COVID-19 related outcomes for hospitalised older people at risk of frailty

  1. Rob Konstant-Hambling
  2. Towhid Imam
  3. Rhiannon K Owen
  4. Andrew Street
  5. Laia Maynou
  6. Suzanne Arkill
  7. Simon Conroy

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Abstract

Background

The COVID-19 pandemic has had a disproportionate impact upon older people. Frailty is being used to further refine the risk of poor outcomes in hospitalised older people. But studies to date on COVID related outcomes using frailty scales have reported inconsistent findings. We plan a retrospective cohort study using national data sources across England. The objectives are:

  • To determine if there is there an association between COVID-19 infection (virus identified), frailty risk (measured using the Hospital Frailty Risk Score - HFRS) and all-cause mortality.

  • To evaluate the association between HFRS in people with COVID-19 infection (virus identified), and the following secondary outcomes: hospital length of stay, critical care (entry to critical care, critical care length of stay and deaths in or following a critical care stay).

  • To determine if there is there an association between COVID-19 infection (virus identified), frailty risk (measured using the HFRS) and costs captured using Healthcare Resource Group tariffs.

    • Methods

    This will be a retrospective cohort study using the NHS England Secondary Uses Service (SUS) electronic database, which records hospital activity and outcomes for all patients admitted to National Health Service hospitals in England. The analyses will use data relating to the index hospital presentation, this being the individual’s first emergency presentation during the study period for which they received a COVID-19 test. The primary and secondary outcomes will be constructed for the index admission. The analyses will control for differences in individual characteristics, using a set of risk adjusters including frailty, age, sex, ethnicity, deprivation, Charlson Comorbidity Index, number of previous admissions, number of (surgical) procedures, Ambulatory Care Sensitive Conditions (ACSCs) and COVID-19 status.

    Results

    Baseline characteristics will be reported using descriptive statistics. Mortality will be described using survival analysis, displayed as Kaplan Meier plots. A Cox proportional hazards model using robust standard errors to account for multiple observations (arising from readmissions) of the same individual will be fitted. The analyses will control for differences in individual characteristics, using a set of risk adjusters including frailty (Hospital Frailty Risk Score (HFRS)), age, sex, ethnicity, deprivation, Charlson Comorbidity Index, number of previous admissions, number of (surgical) procedures, Ambulatory Care Sensitive Conditions (ACSCs) and COVID-19 status (ICD-10 codes). Adjusted and unadjusted hazard ratios will be used to compare the rate of death for those with and without confirmed COVID-19, at different HFRS levels. We will test for an interaction between COVID-19 status and HFRS. A logit model will be implemented to analyse the secondary outcomes of admission to critical care mortality at 30 days, and mortality in critical care. For length of stay in hospital and in critical care, Poisson or negative binomial regression models will be fitted depending upon the dispersion.

    Impact

    The results of the study will inform clinicians about how best to use the frailty concept when assessing older people with COVID-19, for example in national guidelines that the study team have been involved in preparing: https://www.criticalcarenice.org.uk/ .

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    1. ScreenIT

      SciScore for 10.1101/2020.11.16.20232447: (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

      No key resources detected.

      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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

      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

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    2. Version published to 10.1101/2020.11.16.20232447v1 on medRxiv