Prediction of the need for intensive oxygen supplementation during hospitalisation among subjects with COVID-19 admitted to an academic health system in Texas: a retrospective cohort study and multivariable regression model

  1. John W Davis
  2. Beilin Wang
  3. Ewa Tomczak
  4. Chia Chi-Fu
  5. Wissam Harmouch
  6. David Reynoso
  7. Philip Keiser
  8. Miguel Mauricio Cabada

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

SARS-CoV-2 has caused a pandemic claiming more than 4 million lives worldwide. Overwhelming COVID-19 respiratory failure placed tremendous demands on healthcare systems increasing the death toll. Cost-effective prognostic tools to characterise the likelihood of patients with COVID-19 to progress to severe hypoxemic respiratory failure are still needed.

Design

We conducted a retrospective cohort study to develop a model using demographic and clinical data collected in the first 12 hours of admission to explore associations with severe hypoxemic respiratory failure in unvaccinated and hospitalised patients with COVID-19.

Setting

University-based healthcare system including six hospitals located in the Galveston, Brazoria and Harris counties of Texas.

Participants

Adult patients diagnosed with COVID-19 and admitted to one of six hospitals between 19 March and 30 June 2020.

Primary outcome

The primary outcome was defined as reaching a WHO ordinal scale between 6 and 9 at any time during admission, which corresponded to severe hypoxemic respiratory failure requiring high-flow oxygen supplementation or mechanical ventilation.

Results

We included 329 participants in the model cohort and 62 (18.8%) met the primary outcome. Our multivariable regression model found that lactate dehydrogenase (OR 2.36), Quick Sequential Organ Failure Assessment score (OR 2.26) and neutrophil to lymphocyte ratio (OR 1.15) were significant predictors of severe disease. The final model showed an area under the curve of 0.84. The sensitivity analysis and point of influence analysis did not reveal inconsistencies.

Conclusions

Our study suggests that a combination of accessible demographic and clinical information collected on admission may predict the progression to severe COVID-19 among adult patients with mild and moderate disease. This model requires external validation prior to its use.

Article activity feed

  1. Version published to 10.1136/bmjopen-2021-058238
  2. ScreenIT

    SciScore for 10.1101/2021.11.05.21265970: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study protocol was approved by UTMB Institutional Review Board (20-0126) and the Texas Department of Criminal Justice (
    Sex as a biological variablenot detected.
    RandomizationEighty-nine randomly selected charts underwent evaluation by the principal investigators and the data extraction personnel.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analysis: The REDCap dataset was downloaded to a database on SAS (Version 9.4
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    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:
    Our study has several limitations to acknowledge. The troponin was not included in our model because all subjects with abnormal troponin met the primary outcome. Elevated troponin suggested myocardial injury which can be due to a direct effect from SARS-CoV-2 infection and/or a complication from sepsis and the inflammatory response described in COVID 19. The role of troponin as a predictor of COVID-19 associated mortality has been suggested in other studies [32, 33]. However, larger studies are necessary to evaluate their role in predicting severe COVID-19 respiratory failure. Our cohort was constructed prior to introduction of COVID-19 vaccination and therapeutic interventions such as dexamethasone or remdesivir [34]. The validation of our prediction model in the rest of our study population and in more recent cohorts after the emergence of new SARS-CoV-2 variants will be needed to assess its robustness. Our prediction model could contribute to the literature providing tools for clinicians, however none of the variables in our model are specific to SARS-CoV-2 infection and further studies including other populations will be needed for validation.

    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.

  3. Version published to 10.1101/2021.11.05.21265970 on medRxiv