2020
DOI: 10.21203/rs.3.rs-56855/v1
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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 Accelerat… Show more

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Cited by 24 publications
(27 citation statements)
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“…The next generation of bed capacity predictions should use patient bed pathways with local information on length of stay. To the best of our knowledge, there are only three other published models available which used patient bed pathways (33)(34)(35). The first one used the pathways "Ward", "Ward, ICU (not ventilated)", "Ward, ICU (ventilated)", "Ward, ICU (not ventilated), Ward", "Ward, ICU (ventilated), Ward" (33).…”
Section: Results In Contextmentioning
confidence: 99%
See 1 more Smart Citation
“…The next generation of bed capacity predictions should use patient bed pathways with local information on length of stay. To the best of our knowledge, there are only three other published models available which used patient bed pathways (33)(34)(35). The first one used the pathways "Ward", "Ward, ICU (not ventilated)", "Ward, ICU (ventilated)", "Ward, ICU (not ventilated), Ward", "Ward, ICU (ventilated), Ward" (33).…”
Section: Results In Contextmentioning
confidence: 99%
“…As for the second model, it focused on machine-learning methods to estimate transition probabilities between the clinical states moderate/severe and critical, and is hence not comparable (34). The third model is closely aligned to our own work here, as it uses multi-state methods to estimate length of stay using a local hospital and a national dataset from the UK (35). Notably, this study investigates length of stay variations depending on when a patient was admitted to ICU during their hospitalisation, and also concludes that national estimates can differ from local ones.…”
Section: Results In Contextmentioning
confidence: 99%
“…Quarantined households are aware they have been exposed and therefore report infection at symptom onset. If tracing begins after a positive test, we add a further gamma distributed testing delay of mean of 1.54 days and standard deviation of 1.1, parameterised using a truncation corrected maximum likelihood estimator applied to anonymised UK linelist data 20,52 .…”
Section: Case Identification and Contact Tracing Delaysmentioning
confidence: 99%
“…However, as the delay time varies from individual to individual, the effect of the intervention is spread over several days. The delay distribution of the incubation period [ 81 , 100 ] and the time between symptom onset and hospital admission [ 41 ] 126 ] is therefore crucial, as is understanding the heterogeneity in the delay times among individuals. Good knowledge of such delay distributions allows one to back-calculate the number of newly (symptomatic) infected cases, known as nowcasting, from either the number of confirmed cases or hospitalised cases, and assess the impact of intervention measures.…”
Section: Modeling and Monitoring The Epidemicmentioning
confidence: 99%
“…Second, the length of stay in hospital is important, which varies among individuals and among countries due to different health systems. Information about the length of stay in hospital is important to predict the number of required hospital beds, both for beds in general hospital and beds in the ICU, and to track the burden on hospitals [ 126 ]. Individual-specific characteristics, such as, for example, sex, age, comorbidity, and frailty of the individual, can explain differences in length of stay in the hospital and are therefore important to correct for.…”
Section: Modeling and Monitoring The Epidemicmentioning
confidence: 99%