Length of Stay in ICU of Covid-19 Patients in England, March - May 2020

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Abstract

Introduction: Length of Stay (LoS) in Intensive Care Units (ICUs) is an important measure for planning beds capacity during the Covid-19 pandemic.
However, as the pandemic progresses and we learn more about the disease, treatment and subsequent LoS in ICU may change.
Objectives: To investigate the LoS in ICUs in England associated with Covid-19, correcting for censoring, and to evaluate the effect of known predictors of Covid-19 outcomes on ICU LoS.
Data sources: We used retrospective data on Covid-19 patients, admitted to ICU between 6 March and 24 May, from the \Covid-19 Hospitalisation
in England Surveillance System" (CHESS) database, collected daily from England's National Health Service, and collated by Public Health England.
Methods: We used Accelerated Failure Time survival models with Weibull and log-normal distributional assumptions to investigate the effect of predictors, which are known to be associated with poor Covid-19 outcomes, on the LoS in ICU.
Results: Patients admitted before 25 March had significantly longer LoS in ICU (mean=18.4 days, median=12), controlling for age, sex, whether the
patient received Extracorporeal Membrane Oxygenation, and a co-morbid risk factors score, compared with the period after 7 April (mean=15.4, median= 10). The periods of admission reflected the changes in the ICU admission policy in England. Patients aged 50-65 had the longest LoS, while higher co-morbid risk factors score led to shorter LoS. Sex and ethnicity were not associated with ICU LoS.
Conclusions: The skew of the predicted LoS suggests that a mean LoS, as compared with median, might be better suited as a measure used to assess and plan ICU beds capacity. This is important for the ongoing second and any future waves of Covid-19 cases and potential pressure on the ICU resources. Also, changes in the ICU admission policy are likely to be confounded with improvements in clinical knowledge of Covid-19.

Bibliographical metadata

Original languageEnglish
JournalInternational Journal of Population Data Science
DOIs
Publication statusAccepted/In press - 13 Jan 2021