Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward PlanningCitation formats

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Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning. / Vekaria, Bindu (Corresponding); Overton, Christopher; Wiśniowski, Arkadiusz et al.

In: BMC Infectious Diseases, Vol. 21, 700 , 22.07.2021.

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Vekaria, Bindu ; Overton, Christopher ; Wiśniowski, Arkadiusz et al. / Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning. In: BMC Infectious Diseases. 2021 ; Vol. 21.

Bibtex

@article{7df5a4a974e84c86b4dc4fd733bfae12,
title = "Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning",
abstract = "Background: 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.Method: 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.Results: 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 9.3 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 22.8 days.Conclusions: 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 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.",
author = "Bindu Vekaria and Christopher Overton and Arkadiusz Wi{\'s}niowski and Shazaad Ahmad and {Aparicio Castro}, Andrea and Jacob Curran-Sebastian and Jane Eddleston and Neil Hanley and Thomas House and Kim, {Ji Hye} and Wendy Olsen and Maria Pampaka and Lorenzo Pellis and {Perez Ruiz}, Diego and Nick Shryane and Mark Elliot",
year = "2021",
month = jul,
day = "22",
doi = "10.21203/rs.3.rs-56855/v1",
language = "English",
volume = "21",
journal = "BMC Infectious Diseases",
issn = "1471-2334",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning

AU - Overton, Christopher

AU - Wiśniowski, Arkadiusz

AU - Ahmad, Shazaad

AU - Aparicio Castro, Andrea

AU - Curran-Sebastian, Jacob

AU - Eddleston, Jane

AU - Hanley, Neil

AU - House, Thomas

AU - Kim, Ji Hye

AU - Olsen, Wendy

AU - Pampaka, Maria

AU - Pellis, Lorenzo

AU - Perez Ruiz, Diego

AU - Shryane, Nick

AU - Elliot, Mark

A2 - Vekaria, Bindu

PY - 2021/7/22

Y1 - 2021/7/22

N2 - Background: 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.Method: 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.Results: 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 9.3 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 22.8 days.Conclusions: 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 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.

AB - Background: 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.Method: 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.Results: 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 9.3 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 22.8 days.Conclusions: 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 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.

U2 - 10.21203/rs.3.rs-56855/v1

DO - 10.21203/rs.3.rs-56855/v1

M3 - Article

VL - 21

JO - BMC Infectious Diseases

JF - BMC Infectious Diseases

SN - 1471-2334

M1 - 700

ER -