ICU后综合征患者疲劳轨迹的潜在类别及影响因素分析

doi:10.3761/j.issn.0254-1769.2022.03.003

摘要/Abstract

摘要：

目的探讨ICU后综合征患者疲劳轨迹的潜在类别,分析患者人口学资料和疾病相关资料对其疲劳轨迹潜在类别的影响。方法采用便利抽样法,选取2020年1月—9月于贵州省某三级甲等医院综合ICU住院的患者作为调查对象,收集患者人口学及疾病相关资料。分别在患者转出ICU后1周、1个月、3个月时评估患者的疲劳程度,使用增长混合模型识别其疲劳轨迹的潜在类别,采用Logistic回归分析潜在类别的影响因素。结果初步纳入556例ICU转出患者,其中300例发生ICU后综合征,其疲劳程度存在4种轨迹,分别为持续疲劳（19.00%）、疲劳升高（6.00%）、疲劳缓解（27.67%）、无疲劳（47.33%）。Logistic回归分析显示,急性生理与慢性健康状况评分Ⅱ（Acute Physiology and Chronic Health Evaluation Ⅱ,APACHE Ⅱ）、年龄、是否有呼吸系统疾病史是ICU后综合征患者疲劳轨迹潜在类别的独立影响因素（P<0.05）。结论 ICU后综合征患者疲劳程度呈现不同的变化轨迹,医护人员应重视APACHE Ⅱ高、高龄、有呼吸系统疾病史患者疲劳程度的评估和干预。

关键词: 危重病, 重症监护病房, 疲劳, 影响因素分析, 潜在类别分析, 护理

Abstract:

Objective To explore the potential trajectory categories of fatigue degree in patients with post-intensive care syndrome（PICS）,and to analyze the influence of patient demographic data and disease-related data on the potential categories of fatigue trajectory. Methods A convenient sampling method was used to select patients hospitalized in general ICU ward of a tertiary hospital in Guizhou Province from January 2020 to September,and the demographic and disease-related data of patients was collected. The fatigue degree of the patients was evaluated at 1 week（T1）,1 month（T2） and 3 months（T3） after ICU discharge. The growth mixture model was used to identify the potential trajectory category. Logistic regression analysis was used to analyze the prediction factors of trajectory category. Results 556 research subjects were initially enrolled,and 300 of them developed PICS. 4 types of fatigue trajectories were identified in these patients,and they were sustained fatigue group（19.00%）,increased fatigue group（6.00%）,fatigue remission group（27.67%）and non-fatigue group（47.33%）. Logistic regression analysis showed that high APACHE Ⅱ score,advanced age and history of respiratory diseases were independent predictors of fatigue trajectory categories. Conclusion The degree of fatigue in patients with PICS shows different trajectories. Medical staff should pay attention to the evaluation and intervention of fatigue in patients with high APACHE Ⅱ scores,advanced age,and history of respiratory diseases.

Key words: Critical Illness, Intensive Care Units, Fatigue, Root Cause Analysis, Latent Class Analysis, Nursing Care

张习莹, 江智霞, 刘其兰, 何曼曼, 张芳, 向黔灵, 胡汝均. ICU后综合征患者疲劳轨迹的潜在类别及影响因素分析[J]. 中华护理杂志, 2022, 57(3): 272-278.

ZHANG Xiying, JIANG Zhixia, LIU Qilan, HE Manman, ZHANG Fang, XIANG Qianling, HU Rujun. Analysis of potential categories and influencing factors of fatigue trajectories in post-intensive care syndrome patients[J]. Chinese Journal of Nursing, 2022, 57(3): 272-278.

图/表 3

表1 ICU后综合征患者疲劳轨迹潜在类别模型的拟合结果

Table 1 Model fitting results of fatigue score of post-intensive care syndrome patients

表2 ICU后综合征患者疲劳轨迹潜在类别的单因素分析结果

Table 2 Single factor analysis of influencing factors of fatigue degree trajectory type of post-intensive care syndrome patients

表3 ICU后综合征患者疲劳轨迹潜在类别的多分类Logistic回归分析结果

Table 3 Multiple logistic regression analysis of fatigue trajectory category in post-intensive care syndrome patients

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