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Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.
Leclerc, Quentin J; Fuller, Naomi M; Keogh, Ruth H; Diaz-Ordaz, Karla; Sekula, Richard; Semple, Malcolm G; Atkins, Katherine E; Procter, Simon R; Knight, Gwenan M.
  • Leclerc QJ; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK. quentin.leclerc@lshtm.ac.uk.
  • Fuller NM; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK.
  • Keogh RH; Department of Medical Statistics, Faculty of Epidemiology & Population Health, Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK.
  • Diaz-Ordaz K; Department of Medical Statistics, Faculty of Epidemiology & Population Health, Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK.
  • Sekula R; University College London Hospitals NHS Foundation Trust, London, UK.
  • Semple MG; NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK.
  • Atkins KE; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK.
  • Procter SR; Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
  • Knight GM; Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK.
BMC Health Serv Res ; 21(1): 566, 2021 Jun 09.
Article in English | MEDLINE | ID: covidwho-1262505
ABSTRACT

BACKGROUND:

Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.

METHODS:

We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.

RESULTS:

In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.

CONCLUSIONS:

We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Bed Occupancy / COVID-19 Type of study: Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: Europa Language: English Journal: BMC Health Serv Res Journal subject: Health Services Research Year: 2021 Document Type: Article Affiliation country: S12913-021-06509-x

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Bed Occupancy / COVID-19 Type of study: Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: Europa Language: English Journal: BMC Health Serv Res Journal subject: Health Services Research Year: 2021 Document Type: Article Affiliation country: S12913-021-06509-x