A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

Lafuente, M. (Universidad de Zaragoza) ; López, F. J. (Universidad de Zaragoza) ; Mateo, P. M. (Universidad de Zaragoza) ; Cebrián, A. C. (Universidad de Zaragoza) ; Asín, J. (Universidad de Zaragoza) ; Moler, J. A. ; Borque-Fernando, Á. (Universidad de Zaragoza) ; Esteban, L. M. ; Pérez-Palomares, A. (Universidad de Zaragoza) ; Sanz, G. (Universidad de Zaragoza)

Resumen: Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Idioma: Inglés

DOI: 10.1016/j.heliyon.2023.e13545

Año: 2023

Publicado en: Heliyon 9, 2 (2023), e13545 [18 pp.]
ISSN: 2405-8440

Factor impacto JCR: 3.4 (2023)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 28 / 134 = 0.209 (2023) - Q1 - T1

Factor impacto CITESCORE: 4.5 - Multidisciplinary (Q1)

Factor impacto SCIMAGO: 0.617 - Multidisciplinary (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Área Urología (Dpto. Cirugía)