A learning health system approach to the COVID ‐19 pandemic: System‐wide changes in clinical practice and 30‐day mortality among hospitalized patients

  1. Erin K. McCreary
  2. Kevin E. Kip
  3. J. Ryan Bariola
  4. Mark Schmidhofer
  5. Tami Minnier
  6. Katelyn Mayak
  7. Debbie Albin
  8. Jessica Daley
  9. Kelsey Linstrum
  10. Erik Hernandez
  11. Rachel Sackrowitz
  12. Kailey Hughes
  13. Christopher Horvat
  14. Graham M. Snyder
  15. Bryan J. McVerry
  16. Donald M. Yealy
  17. David T. Huang
  18. Derek C. Angus
  19. Oscar C. Marroquin

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Abstract

Introduction

Rapid, continuous implementation of credible scientific findings and regulatory approvals is often slow in large, diverse health systems. The coronavirus disease 2019 (COVID‐19) pandemic created a new threat to this common “slow to learn and adapt” model in healthcare. We describe how the University of Pittsburgh Medical Center (UPMC) committed to a rapid learning health system (LHS) model to respond to the COVID‐19 pandemic.

Methods

A treatment cohort study was conducted among 11 429 hospitalized patients (pediatric/adult) from 22 hospitals (PA, NY) with a primary diagnosis of COVID‐19 infection (March 19, 2020 ‐ June 6, 2021). Sociodemographic and clinical data were captured from UPMC electronic medical record (EMR) systems. Patients were grouped into four time‐defined patient “waves” based on nadir of daily hospital admissions, with wave 3 (September 20, 2020 ‐ March 10, 2021) split at its zenith due to high volume with steep acceleration and deceleration. Outcomes included changes in clinical practice (eg, use of corticosteroids, antivirals, and other therapies) in relation to timing of internal system analyses, scientific publications, and regulatory approvals, along with 30‐day rate of mortality over time.

Results

The mean (SD) daily number of admissions across hospitals was 26 (29) with a maximum 7‐day moving average of 107 patients. System‐wide implementation of the use of dexamethasone, remdesivir, and tocilizumab occurred within days of release of corresponding seminal publications and regulatory actions. After adjustment for differences in patient clinical profiles over time, each month of hospital admission was associated with an estimated 5% lower odds of 30‐day mortality (adjusted odds ratio [OR] = 0.95, 95% confidence interval: 0.93‐0.97, P  < .001).

Conclusions

In our large LHS, near real‐time changes in clinical management of COVID‐19 patients happened promptly as scientific publications and regulatory approvals occurred throughout the pandemic. Alongside these changes, patients with COVID‐19 experienced lower adjusted 30‐day mortality following hospital admission over time.

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  1. Version published to 10.1002/lrh2.10304
  2. ScreenIT

    SciScore for 10.1101/2021.08.26.21262661: (What is this?)

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

    Table 1: Rigor

    EthicsIACUC: Our study received formal ethics approval by the UPMC Ethics and Quality Improvement Review Committee (
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A two-sided type I error rate of 0.05 was used, and all analyses were conducted using the SAS System, Version 9.4 (SAS Institute, Cary, NC).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    We used The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) approach8 (see Supplemental Table 2).
    RECORD
    suggested: (RECORD, RRID:SCR_009097)

    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: We detected the following sentences addressing limitations in the study:
    There are some limitations to our study; because this is the experience in one, albeit large, integrated healthcare system in Western Pennsylvania, the generalizability of our findings may be questioned. However, the fact that we saw similar findings across our different sites suggests that our findings are applicable across academic, community, and rural hospitals. In addition, we cannot determine the extent to which the therapeutic interventions implemented uniformly by the UPMC COVID-19 Therapeutics Committee contributed to lower adjusted mortality over time, as opposed to other less well documented clinical practices that may have been implemented over time (i.e., mechanical ventilation). The LHS description and results presented herein are not meant to be content- or institution-specific, but rather to illustrate some of the processes that can be used to support the NAM imperative for clinical decisions that are supported by accurate, timely, and up-to-date clinical information that reflects the best available evidence.22 On a broader level, we support the stated advocacy for a learning health network that promotes collaboration among health systems, community-based organizations, and government agencies, especially during public health emergencies.4

    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.
    • Thank you for including a protocol registration statement.

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

    About SciScore

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  3. Version published to 10.1101/2021.08.26.21262661v1 on medRxiv