Reduction in Risk of Death Among Patients Admitted With COVID-19 Between the First and Second Epidemic Waves in New York City

  1. Anthony Bowen
  2. Jason Zucker
  3. Yanhan Shen
  4. Simian Huang
  5. Qiheng Yan
  6. Medini K Annavajhala
  7. Anne-Catrin Uhlemann
  8. Louise Kuhn
  9. Magdalena Sobieszczyk
  10. Delivette Castor

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

Many regions have experienced successive epidemic waves of coronavirus disease 2019 (COVID-19) since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with heterogeneous differences in mortality. Elucidating factors differentially associated with mortality between epidemic waves may inform clinical and public health strategies.

Methods

We examined clinical and demographic data among patients admitted with COVID-19 during the first (March–August 2020) and second (August 2020–March 2021) epidemic waves at an academic medical center in New York City.

Results

Hospitalized patients (n = 4631) had lower overall and 30-day in-hospital mortality, defined as death or discharge to hospice, during the second wave (14% and 11%) than the first (22% and 21%). The wave 2 in-hospital mortality decrease persisted after adjusting for several potential confounders. Adjusting for the volume of COVID-19 admissions, a measure of health system strain, accounted for the mortality difference between waves. Several demographic and clinical patient factors were associated with an increased risk of mortality independent of wave: SARS-CoV-2 cycle threshold, do-not-intubate status, oxygen requirement, and intensive care unit admission.

Conclusions

This work suggests that the increased in-hospital mortality rates observed during the first epidemic wave were partly due to strain on hospital resources. Preparations for future epidemics should prioritize evidence-based patient risks, treatment paradigms, and approaches to augment hospital capacity.

Article activity feed

  1. Version published to 10.1093/ofid/ofac436
  2. ScreenIT

    SciScore for 10.1101/2022.03.29.22273044: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Study approvals were obtained from the Columbia University Irving Medical Center Institutional Review Board (
    Consent: The requirement for obtaining written informed consent was waived by the IRB.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analyses: Histogram plots were used to visualize the distribution of COVID-19 cases and admissions.
    Statistical Analyses
    suggested: None

    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: Our study has several limitations. Cases of COVID-19 included in this analysis are likely to be undercounted from the first wave. All cases admitted with positive tests during this period would be included. Still, testing capacity was limited at the time resulting in tests being prioritized for patients with high clinical suspicion of COVID-19 or underlying comorbidities. Detection of incidental COVID-19 likely increased in the second wave when routine testing was widely available. Information bias in the EHR resulted in inadequate information to accurately characterize patients with co-morbid conditions. RT-PCR Ct data were also missing in a differential way that could have biased findings in either direction. The inclusion of patients admitted to our institution may also not wholly reflect NYC-wide cases since some individuals likely decided to avoid presentation to the hospital, especially during the first pandemic wave. In addition, it is possible that pre-existing immunity had a differential impact on infections and severe illness during the second wave. Our conclusions are limited to hospitalized patients. Extrapolating to the general population can increase the likelihood of Berkson’s bias in identifying spurious correlations not present outside the hospital setting. Our estimates of hospital capacity are based on COVID-19 admissions due to difficulties accurately estimating total hospital admissions from our database. Patients admitted with COVID-19, howe...

    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 10.1101/2022.03.29.22273044v1 on medRxiv