Resource Profile: The Regenstrief Institute COVID-19 Research Data Commons (CoRDaCo)

  1. Katie S. Allen
  2. Nader Zidan
  3. Vishal Dey
  4. Eneida A. Mendonca
  5. Shaun Grannis
  6. Suranga Kasturi
  7. Babar Khan
  8. Sarah Zappone
  9. David Haggstrom
  10. Laura Ruppert
  11. Titus Schleyer
  12. Xia Ning
  13. Peter Embi
  14. Umberto Tachinardi

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Abstract

The primary objective of the COVID-19 Research Data Commons (CoRDaCo) is to provide broad and efficient access to a large corpus of clinical data related to COVID-19 in Indiana, facilitating research and discovery. This curated collection of data elements provides information on a significant portion of COVID-19 positive patients in the State from the beginning of the pandemic, as well as two years of health information prior its onset. CoRDaCo combines data from multiple sources, including clinical data from a large, regional health information exchange, clinical data repositories of two health systems, and state laboratory reporting and vital records, as well as geographic-based social variables. Clinical data cover information such as healthcare encounters, vital measurements, laboratory orders and results, medications, diagnoses, the Charlson Comorbidity Index and Pediatric Early Warning Score, COVID-19 vaccinations, mechanical ventilation, restraint use, intensive care unit and ICU and hospital lengths of stay, and mortality. Interested researchers can visit ridata.org or email askrds@regenstrief.org to discuss access to CoRDaCo.

Key Features

  • CoRDaCo includes patient-level data on diagnosis and treatment, healthcare utilization, outcomes, and demographics. The level of detail available for each patient varies depending on the source of the clinical data.

  • CoRDaCo uses geographic identifiers to link patient-specific data to area-level social factors, such as census variables and social deprivation indices.

  • As of 4/30/21, the CoRDaCo cohort consists of over 776,000 cases, including granular data on over 15,000 patients who were admitted to an intensive care unit, and over 1,362,000 COVID-19-negative controls. Data is currently refreshed two times per month.

  • The most prevalent comorbidities in the data set include hypertension, diabetes, chronic pulmonary disease, renal disease, cancer, and congestive heart failure.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.12.17.21267942: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.12.17.21267942v1 on medRxiv