A Home-Treatment Algorithm Based on Anti-inflammatory Drugs to Prevent Hospitalization of Patients With Early COVID-19: A Matched-Cohort Study (COVER 2)

  1. Elena Consolaro
  2. Fredy Suter
  3. Nadia Rubis
  4. Stefania Pedroni
  5. Chiara Moroni
  6. Elena Pastò
  7. Maria Vittoria Paganini
  8. Grazia Pravettoni
  9. Umberto Cantarelli
  10. Norberto Perico
  11. Annalisa Perna
  12. Tobia Peracchi
  13. Piero Ruggenenti
  14. Giuseppe Remuzzi

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Abstract

While considerable success has been achieved in the management of patients hospitalized with severe coronavirus disease 2019 (COVID-19), far less progress has been made with early outpatient treatment. We assessed whether the implementation of a home treatment algorithm—designed based on a pathophysiologic and pharmacologic rationale—and including non-steroidal anti-inflammatory drugs, especially relatively selective cyclooxygenase-2 inhibitors and, when needed, corticosteroids, anticoagulants, oxygen therapy and antibiotics—at the very onset of mild COVID-19 symptoms could effectively reduce hospital admissions.

Methods

This fully academic, matched-cohort study evaluated outcomes in 108 consecutive consenting patients with mild COVID-19, managed at home by their family doctors between January 2021 and May 2021, according to the proposed treatment algorithm and in 108 age-, sex-, and comorbidities-matched patients on other therapeutic schedules (ClinicalTrials.gov: NCT04854824). The primary outcome was COVID-19-related hospitalization. Analyses were by intention-to-treat.

Results

One (0.9%) patient in the “recommended” cohort and 12 (11.1%) in the “control” cohort were admitted to hospital ( P = 0.0136). The proposed algorithm reduced the cumulative length of hospital stays by 85% (from 141 to 19 days) as well as related costs (from €60.316 to €9.058). Only 9.8 patients needed to be treated with the recommended algorithm to prevent one hospitalization event. The rate of resolution of major symptoms was numerically—but not significantly—higher in the “recommended” than in the “control” cohort (97.2 vs. 93.5%, respectively; P = 0.322). Other symptoms lingered in a smaller proportion of patients in the “recommended” than in the “control” cohort (20.4 vs. 63.9%, respectively; P < 0.001), and for a shorter period.

Conclusion

The adoption of the proposed outpatient treatment algorithm during the early, mild phase of COVID-19 reduced the incidence of subsequent hospitalization and related costs.

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  1. Version published to 10.3389/fmed.2022.785785
  2. ScreenIT

    SciScore for 10.1101/2021.09.29.21264298: (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
    Moreover, to verify the robustness of the above-described propensity score method, a further exploratory approach was performed by using the ‘teffects iptw’ STATA command to estimate the average treatment effect from observational data by inverse probability treatment weighting (IPTW), including 3368 patients in the control ORIGIN database.
    STATA
    suggested: (Stata, RRID:SCR_012763)
    All analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, NC) and Stata 15 (StataCorp, College Station, TX).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    Subjects in the ‘control’ cohort (from the ORIGIN database) signed a consent form to participate in the ORIGIN study, which also explicitly included consent to use their data for future studies, such as COVER 2.
    ORIGIN
    suggested: (Origin, RRID:SCR_014212)

    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 non-randomized design is a major limitation of the study, which is observational in nature. Nonetheless, comparative analysis of patient cohorts in everyday clinical practice with adjustments for possible confounding biases may offer a suitable alternative to the recommended clinical trials to evaluate the effectiveness of different therapeutic regimens (53,54). Moreover, the matched-cohort study protocol with a statistical plan was predefined and the analyses were performed accordingly. There is the additional limitation that the collection of outcome information in the ‘control’ cohort was through interviews and questionnaires related to events that happened before the survey. This was not the case for the ‘recommended algorithm’ cohort, where data were gathered by family doctors. However, in both cohorts the date of hospital admission (primary outcome) and data about the course of hospitalization were well documented in the hospital discharge letter. Moreover, further evidence of the observed difference between the hospital admission rates for the two cohorts is offered by the results of the additional explorative analysis of 3368 patients in the control ORIGIN database, which confirmed a significantly lower rate of hospitalization in the ‘recommended algorithm’ than in the ‘control’ group. On the other hand, the COVER 2 study formally tested outcomes for COVID-19 patients managed by their family physicians according to a therapy recommendation algorithm that targets e...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04854824Not yet recruitingA Simple Approach to Prevent Hospitalization for COVID-19 Pa…
    NCT04799834RecruitingGenotype and Susceptibility to COVID-19

    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 10.1101/2021.09.29.21264298 on medRxiv