Real-world Effect of Monoclonal Antibody Treatment in COVID-19 Patients in a Diverse Population in the United States

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Abstract

Background

Monoclonal antibodies (mAbs) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a promising treatment for limiting the progression of coronavirus disease 2019 (COVID-19) and decreasing strain on hospitals. Their use, however, remains limited, particularly in disadvantaged populations.

Methods

Electronic health records were reviewed from SARS-CoV-2 patients at a single medical center in the United States that initiated mAb infusions in January 2021 with the support of the US Department of Health and Human Services’ National Disaster Medical System. Patients who received mAbs were compared with untreated patients from the time period before mAb availability who met eligibility criteria for mAb treatment. We used logistic regression to measure the effect of mAb treatment on the risk of hospitalization or emergency department (ED) visit within 30 days of laboratory-confirmed COVID-19.

Results

Of 598 COVID-19 patients, 270 (45%) received bamlanivimab and 328 (55%) were untreated. Two hundred thirty-one patients (39%) were Hispanic. Among treated patients, 5/270 (1.9%) presented to the ED or required hospitalization within 30 days of a positive SARS-CoV-2 test, compared with 39/328 (12%) untreated patients (P < .001). After adjusting for age, gender, and comorbidities, the risk of ED visit or hospitalization was 82% lower in mAb-treated patients compared with untreated patients (95% CI, 56%–94%).

Conclusions

In this diverse, real-world COVID-19 patient population, mAb treatment significantly decreased the risk of subsequent ED visit or hospitalization. Broader treatment with mAbs, including in disadvantaged patient populations, can decrease the burden on hospitals and should be facilitated in all populations in the United States to ensure health equity.

Article activity feed

  1. SciScore for 10.1101/2021.04.08.21254705: (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

    Software and Algorithms
    SentencesResources
    Race categories were defined as American Indian/Alaskan Native, Asian, Black, Hawaiian/Pacific Islander, White, and other.
    Islander
    suggested: (Islander, RRID:SCR_007758)

    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:
    These limitations could be further evaluated in a larger, prospective, observational study. While individuals at increased risk of SARS-CoV-2 infection and severe disease are prioritized for vaccination in most U.S. states, therapeutic options such as mAb infusions remain a necessity for those who remain unvaccinated due to contraindications or vaccine hesitancy [28,29]. Although viral variants are being discovered that are poorly neutralized by several mAbs in laboratory studies [30,31], suggesting reduced effectiveness in patient populations, relatively minor adjustments to the currently available mAb products can counter these changes. Additionally, the FDA has issued guidance encouraging use of existing formulations, platforms, and clinical protocols to facilitate expedited review and rapid introduction of these modified mAb products to general public [32]. In summary, we demonstrated that mAb treatment with bamlanivimab was associated with an approximately 80% reduction in the risk of medical visits among a diverse COVID-19 patient population under real-world conditions. Increasing availability and utilization of novel COVID-19 therapeutics may improve patient outcomes, reduce burden on the health system, and contribute to increased health equity in the United States.

    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.