Literature DB >> 32841815

What factors predict length of stay in the intensive care unit? Systematic review and meta-analysis.

Igor Tona Peres1, Silvio Hamacher2, Fernando Luiz Cyrino Oliveira3, Antônio Márcio Tavares Thomé4, Fernando Augusto Bozza5.   

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.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Intensive care unit; Length of stay; Meta-analysis; Predictors; Prognostic factors; Systematic literature review

Mesh:

Year:  2020        PMID: 32841815     DOI: 10.1016/j.jcrc.2020.08.003

Source DB:  PubMed          Journal:  J Crit Care        ISSN: 0883-9441            Impact factor:   3.425


  3 in total

1.  Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients.

Authors:  Yuhan Deng; Shuang Liu; Ziyao Wang; Yuxin Wang; Yong Jiang; Baohua Liu
Journal:  Front Med (Lausanne)       Date:  2022-09-28

2.  Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

Authors:  Zhixu Hu; Hang Qiu; Liya Wang; Minghui Shen
Journal:  BMC Med Inform Decis Mak       Date:  2022-03-10       Impact factor: 2.796

Review 3.  Prediction of intensive care units length of stay: a concise review.

Authors:  Igor Tona Peres; Silvio Hamacher; Fernando Luiz Cyrino Oliveira; Fernando Augusto Bozza; Jorge Ibrain Figueira Salluh
Journal:  Rev Bras Ter Intensiva       Date:  2021 Apr-Jun
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.