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Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).
López-Cheda, Ana; Jácome, María-Amalia; Cao, Ricardo; De Salazar, Pablo M.
  • López-Cheda A; Universidade da Coruña, CITIC, MODES, A Coruña, Spain.
  • Jácome MA; Universidade da Coruña, CITIC, MODES, A Coruña, Spain.
  • Cao R; Universidade da Coruña, CITIC, ITMATI, MODES, A Coruña, Spain.
  • De Salazar PM; Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, USA.
Epidemiol Infect ; 149: e102, 2021 04 27.
Article in English | MEDLINE | ID: covidwho-1279797
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Statistical / Forecasting / COVID-19 / Length of Stay Type of study: Case report / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Epidemiol Infect Journal subject: Communicable Diseases / Epidemiology Year: 2021 Document Type: Article Affiliation country: S0950268821000959

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Statistical / Forecasting / COVID-19 / Length of Stay Type of study: Case report / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Epidemiol Infect Journal subject: Communicable Diseases / Epidemiology Year: 2021 Document Type: Article Affiliation country: S0950268821000959