About: COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

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About: COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care
Creator	Wood, Richard Thomas, Matthew Bourdeaux, Christopher Mcwilliams, Christopher Vasilakis, Christos
Source	Medline; PMC
abstract	Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ‘capacity-dependent’ deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ‘capacity-independent’ deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10729-020-09511-7) contains supplementary material, which is available to authorized users.
has issue date	2020-07-08(xsd:dateTime)
bibo:doi	10.1007/s10729-020-09511-7
bibo:pmid	32642878
has license	no-cc
sha1sum (hex)	71b4fc38346170ccd17363d4e782a5dd57e0f3cb
schema:url	https://doi.org/10.1007/s10729-020-09511-7
resource representing a document's title	COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care
has PubMed Central identifier	PMC7341703
has PubMed identifier	32642878
schema:publication	Health Care Manag Sci
resource representing a document's body	covid:71b4fc38346170ccd17363d4e782a5dd57e0f3cb#body_text
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