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Development trajectory and risk factors of delirium in ICU patients

Published on Sep. 30, 2024Total Views: 745 timesTotal Downloads: 255 timesDownloadMobile

Author: SUN Shanshan 1 TAO Lei 1 ZHONG Mingming 1 TIAN Jinhui 2 WANG Min 1 ZHANG Zhigang 1, 3

Affiliation: 1. School of Nursing, Lanzhou university, Lanzhou 730011, China 2. Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China 3. Department of Intensive Care Medicine, The First Hospital of Lanzhou University, Lanzhou 730030, China

Keywords: Intensive care unit Delirium Risk factors Latent class growth model Development trajectory

DOI: 10.12173/j.issn.1004-5511.202405097

Reference: Sun SS, Tao L, Zhong MM, Tian JH, Wang M, Zhang ZG. Development trajectory and risk factors of delirium in ICU patients[J]. Yixue Xinzhi Zazhi, 2024, 34(9):978-988. DOI: 10.12173/j.issn.1004-5511.202405097.[Article in Chinese]

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Abstract

Objective  To explore the different trajectories and risk factors of delirium in ICU patients, and to provide theoretical basis for delirium management.

Methods  From March 2023 to December 2023, ICU patients from The First Hospital of Lanzhou University were selected by convenient sampling method. Baseline and delirium assessments were performed at 24 hours (T1) after admission to the ICU using the general data questionnaire, Confusion Assessment Method of Intensive Care Unit (CAM-ICU), and Confusion Assessment Method for the Intensive Care Unit-7 (CAM-ICU-7), Richmond Agitation-Sedation Scale (RASS). Patients who developed delirium were continuously monitored at day 2, 3, 4, and 5 (T2-T5) time points after admission using RASS, CAM-ICU, and CAM-ICU-7. The latent class growth model was used to identify the category of delirium trajectory, and the influencing factors of different categories were analyzed.

Results  269 ICU patients were included, of which 126 patients experienced delirium and 102 patients with delirium participated in the whole 5 times of investigation, the incidence of delirium in ICU patients was 42.86%. The potential category growth model showed that the model with 3 subgroups fitted best. The 3 subgroups were named as the persistent delirium group(30.4%), the high-risk declining group(29.4%), and the low-risk rising group(40.2%). The univariate analysis showed that gender, type of sedatives and RASS score at different time points influenced the development trajectory of delirium (P<0.05). Multiple Logistic regression analysis showed that compared with the persistent delirium group, gender and type of sedatives were the main predictors of developing delirium in ICU patients into the high-risk declining group. Compared with the persistent delirium group, the predictors of ICU patients developing into the low-risk elevated group were T1 and T5 time point RASS scores.

Conclusions  There is population heterogeneity in the development trajectory of delirium in ICU patients, which can be divided into 3 potential categories. Gender, type of sedation use, and RASS score are influential factors in the latent category of delirium development trajectory in ICU patients. The medical staff should conduct personalized delirium management for ICU patients according to different delirium change trajectories.

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