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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.
Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England
Leclerc Q. J.;Fuller N. M.;Keogh R. H.;Diaz-Ordaz K.;Sekula R.;Semple M. G.;Baillie J. K.;Openshaw P. J. M.;Carson G.;Alex B.;Bach B.;Barclay W. S.;Bogaert D.;Chand M.;Cooke G. S.;Docherty A. B.;Dunning J.;da Silva Filipe A.;Fletcher T.;Green C. A.;Harrison E. M.;Hiscox J. A.;Ho A. Y. W.;Horby P. W.;Ijaz S.;Khoo S.;Klenerman P.;Law A.;Lim W. S.;Mentzer A. J.;Merson L.;Meynert A. M.;Noursadeghi M.;Moore S. C.;Palmarini M.;Paxton W. A.;Pollakis G.;Price N.;Rambaut A.;Robertson D. L.;Russell C. D.;Sancho-Shimizu V.;Scott J. T.;de Silva T.;Sigfrid L.;Solomon T.;Sriskandan S.;Stuart D.;Summers C.;Tedder R. S.;Thomson E. C.;Thompson A. A. R.;Thwaites R. S.;Turtle L. C. W.;Zambon M.;Hardwick H.;Donohue C.;Lyons R.;Griffiths F.;Oosthuyzen W.;Norman L.;Pius R.;Drake T. M.;Fairfield C. J.;Knight S.;Mclean K. A.;Murphy D.;Shaw C. A.;Dalton J.;Lee J.;Plotkin D.;Girvan M.;Saviciute E.;Roberts S.;Harrison J.;Marsh L.;Connor M.;Halpin S.;Jackson C.;Gamble C.;Petersen C.;Mullaney S.;Leeming G.;Wham M.;Clohisey S.;Hendry R.;Scott-Brown J.;Greenhalf W.;Shaw V.;McDonald S.;Keating S.;Ahmed K. A.;Armstrong J. A.;Ashworth M.;Asiimwe I. G.;Bakshi S.;Barlow S. L.;Booth L.;Brennan B.;Bullock K.;Catterall B. W. A.;Clark J. J.;Clarke E. A.;Cole S.;Cooper L.;Cox H.;Davis C.;Dincarslan O.;Dunn C.;Dyer P.;Elliott A.;Evans A.;Finch L.;Fisher L. W. S.;Foster T.;Garcia-Dorival I.;Gunning P.;Hartley C.;Ho A.;Jensen R. L.;Jones C. B.;Jones T. R.;Khandaker S.;King K.;Kiy R. T.;Koukorava C.;Lake A.;Lant S.;Latawiec D.;Lavelle-Langham L.;Lefteri D.;Lett L.;Livoti L. A.;Mancini M.;McDonald S.;McEvoy L.;McLauchlan J.;Metelmann S.;Miah N. S.;Middleton J.;Mitchell J.;Murphy E. G.;Penrice-Randal R.;Pilgrim J.;Prince T.;Reynolds W.;Ridley P. M.;Sales D.;Shaw V. E.;Shears R. K.;Small B.;Subramaniam K. S.;Szemiel A.;Taggart A.;Tanianis-Hughes J.;Thomas J.;Trochu E.;van Tonder L.;Wilcock E.;Zhang J. E.;Adeniji K.;Agranoff D.;Agwuh K.;Ail D.;Alegria A.;Angus B.;Ashish A.;Atkinson D.;Bari S.;Barlow G.;Barnass S.;Barrett N.;Bassford C.;Baxter D.;Beadsworth M.;Bernatoniene J.;Berridge J.;Best N.;Bothma P.;Brealey D.;Brittain-Long R.;Bulteel N.;Burden T.;Burtenshaw A.;Caruth V.;Chadwick D.;Chambler D.;Chee N.;Child J.;Chukkambotla S.;Clark T.;Collini P.;Cosgrove C.;Cupitt J.;Cutino-Moguel M. -T.;Dark P.;Dawson C.;Dervisevic S.;Donnison P.;Douthwaite S.;DuRand I.;Dushianthan A.;Dyer T.;Evans C.;Eziefula C.;Fegan C.;Finn A.;Fullerton D.;Garg S.;Garg S.;Garg A.;Gkrania-Klotsas E.;Godden J.;Goldsmith A.;Graham C.;Hardy E.;Hartshorn S.;Harvey D.;Havalda P.;Hawcutt D. B.;Hobrok M.;Hodgson L.;Hormis A.;Jacobs M.;Jain S.;Jennings P.;Kaliappan A.;Kasipandian V.;Kegg S.;Kelsey M.;Kendall J.;Kerrison C.;Kerslake I.;Koch O.;Koduri G.;Koshy G.;Laha S.;Laird S.;Larkin S.;Leiner T.;Lillie P.;Limb J.;Linnett V.;Little J.;MacMahon M.;MacNaughton E.;Mankregod R.;Masson H.;Matovu E.;McCullough K.;McEwen R.;Meda M.;Mills G.;Minton J.;Mirfenderesky M.;Mohandas K.;Mok Q.;Moon J.;Moore E.;Morgan P.;Morris C.;Mortimore K.;Moses S.;Mpenge M.;Mulla R.;Murphy M.;Nagel M.;Nagarajan T.;Nelson M.;Otahal I.;Pais M.;Panchatsharam S.;Paraiso H.;Patel B.;Pattison N.;Pepperell J.;Peters M.;Phull M.;Pintus S.;Pooni J. S.;Post F.;Price D.;Prout R.;Rae N.;Reschreiter H.;Reynolds T.;Richardson N.;Roberts M.;Roberts D.;Rose A.;Rousseau G.;Ryan B.;Saluja T.;Shah A.;Shanmuga P.;Sharma A.;Shawcross A.;Sizer J.;Shankar-Hari M.;Smith R.;Snelson C.;Spittle N.;Staines N.;Stambach T.;Stewart R.;Subudhi P.;Szakmany T.;Tatham K.;Thomas J.;Thompson C.;Thompson R.;Tridente A.;Tupper-Carey D.;Twagira M.;Ustianowski A.;Vallotton N.;Vincent-Smith L.;Visuvanathan S.;Vuylsteke A.;Waddy S.;Wake R.;Walden A.;Welters I.;Whitehouse T.;Whittaker P.;Whittington A.;Wijesinghe M.;Williams M.;Wilson L.;Wilson S.;Winchester S.;Wiselka M.;Wolverson A.;Wooton D. G.;Workman A.;Yates B.;Young P.;Quaife M.;Jarvis C. I.;Meakin S. R.;Quilty B. J.;Prem K.;Villabona-Arenas C. J.;Sun F. Y.;Abbas K.;Auzenbergs M.;Gimma A.;Tully D. C.;Sherratt K.;Rosello A.;Davies N. G.;Liu Y.;Lowe R.;Gibbs H. P.;Waterlow N. R.;Edmunds W. J.;Simons D.;Medley G.;Munday J. D.;Flasche S.;Sandmann F. G.;Showering A.;Eggo R. M.;Chan Y. -W. D.;Pearson C. A. B.;Kucharski A. J.;Foss A. M.;Russell T. W.;Bosse N. I.;Jit M.;Abbott S.;Williams J.;Endo A.;Clifford S.;Gore-Langton G. R.;Klepac P.;Brady O.;Hellewell J.;Funk S.;van Zandvoort K.;Barnard R. C.;Nightingale E. S.;Jombart T.;Atkins K. E.;Procter S. R.;Knight G. M.
2021-01-01
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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1468777
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
