Predictors of all‐cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS‐CoV‐2

  1. Mackenzie A. Hamilton
  2. Ying Liu
  3. Andrew Calzavara
  4. Maria E. Sundaram
  5. Mohamed Djebli
  6. Dariya Darvin
  7. Stefan Baral
  8. Rafal Kustra
  9. Jeffrey C. Kwong
  10. Sharmistha Mishra

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Abstract

Background

Shared and divergent predictors of clinical severity across respiratory viruses may support clinical and community responses in the context of a novel respiratory pathogen.

Methods

We conducted a retrospective cohort study to identify predictors of 30‐day all‐cause mortality following hospitalization with influenza ( N  = 45,749; 2010‐09 to 2019‐05), respiratory syncytial virus (RSV; N  = 24 345; 2010‐09 to 2019‐04), or severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2; N  = 8988; 2020‐03 to 2020‐12; pre‐vaccine) using population‐based health administrative data from Ontario, Canada. Multivariable modified Poisson regression was used to assess associations between potential predictors and mortality. We compared the direction, magnitude, and confidence intervals of risk ratios to identify shared and divergent predictors of mortality.

Results

A total of 3186 (7.0%), 697 (2.9%), and 1880 (20.9%) patients died within 30 days of hospital admission with influenza, RSV, and SARS‐CoV‐2, respectively. Shared predictors of increased mortality included older age, male sex, residence in a long‐term care home, and chronic kidney disease. Positive associations between age and mortality were largest for patients with SARS‐CoV‐2. Few comorbidities were associated with mortality among patients with SARS‐CoV‐2 as compared with those with influenza or RSV.

Conclusions

Our findings may help identify patients at greatest risk of illness secondary to a respiratory virus, anticipate hospital resource needs, and prioritize local prevention and therapeutic strategies to communities with higher prevalence of risk factors.

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  1. Version published to 10.1111/irv.13004
  2. ScreenIT

    SciScore for 10.1101/2022.03.31.22273111: (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
    Statistical analyses: Data processing and analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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:
    When using our results to inform prioritization of services, or to develop clinical prediction tools, we must consider these limitations so that other at-risk patients do not fall through the cracks. Finally, to provide insights on shared predictors of mortality in the context of a novel, emerging pathogen, we purposefully restricted the study period of SARS-CoV-2 to exclude hospitalizations of patients vaccinated against SARS-CoV-2, or those with SARS-CoV-2 variants. Future work would benefit from comparisons of predictors of mortality among patients hospitalized with influenza, RSV or SARS-CoV-2 variants and/or breakthrough infections to determine whether shared and divergent trends remain. Our results add to the growing literature base comparing similarities and differences in clinical disease progression of patients hospitalized with influenza and SARS-CoV-2 [42–44], and have three important implications for clinical care and health systems. First, shared predictors of mortality could be used to identify, target, and prioritize hospitalized patients who are at greatest risk of death for prevention (e.g. vaccines), testing (e.g. rapid tests) and therapeutics (e.g. antivirals). Second, the underlying prevalence of shared predictors in a given geography could help prepare health systems for, and efficiently allocate health resources during, times of peak respiratory infection transmission. Finally, differences in observed predictors of mortality across the three viruses sign...

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.03.31.22273111 on medRxiv