COVID-19 after adjusting for demographic and clinical risk factors.

To meet these aims, we identified cohorts of individuals who had evidence of a COVID-19 infection and separately evaluated the occurrence of any hospitalization with COVID-19 and hospitalization with COVID-19 without elements of critical COVID-19 within 30 days of cohort entry. We also identified cohorts of individuals who were hospitalized with COVID-19, and separately evaluated the occurrence of hospitalization with critical COVID-19, inpatient all-cause mortality, or any all-cause mortality within 30 days of cohort entry. We described all study cohorts at baseline by race, urbanicity, and outcome status. We evaluated crude and adjusted associations between race and COVID-19 outcomes using univariate and multivariable logistic regression models run at each Data Partner site.

Effect estimates from all participating sites were individually reviewed for any warnings related to model convergence. Only results for those sites where the multivariable outcome regression models converged without any warnings are included in the final study report published on our website. Site-specific estimates were pooled at Sentinel Operations Center in a random-effects meta-analysis using local plus programming. A publicly available macro was used to conduct the meta-analysis.

The analytic package associated with this analysis can be found externally in Sentinel's Git Repository located here. The Git Repository serves as Sentinel's version control tracking system for analytic packages and technical documentation

Sources:

1. Laboratory Confirmed COVID-19-Associated Hospitalizations. https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html

2. The Color of Coronavirus: COVID-19 Deaths by Race and Ethnicity in the U.S. https://www.apmresearchlab.org/covid/deaths-by-race

3. United States Census Bureau. Quick Facts. https://www.census.gov/quickfacts/fact/table/US/PST045219

4. CDC COVID Data Tracker. https://covid.cdc.gov/covid-data-tracker/#demographics

This project was funded by U.S. Food and Drug Administration (FDA) Office of Minority Health and Health Equity (OMHHE).

​​​​​This presentation explores the natural history of coagulopathy in COVID-19. It was presented at the Reagan-Udall Foundation's COVID-19 Evidence Accelerator Therapeutics Parallel Analysis Working Group meeting.

This presentation shares experiences of the US FDA’s Sentinel System during the development and implementation of its COVID-19-related studies by covering the following topics: 1) Rationale and development of a Master Protocol to identify health plan members with COVID-19 in the US FDA Sentinel System, classify COVID-19 disease severity, and ascertain relevant data elements; 2) Methods to identify persons with COVID-19 and key data elements validly within inpatient electronic health records; 3) Challenges and solutions to examining complications of COVID-19 in claims data; and 4) Approaches to enhance collaborations with researchers, data providers, and regulatory agencies to enhance the validity and generalizability of results.

It was presented as a symposium 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. 

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. 

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post‐market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID‐19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID‐19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data.

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 people. Evidence suggests that pregnant people are more likely to experience severe illness related to respiratory infections, including COVID-19, than nonpregnant people (1,2). Little information is available to support understanding the natural history of COVID-19 disease in pregnant people, or the impact of COVID-19 treatment upon pregnant people or the developing fetus.

To address vulnerable populations, including pregnant people, 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 people. 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 people with COVID‐19, pregnant people without COVID‐19, and nonpregnant people with COVID‐19.
2. To describe severity and clinical outcomes of COVID‐19 disease in pregnant people with COVID‐19, according to treatments received during pregnancy, and compare these data with those of nonpregnant people of reproductive age with COVID‐19.
3. To assess and compare the rates of adverse maternal and neonatal outcomes in cohorts of pregnant people with COVID-19 diagnosis in the first, second, or third trimester during pregnancy and pregnant people without COVID-19.

(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 adults 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.