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 C

SUBMITTER: Vekaria B 

PROVIDER: S-EPMC8295642 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature