Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

  1. Stephen Salerno
  2. Yuming Sun
  3. Emily L. Morris
  4. Xinwei He
  5. Yajing Li
  6. Ziyang Pan
  7. Peisong Han
  8. Jian Kang
  9. Michael W. Sjoding
  10. Yi Li

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Abstract

Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories.

Methods

This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions.

Results

Younger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates.

Conclusions

This study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.

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  1. Version published to 10.1371/journal.pone.0258278
  2. ScreenIT

    SciScore for 10.1101/2021.09.07.21263213: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Study Population: This was a retrospective study, approved by the UM Institutional Review Board, on multiple outcomes of COVID-19 patients and their associated risk factors.
    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: We detected the following sentences addressing limitations in the study:
    Limitations: First, as a small number of patients who transferred in from other institutions did not have medical history data, we had to remove them from analysis, though their impacts on our results were limited. Second, as this study was based exclusively on patients of Michigan Medicine, there may be biases in the patient mix, affecting the generalizability to more diverse populations or other geographic areas. On the other hand, these patients did offer an opportunity to study COVID-19 outcomes in a local region that had been severely impacted by the pandemic. Third, this was a retrospective study of an existing EMR database. As such, we are limited in our ability to draw causal interpretations from these results. In addition, due to the nature of EMR data, there is always the possibility for misclassification bias and/or inaccurate data entry. Lastly, future work, with longer follow-up, should focus on the residual impact of COVID-19 among recovered patients to elucidate the effect of lasting symptoms and acquired comorbidity burden on long-term quality of life and mortality.

    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.

  3. Version published to 10.1101/2021.09.07.21263213v1 on medRxiv