Patient characteristics and predictors of mortality in 470 adults admitted to a district general hospital in England with Covid-19

  1. Joseph V Thompson
  2. Nevan Meghani
  3. Bethan M Powell
  4. Ian Newell
  5. Roanna Craven
  6. Gemma Skilton
  7. Lydia J Bagg
  8. Irha Yaqoob
  9. Michael J Dixon
  10. Eleanor J Evans
  11. Belina Kambele
  12. Asif Rehman
  13. Georges Ng Man Kwong

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Abstract

Background

Understanding risk factors for death in Covid-19 is key to providing good quality clinical care. Due to a paucity of robust evidence, we sought to assess the presenting characteristics of patients with Covid-19 and investigate factors associated with death.

Methods

Retrospective analysis of adults admitted with Covid-19 to Royal Oldham Hospital, UK. Logistic regression modelling was utilised to explore factors predicting death.

Results

470 patients were admitted, of whom 169 (36%) died. The median age was 71 years (IQR 57–82), and 255 (54.3%) were men. The most common comorbidities were hypertension (n=218, 46.4%), diabetes (n=143, 30.4%) and chronic neurological disease (n=123, 26.1%). The most frequent complications were acute kidney injury (n=157, 33.4%) and myocardial injury (n=21, 4.5%). Forty-three (9.1%) patients required intubation and ventilation, and 39 (8.3%) received non-invasive ventilation Independent risk factors for death were increasing age (OR per 10 year increase above 40 years 1.87, 95% CI 1.57-2.27), hypertension (OR 1.72, 1.10-2.70), cancer (OR 2.20, 1.27-3.81), platelets <150×10 3 /µL (OR 1.93, 1.13-3.30), C-reactive protein ≥100 µg/mL (OR 1.68, 1.05-2.68), >50% chest radiograph infiltrates, (OR 2.09, 1.16-3.77) and acute kidney injury (OR 2.60, 1.64-4.13). There was no independent association between death and gender, ethnicity, deprivation level, fever, SpO 2 /FiO 2 (oxygen saturation index), lymphopenia or other comorbidities.

Conclusions

We characterised the ‘first wave’ of patients with Covid-19 in one of England’s highest incidence areas, determining which factors predict death. These findings will inform clinical and shared decision making, including the use of respiratory support and therapeutic agents.

Summary

Increasing age, hypertension, cancer, platelets <150×10 3 /µL, CRP≥100 µg/mL, >50% chest radiograph infiltrates, and acute kidney injury predict in-hospital death from Covid-19, whilst gender, ethnicity, deprivation level, fever, SpO2/FiO2 (oxygen saturation index), lymphopenia and other comorbidities do not.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.21.20153650: (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
    Statistical analysis: Data was collected using Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    STATA 13 was used to produce summary statistics and assess associations with death in hospital or within 30 days of discharge.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    Limitations: We assessed risk factors for death present in hospitalised patients and have not investigated those not requiring admission. Consequently, there may be risk factors that confer risk for hospitalisation, and therefore a higher risk of death, that we have not identified. This is suggested, for example, by the disproportionate number of men admitted.

    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.

  2. Version published to 10.1101/2020.07.21.20153650v1 on medRxiv
  3. Version published to 10.1017/s0950268820002873