天津医药

天津医药 ›› 2021, Vol. 49 ›› Issue (8): 833-837.doi: 10.11958/20210206

• 临床研究 • 上一篇    下一篇

哮喘患者疲劳症状的临床特征及影响因素分析

袁琛,朱振刚   

  1. 天津中医药大学第一附属医院呼吸内科（邮编300193）
  • 收稿日期:2021-01-25 修回日期:2021-04-19 出版日期:2021-08-15 发布日期:2021-08-15
  • 作者简介:袁琛（1988），女，硕士，主治医师，主要从事哮喘的预防与治疗相关方面研究。E-mail：qingfeng958810@sina.com
  • 基金资助:
    国家自然科学基金资助项目（81673900）

Analysis of clinical characteristics and related factors of fatigue symptoms in asthmatic patients

YUAN Chen, ZHU Zhen-gang   

  1. Department of Respiratory Medicine, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
  • Received:2021-01-25 Revised:2021-04-19 Published:2021-08-15 Online:2021-08-15

PDF (PC)

3205

摘要： 目的 探讨哮喘患者疲劳症状的临床特点及影响因素。方法 选取哮喘患者198例，根据疲劳严重度量 表（FSS）评分将患者分为疲劳组（≥4分，126例）和非疲劳组（＜4分，72例）。分析2组临床基本特征、肺功能、呼出气 一氧化氮（FeNO）、呼吸困难分级（mMRC）、哮喘控制测试评分（ACT）、日常生活能力（MBI）评分、6 min 步行测试 （6MWT）、汉密尔顿抑郁量表17项（HAMD-17）、匹兹堡睡眠质量指数量表（PSQI）、过去1年急性发作次数等方面的 差异。二分类Logistic回归分析哮喘患者疲劳症状的影响因素。根据筛选后的指标构建列线图预测模型，通过受试 者工作特征（ROC）曲线和校准曲线评价模型的预测价值。结果 疲劳组 ACT 评分低于非疲劳组，mMRC 分级、 HAMD-17评分、PSQI评分及上1年急性发作次数高于非疲劳组（P＜0.01），2组余指标差异无统计学意义（P＞0.05）。 二分类 Logistic 回归分析显示，ACT 评分（OR=0.644，95%CI：0.508~0.816）、mMRC 分级（OR=2.313，95%CI：1.349～ 3.966）、HAMD-17评分（OR=1.561，95%CI：1.273~1.913）及PSQI评分（OR=1.932，95%CI：1.506～2.479）是哮喘患者发 生疲劳的影响因素。基于4项影响因素构建列线图预测模型，预测模型的ROC曲线下面积为0.935（95%CI：0.902~ 0.967）。内部验证显示，C-index 为 0.929，校准曲线表明列线图的预测结果与实际的观测结果一致性良好。结论 哮喘患者中疲劳的发生率较高，哮喘控制不佳、高mMRC分级、负性情绪和睡眠障碍是发生疲劳的重要影响因素，根 据影响因素构建的列线图具有较高的预测价值。

关键词: 哮喘, 疲劳, 影响因素分析, 列线图, 疲劳严重度量表

Abstract: Objective To investigate the clinical characteristics and related factors of fatigue symptoms in patients with asthma. Methods A total of 198 asthmatic patients who visited the respiratory department of our hospital were included in this study. According to the Fatigue Severity Scale (FSS), the patients were divided into the fatigue group (≥4, n= 126) and the non-fatigue group (＜4, n=72). The clinical characteristics, pulmonary function, exhaled nitric oxide test (FENO), the dyspnea score (MRC), asthma control scale (ACT), daily life ability (MBI)，6 min walk test (6MWT)，Hamilton depression scale-17 items (HAMD-17), Pittsburgh sleep quality index scale (PSQI) and the number of acute episodes in the past year were analyzed between the two groups. Binary Logistic regression analysis was used for analyzing the influencing factors of fatigue symptoms in asthmatic patients. According to the selected indicators, the prediction model of the nomogram was constructed, and the prediction value of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Results The ACT score was lower in fatigue group than that of non-fatigue group, and the mMRC grading, HAMD-17 score, PSQI score and the frequency of acute attack in the previous 1 year were higher in fatigue group than those of non-fatigue group (P＜0.05). There were no significant differences in the other indexes between the two groups (P＞0.05). Binary Logistic regression analysis showed that ACT score (OR=0.644, 95%CI: 0.508-0.816), mMRC grading (OR=2.313, 95%CI: 1.349-3.966), HAMD-17 score (OR=1.561, 95%CI: 1.273-1.913) and PSQI score (OR=1.932, 95%CI: 1.506-2.479) were the influencing factors of fatigue in asthmatic patients. The prediction model was built based on the four influencing factors. The area under the ROC curve of the prediction model was 0.935 (95%CI: 0.902-0.967). Through internal verification, the C-index was 0.929, and the calibration curve showed that the predicted results of the nomogram were in good agreement with the actual observation results. Conclusion The incidence of fatigue is high in patients with asthma. Poor asthma control, high mMRC grading, negative mood and sleep disturbance are important influencing factors of fatigue. The nomogram constructed according to the influencing factors has high predictive value.

Key words: asthma, fatigue, root cause analysis, nomograms, fatigue severity scale

中图分类号: