Abstract:

Objective To develop and validate a fatigue risk nomogram model in Chronic Obstructive Pulmonary Disease（COPD） patients. Methods A prospective study design was adopted，and 430 COPD patients recruited from a tertiary A hospital in Huzhou City from January to December 2022 were conveniently selected for model construction，and 129 patients were recruited from the same hospital from January to June 2023 for external validation of the model. The general information questionnaire，Pittsburgh Sleep Quality Index，2-item Generalized Anxiety Disorder Scale，2-item Patient Health Questionnaire，modified British Medical Research Council Dyspnea Index，International Physical Activity Questionnaire，and Fatigue Severity Scale were used for questionnaire survey.The risk prediction model and nomograms model were constructed using Logistic regression analysis and R 4.3.2 software，and the area under the receiver operating characteristic（ROC） curve was used to test the prediction effect of the model. Results Univariate and binary logistic regression analysis results showed that age（OR=1.095），gender（OR=2.077），dyspnea（OR=3.309），sleep quality（OR=1.979），anemia（OR=3.289），the number of acute exacerbation（OR=2.991） were independent influencing factors for fatigue in COPD patients. The internal evaluation and external validation results of the model showed that the areas under the curve are 0.912 and 0.844 respectively，and the Hosmer-Lemeshow goodness of fit test P values were 0.806 and 0.526 respectively. The average absolute errors were 0.013 and 0.019 respectively. Conclusion The COPD fatigue risk prediction model constructed in this study has good prediction effect. The visual nomogram is intuitive，convenient and easy to operate. It can provide a tool for early screening of fatigue in COPD patients.

Key words: Chronic Obstructive Pulmonary Disease, Fatigue, Prediction Model, Nomogram, Nursing Care

摘要：

目的 构建并验证COPD患者疲劳风险列线图模型。 方法 采用前瞻性研究设计，便利选取2022年1月—12月在湖州市某三级甲等医院招募的430例COPD患者进行模型构建，选取2023年1月—6月招募的129例COPD患者进行模型外部验证。采用一般资料调查表、匹兹堡睡眠质量指数量表、广泛性焦虑量表简版、患者健康问卷、改良版英国医学研究委员会呼吸困难指数、国际体力活动问卷、疲劳程度量表进行调查。采用Logistic回归分析筛选COPD患者发生疲劳的危险因素，使用R 4.3.2软件构建风险预测模型和列线图模型，采用受试者工作特征曲线下面积检验模型预测效果。 结果 Logistic回归分析结果显示：年龄（OR=1.095）、性别（OR=2.077）、呼吸困难程度（OR=3.309）、睡眠质量（OR=1.979）、贫血（OR=3.289）、急性加重次数（OR=2.991）是COPD患者发生疲劳的独立影响因素。模型内部评价及外部验证结果显示，受试者工作特征曲线下面积分别为0.912、0.844，Hosmer-Lemeshow检验P值分别为0.806、0.526，平均绝对误差分别为0.013、0.019。 结论 该研究构建的COPD疲劳风险列线图模型预测效果良好，可为COPD患者疲劳的早期筛查提供可靠工具。

关键词: 慢性阻塞性肺疾病, 疲劳, 预测模型, 列线图, 护理