Decreased Stroke, Acute Coronary Syndrome, and Corresponding Interventions at 65 US Hospitals Following COVID-19

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Abstract

Background

Following the emergence of coronavirus disease 2019 (COVID-19), early reports suggested a decrease in stroke and acute coronary syndrome (ACS). We sought to provide descriptive statistics for stroke and ACS from a sample of hospitals throughout the United States, comparing data from March 2020 to similar months pre-COVID.

Methods

We performed a retrospective analysis of 65 academic and community hospitals in the Vizient Clinical Data Base. The primary outcome is monthly count of stroke and ACS, and acute procedures for both, from February and March in 2020 compared to the same months in 2018 and 2019. Results are aggregated for all hospitals and reported by Census Region.

Results

We identified 51,246 strokes (42,780 ischemic, 8,466 hemorrhagic), 1,043 mechanical thrombectomies (MT), 836 tissue plasminogen activator (tPA) administrations, 36,551 ACS, and 3,925 percutaneous coronary interventions (PCI) for ACS. In February 2020, relative to February 2018 and 2019, hospitalizations with any discharge diagnosis of stroke and ACS increased by 9.8% and 12.1%, respectively, while in March 2020 they decreased 18.5% and 7.5%, relative to March 2018 and 2019. When only including hospitalizations with the primary discharge diagnosis of stroke or ACS, in March 2020 they decreased 17.6% and 25.7%, respectively. In March 2020, tPA decreased 3.3%, MT increased 18.8%, although in February 2020 it had increased 36.8%, and PCI decreased 14.7%. These decreases were observed in all Census regions.

Conclusions

Following greater recognition of the risks of COVID-19, hospitalizations with stroke and ACS were markedly diminished in a geographically diverse sample of United States hospitals. Because the most likely explanation is that some patients with stroke and ACS did not seek medical care, the underlying reasons for this decrease warrant additional study to inform public health efforts and clinical care during this and future pandemics.

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  1. SciScore for 10.1101/2020.05.07.20083386: (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

    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:
    Our study has several limitations, including that it is not a fully representative sample and that case identification with administrative and billing codes has bias. For example, we captured less MT and tPA cases compared to expected rates,17 but the methodology and sample was consistent across 6 time points, lending it validity. With the current data we are not able to explore the causal factors for the reduction and this phenomenon may be part of a greater year over year trend that is not captured in three years of data. Despite these limitations, we have taken a critical step by showing that the reduction exists beyond single centers and across Census regions. Future work will confirm these findings in larger datasets and analyze potential mediators.

    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.