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dc.contributor.authorVekaria, Bindu; orcid: 0000-0001-6605-7956; email: bindu.vekaria@manchester.ac.uk
dc.contributor.authorOverton, Christopher; email: christopher.overton@manchester.ac.uk
dc.contributor.authorWiśniowski, Arkadiusz; email: a.wisniowski@manchester.ac.uk
dc.contributor.authorAhmad, Shazaad
dc.contributor.authorAparicio-Castro, Andrea
dc.contributor.authorCurran-Sebastian, Jacob
dc.contributor.authorEddleston, Jane
dc.contributor.authorHanley, Neil A
dc.contributor.authorHouse, Thomas
dc.contributor.authorKim, Jihye
dc.contributor.authorOlsen, Wendy
dc.contributor.authorPampaka, Maria
dc.contributor.authorPellis, Lorenzo
dc.contributor.authorRuiz, Diego Perez
dc.contributor.authorSchofield, John
dc.contributor.authorShryane, Nick
dc.contributor.authorElliot, Mark J
dc.date.accessioned2021-08-24T00:37:06Z
dc.date.available2021-08-24T00:37:06Z
dc.date.issued2021-07-22
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/625671/article.pdf?sequence=2
dc.identifier.citationBMC infectious diseases, volume 21, issue 1, page 700
dc.identifier.urihttp://hdl.handle.net/10034/625671
dc.descriptionFrom Europe PMC via Jisc Publications Router
dc.descriptionHistory: ppub 2021-07-01, epub 2021-07-22
dc.descriptionPublication status: Published
dc.descriptionFunder: Medical Research Council; Grant(s): MR/R502236/1
dc.descriptionFunder: Royal Society; Grant(s): 202562/Z/16/Z, INF/R2/180067
dc.description.abstract<h4>Background</h4>Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data.<h4>Method</h4>On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy.<h4>Results</h4>All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days.<h4>Conclusions</h4>Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.
dc.languageeng
dc.rightsLicence for this article: cc by
dc.sourceissn: 1471-2334
dc.sourcenlmid: 100968551
dc.sourceessn: 1471-2334
dc.subjectSurvival analysis
dc.subjectEngland
dc.subjectLength Of Stay
dc.subjectCovid-19
dc.subjectHumans
dc.subjectLength of Stay
dc.subjectAged
dc.subjectMiddle Aged
dc.subjectHospital Bed Capacity
dc.subjectIntensive Care Units
dc.subjectHospital Planning
dc.subjectFemale
dc.subjectMale
dc.subjectData Analysis
dc.subjectCOVID-19
dc.titleHospital length of stay for COVID-19 patients: Data-driven methods for forward planning.
dc.typearticle
dc.date.updated2021-08-24T00:37:06Z


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