Mostrar o rexistro simple do ítem
Estimating Lengths-Of-Stay of Hospitalized COVID-19 Patients Using a Non-parametric Model: A Case Study in Galicia (Spain)
dc.contributor.author | López-Cheda, Ana | |
dc.contributor.author | Cao, Ricardo | |
dc.contributor.author | De Salazar, Pablo M. | |
dc.contributor.author | Jácome, M. A. | |
dc.date.accessioned | 2021-06-23T14:36:38Z | |
dc.date.available | 2021-06-23T14:36:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | López-Cheda A, Jácome M-A, Cao R, De Salazar PM (2021). Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain). Epidemiology and Infection 149, e102, 1–8. https://doi.org/ 10.1017/S0950268821000959 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/28123 | |
dc.description.abstract | [Abstract] Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds’ demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients’ hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes. | es_ES |
dc.description.sponsorship | ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish from MICINN (Ministerio de Ciencia, Innovación y Universidades) with reference BGP18/00154. ALC, MAJ and RC acknowledge partial support by the MINECO (Ministerio de Economía y Competitividad) Grant MTM2014-52876-R (EU ERDF support included) and the MICINN Grant MTM2017-82724-R (EU ERDF support included) and partial support of Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation ED431G 2019/01 and Grupos de Referencia Competitiva ED431C-2020-14 and ED431C2016-015) and the European Union (European Regional Development Fund - ERDF). PMD is a current recipient of the Grant of Excellence for postdoctoral studies by the Ramón Areces Foundation | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016/015 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Cambridge University Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIU/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/BEAGAL18%2F00143/ES/ | |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRI | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | |
dc.relation.uri | https://doi.org/10.1017/S0950268821000959 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | COVID-19 | es_ES |
dc.subject | Forecasting | es_ES |
dc.subject | ICU | es_ES |
dc.subject | Length-of-stay | es_ES |
dc.subject | Mixture cure model | es_ES |
dc.subject | Non-parametric | es_ES |
dc.title | Estimating Lengths-Of-Stay of Hospitalized COVID-19 Patients Using a Non-parametric Model: A Case Study in Galicia (Spain) | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Epidemiology and Infection | es_ES |
UDC.volume | 149 | es_ES |
dc.identifier.doi | 10.1017/S0950268821000959 |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-MODES - Artigos [141]