Effect of the COVID-19 pandemic in stroke code activations in the region of Madrid

  1. Nicolás Riera-López
  2. Blanca Fuentes
  3. Jorge Rodríguez-Pardo de Donlebún

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Abstract

Acute sstroke is the most common time-dependent disease attended in the emergency medical service (EMS) of Madrid (SUMMA 112). Community of Madrid has been one of the most affected regions in Spain by the coronavirus disease 2019 (COVID-19) pandemic. A significant reduction in acute sstroke hospital admissions has been reported during the COVID-19 pandemic compared to the same period 1 year before. As international clinical practice guidelines support those patients with suspected acute stroke should be accessed via EMS, it is important to know whether the pandemic has jeopardized urgent pre-hospital stroke care, the first medical contact for most patients. We aimed to examine the impact of the COVID-19 in stroke codes (SC) in our EMS among 3 periods of time: the COVID-19 period, the same period the year before, and the 2019-2020 seasonal influenza period.

We compared the SC frequency among the periods with high cumulative infection rate (above the median of the series) of the first wave of COVID-19, seasonal influenza, and also with the same period of the year before.One thousand one hundred thirty SC were attended during the 3 periods. No significant reduction in SC was found during the COVID-19 pandemic. The reduction of hospital admissions might be attributable to patients attending the hospital by their means. The maximum SC workload seen during seasonal influenza has not been reached during the pandemic. We detected a nonsignificant deviation from the SC protocol, with a slight increase in hospitals’ transfers to hospitals without stroke units.

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  1. Version published to 10.1097/md.0000000000027634
  2. Version published to 10.1101/2020.12.10.20246892v3 on medRxiv
  3. Version published to 10.1101/2020.12.10.20246892v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.12.10.20246892: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We used Rstudio for statistical analyses and Microsoft Excel for the graphs.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    The main limitation of this study is that it is focused on calls to 112, and the information on other AS patients is lacking. We de did not find evidence of a significant reduction of SC during the COVID-19 pandemic compared with the same period of the year before. The number of SC was significantly lower during the COVID-19 period than the 2019-2020 seasonal influenza period, thus SC seems to be capable of operating correctly, even with a high level of health system saturation.

    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

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  5. Version published to 10.1101/2020.12.10.20246892v1 on medRxiv