Author | Peres, Igor Tona | |
Author | Hamacher, Silvio | |
Author | Oliveira, Fernando Luiz Cyrino | |
Author | Thomé, Antônio Márcio Tavares | |
Author | Bozza, Fernando A. | |
Access date | 2020-10-31T17:53:43Z | |
Available date | 2020-10-31T17:53:43Z | |
Document date | 2020 | |
Citation | PERES, Igor Tona et al. What factors predict length of stay in the intensive care unit? Systematic review and meta-analysis. Journal of Critical Care, v. 60, p. 183-194, 2020. | pt_BR |
ISSN | 0883-9441 | pt_BR |
URI | https://www.arca.fiocruz.br/handle/icict/44291 | |
Language | eng | pt_BR |
Publisher | Elsevier | pt_BR |
Rights | restricted access | pt_BR |
Title | What factors predict length of stay in the intensive care unit? Systematic review and meta-analysis | pt_BR |
Type | Article | pt_BR |
DOI | 10.1016/j.jcrc.2020.08.003 | |
Abstract | Purpose: Studies have shown that a small percentage of ICU patients have prolonged length of stay (LoS) and account for a large proportion of resource use. Therefore, the identification of prolonged stay patients can improve unit efficiency. In this study, we performed a systematic review and meta-analysis to understand the risk factors of ICU LoS.
Materials and methods: We searched MEDLINE, Embase and Scopus databases from inception to November 2018. The searching process focused on papers presenting risk factors of ICU LoS. A meta-analysis was performed for studies reporting appropriate statistics.
Results: From 6906 citations, 113 met the eligibility criteria and were reviewed. A meta-analysis was performed for six factors from 28 papers and concluded that patients with mechanical ventilation, hypomagnesemia, delirium, and malnutrition tend to have longer stay, and that age and gender were not significant factors.
Conclusions: This work suggested a list of risk factors that should be considered in prediction models for ICU LoS, as follows: severity scores, mechanical ventilation, hypomagnesemia, delirium, malnutrition, infection, trauma, red blood cells, and PaO2:FiO2. Our findings can be used by prediction models to improve their predictive capacity of prolonged stay patients, assisting in resource allocation, quality improvement actions, and benchmarking analysis. | pt_BR |
Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Educação, Rio de Janeiro, RJ, Brasil. | pt_BR |
Subject | Intensive care unit | pt_BR |
Subject | Length of stay | pt_BR |
Subject | Meta-analysis | pt_BR |
Subject | Predictors | pt_BR |
Subject | Prognostic factors | pt_BR |
Subject | Systematic literature review | pt_BR |