Describing COVID-19 Patients During The First Two Months of Paxlovid (Nirmatrelvir/Ritonavir) Initiation in a Large HMO in Israel

  1. Lilac Tene
  2. Gabriel Chodick
  3. Noga Fallach
  4. Wajeeha Ansari
  5. Tal Distelman-Menachem
  6. Yasmin Maor

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Abstract

Title

Describing COVID-19 Patients During The First Two Months of Paxlovid (Nirmatrelvir/Ritonavir) Initiation in a Large HMO in Israel

Objective

The objective of this feasibility study was to assess the number of patients that could be included in a future Real World Evidence study, which would be designed to explore the impact of Paxlovid (nirmatrelvir/ritonavir) on patient outcomes and healthcare resource utilization (HCRU). We also intend to assess the comparability of the patients who were treated with Paxlovid versus patients who did not receive the treatment, either because they declined any COVID-19 treatment or were diagnosed with COVID-19 prior to Paxlovid availability.

Methods

This retrospective observational secondary data study used data from the Maccabi Healthcare Services database during the identification period of June 1, 2021, to February 28, 2022. The study population included patients with at least one positive SARS-CoV-2 RT-PCR test, or a formal rapid antigen test for SARS-CoV-2, during the identification period, the date of which also served as the COVID-19 diagnosis date. We then divided the study population into the following cohorts: Pre-Paxlovid Time Period and Paxlovid Time Period, which was further split into Paxlovid Treated and Paxlovid Untreated.

Results

Application of inclusion and exclusion criteria to the study population rendered 20,284 patients in the Pre-Paxlovid Time Period cohort and 5,542 in the Paxlovid Time Period cohort that were eligible to receive Paxlovid. This resulted in 3,714 in the Paxlovid Treated and 1,810 in the Paxlovid Untreated cohorts.

Conclusions

This RWE feasibility study of patients with a positive test for COVID-19 between June 1, 2021 to February 28, 2022 illustrates potential comparability between cohorts, as described by their demographics and characteristics.

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  1. ScreenIT

    SciScore for 10.1101/2022.05.02.22274586: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All data analysis was performed by Maccabi Healthcare Services using Statistical Software – SAS v9.4.
    Maccabi Healthcare
    suggested: None
    Statistical Software
    suggested: (Free Statistical Software , RRID:SCR_013834)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. Version published to 10.1101/2022.05.02.22274586v1 on medRxiv