Characteristics of 24,516 Patients Diagnosed with COVID-19 Illness in a National Clinical Research Network: Results from PCORnet ®

  1. Jason P. Block
  2. Keith A. Marsolo
  3. Kshema Nagavedu
  4. L. Charles Bailey
  5. Henry Cruz
  6. Christopher B. Forrest
  7. Kevin Haynes
  8. Adrian F. Hernandez
  9. Rainu Kaushal
  10. Abel Kho
  11. Kathleen M. McTigue
  12. Vinit P. Nair
  13. Richard Platt
  14. Jon Puro
  15. Russell L. Rothman
  16. Elizabeth Shenkman
  17. Lemuel Russell Waitman
  18. Mark G. Weiner
  19. Neely Williams
  20. Thomas W. Carton
  21. Network Partners

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

National data from diverse institutions across the United States are critical for guiding policymakers as well as clinical and public health leaders. This study characterized a large national cohort of patients diagnosed with COVID-19 in the U.S., compared to patients diagnosed with viral pneumonia and influenza.

Methods and Findings

We captured cross-sectional information from 36 large healthcare systems in 29 U.S. states, participating in PCORnet ® , the National Patient-Centered Clinical Research Network. Patients included were those diagnosed with COVID-19, viral pneumonia and influenza in any care setting, starting from January 1, 2020. Using distributed queries executed at each participating institution, we acquired information for patients on care setting (any, ambulatory, inpatient or emergency department, mechanical ventilator), age, sex, race, state, comorbidities (assessed with diagnostic codes), and medications used for treatment of COVID-19 (hydroxychloroquine with or without azithromycin; corticosteroids, anti-interleukin-6 agents).

During this time period, 24,516 patients were diagnosed with COVID-19, with 42% in an emergency department or inpatient hospital setting; 79,639 were diagnosed with viral pneumonia (53% inpatient/ED) and 163,984 with influenza (41% inpatient/ED). Among COVID-19 patients, 68% were 20 to <65 years of age, with more of the hospitalized/ED patients in older age ranges (23% 65+ years vs. 12% for COVID-19 patients in the ambulatory setting). Patients with viral pneumonia were of a similar age, and patients with influenza were much younger. Comorbidities were common, especially for patients with COVID-19 and viral pneumonia, with hypertension (32% for COVID-19 and 46% for viral pneumonia), arrhythmias (20% and 35%), and pulmonary disease (19% and 40%) the most common. Hydroxychloroquine was used in treatment for 33% and tocilizumab for 11% of COVID-19 patients on mechanical ventilators (25% received azithromycin as well).

Conclusion and Relevance

PCORnet leverages existing data to capture information on one of the largest U.S. cohorts to date of patients diagnosed with COVID-19 compared to patients diagnosed with viral pneumonia and influenza.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.01.20163733: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The codes for SARS-CoV-2 laboratory orders were Special Use terms identified via the Logical Observation Identifiers Names and Codes (LOINC) Prerelease Page, as well as guidance from the Centers for Medicare and Medicaid Services (CMS) on SARS-CoV-2-related Healthcare Common Procedure Coding System (HCPCS) codes (18).
    SARS-CoV-2-related Healthcare
    suggested: None
    Using RxNorm and National Drug Codes (NDC) lists, we identified use of immunosuppressive medications over the same three-year period (19, 20).
    RxNorm
    suggested: (RxNorm, RRID:SCR_006645)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Several limitations are inherent to working with structured healthcare data. First, electronic health records may not have complete data on some patients, especially patients diagnosed in virtual visits or who were diagnosed and treated entirely outside of a PCORnet health system, including in skilled nursing facilities or nursing homes. In such cases, the PCORnet Common Data Model may not have any information about a COVID-19 infection or inadequate information on chronic diseases and medication use. We separately captured information on patients who were seen in the healthcare system prior to their hospitalization or emergency department visit for COVID-19; among these patients, prevalence of comorbidities was slightly higher when compared to all patients seen in the emergency department or hospitalized. Missing data can also be an issue for variables such as race. Some medications being used in research protocols also were likely missing because they are not captured in routine medication administration tables. Second, because the research network was set up to accommodate quarterly refreshes of clinical data, we quickly changed protocols to allow for weekly updates. The standard quarterly data refreshes undergo detailed curation to ensure that data are compliant with standards of the Common Data Model, and such efforts are not possible with weekly refreshes. Erroneous records may exist that will only be identified later through data curation. The PCORnet Coor...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.08.01.20163733v1 on medRxiv