A Learning Health System Randomized Trial of Monoclonal Antibodies for Covid-19

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Abstract

Background

Neutralizing monoclonal antibodies (mAb) targeting SARS-CoV-2 decrease hospitalization and death in patients with mild to moderate Covid-19. Yet, their clinical use is limited, and comparative effectiveness is unknown.

Methods

We present the first results of an ongoing, learning health system adaptive platform trial to expand mAb treatment to all eligible patients and evaluate the comparative effectiveness of available mAbs. The trial launched March 10, 2021. Results are reported as of June 25, 2021 due to the U.S. federal decision to pause distribution of bamlanivimab-etesevimab; patient follow-up concluded on July 23, 2021. Patients referred for mAb who met Emergency Use Authorization criteria were provided a random mAb allocation of bamlanivimab, bamlanivimab-etesevimab, or casirivimab-imdevimab with a therapeutic interchange policy. The primary outcome was hospital-free days (days alive and free of hospital) within 28 days, where patients who died were assigned -1 day. The primary analysis was a Bayesian cumulative logistic model of all patients treated at an infusion center or emergency department, adjusting for treatment location, age, sex, and time. Inferiority was defined as a 99% posterior probability of an odds ratio < 1. Equivalence was defined as a 95% posterior probability that the odds ratio is within a given bound.

Results

Prior to trial launch, 3.1% (502) of 16,345 patients who were potentially eligible by an automated electronic health record (EHR) screen received mAb. During the trial period, 23.2% (1,201) of 5,173 EHR-screen eligible patients were treated, a 7.5-fold increase. After including additional referred patients from outside the health system, a total of 1,935 study patients received mAb therapy (128 bamlanivimab, 885 bamlanivimab-etesevimab, 922 casirivimab-imdevimab). Mean age ranged from 55 to 57 years, half were female (range, 53% to 54%), and 17% were Black (range, 12% to 19%). Median hospital–free days were 28 (IQR, 28 to 28) for each mAb group. Hospitalization varied between groups (bamlanivimab, 12.5%; bamlanivimab-etesevimab, 14.7%, casirivimab-imdevimab, 14.3%). Relative to casirivimab-imdevimab, the median adjusted odds ratios were 0.58 (95% credible interval (CI), 0.30 to 1.16) and 0.94 (95% CI, 0.72 to 1.24) for the bamlanivimab and bamlanivimab-etesevimab groups, respectively. These odds ratios yielded 91% and 94% probabilities of inferiority of bamlanivimab versus bamlanivimab-etesevimab and casirivimab-imdevimab respectively, and an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab, at the prespecified odds ratio bound of 0.25. Twenty-one infusion-related adverse events occurred in 0% (0/128), 1.4% (12/885), and 1.0% (9/922) of patients treated with bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab, respectively.

Conclusion

In non-hospitalized patients with mild to moderate Covid-19, bamlanivimab, compared to bamlanivimab-etesevimab and casirivimab-imdevimab, resulted in 91% and 94% probabilities of inferiority with regards to odds of improvement in hospital-free days within 28 days. There was an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab at an odds ratio bound of 0.25. However, the trial was unblinded early due to federal distribution decisions, and no mAb met prespecified criteria for statistical inferiority or equivalence. ( ClinicalTrials.gov , NCT04790786 ).

Article activity feed

  1. SciScore for 10.1101/2021.09.03.21262551: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The University of Pittsburgh Institutional Review Board considered provision of mAb therapy quality improvement and only the additional data collection and analyses represented research (STUDY21020179).
    Consent: Patients provided verbal consent to receive mAb therapy as part of routine care.
    Sex as a biological variablenot detected.
    Randomization7-9 Trial Design and Oversight: OPTIMISE-C19 is an open-label, pragmatic, comparative effectiveness, platform trial with response-adaptive randomization.
    BlindingAdverse event severity was adjudicated blinded to mAb type.
    Power Analysisnot detected.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Race was derived from registration system data using fixed categories consistent with the Centers for Medicare & Medicaid Services EHR meaningful use dataset and the AMA Manual of Style.19,20 Pre-specified categories included non-Hispanic Black, non-Hispanic White, and Other.
    non-Hispanic White
    suggested: None
    Software and Algorithms
    SentencesResources
    25 An unblinded statistical analysis committee conducted interim and final analyses with R version 4.0.5 using the RStan package version 2.21.0 (R Foundation, Vienna, Austria) and reported results to the UPMC Chief Medical Officer who functioned in a data and safety monitor role for the study.
    RStan
    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:
    The trial also has limitations. First, the results are presented before any prespecified internal trigger was reached. Nonetheless, to our knowledge, this trial represents the largest randomized comparative effectiveness data of mAb for Covid-19. Second, the absence of patient-level variant data limited ability to directly assess comparative effectiveness relative to variant strains. Alpha was also the dominant variant during the majority of the trial. Using regional data as a surrogate for variant data in the Pennsylvania population over time, we found no difference in treatment effect over time. Third, we primarily relied on UPMC EHR data to capture death and hospitalization, and patients may have accessed care outside our health system after mAb treatment. We conducted direct-to patient calls and national death registry queries to address this concern. Fourth, the EHR eligibility screen identified most, but not all EUA risk factors, and could not identify if a patient was symptomatic.

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

    IdentifierStatusTitle
    NCT04790786RecruitingUPMC OPTIMISE-C19 Trial, a COVID-19 Study
    NCT04497987CompletedA Study of LY3819253 (LY-CoV555) and LY3832479 (LY-CoV016) i…
    NCT04545060Active, not recruitingVIR-7831 for the Early Treatment of COVID-19 in Outpatients


    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.
    • Thank you for including a protocol registration statement.

    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.