Chen Shubing, Peng Yongyi, Shen Beilan, Zhong Liping, Wu Zhongping, Zheng Jinping, Gao Yi
State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
J Asthma Allergy. 2023 Jan 24;16:159-172. doi: 10.2147/JAA.S396694. eCollection 2023.
To develop and internally validate a nomogram for predicting the risk of incorrect inhalation techniques in patients with chronic airway diseases.
A total of 206 patients with chronic airway diseases treated with inhaled medications were recruited in this study. Patients were divided into correct (n=129) and incorrect (n=77) cohorts based on their mastery of inhalation devices, which were assessed by medical professionals. Data were collected on the basis of questionnaires and medical records. The least absolute shrinkage and selection operator method (LASSO) and multivariate logistic regression analyses were conducted to identify the risk factors of incorrect inhalation techniques. Then, calibration curve, Harrell's C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and bootstrapping validation were applied to assess the apparent performance, clinical validity and internal validation of the predicting model, respectively.
Seven risk factors including age, education level, drug cognition, self-evaluation of curative effect, inhalation device use instruction before treatment, post-instruction evaluation and evaluation at return visit were finally determined as the predictors of the nomogram prediction model. The ROC curve obtained by this model showed that the AUC was 0.814, with a sensitivity of 0.78 and specificity of 0.75. In addition, the C-index was 0.814, with a Z value of 10.31 (P<0.001). It was confirmed to be 0.783 by bootstrapping validation, indicating that the model had good discrimination and calibration. Furthermore, analysis of DCA showed that the nomogram had good clinical validity.
The application of the developed nomogram to predict the risk of incorrect inhalation techniques during follow-up visits is feasible.
开发并内部验证一种用于预测慢性气道疾病患者吸入技术不正确风险的列线图。
本研究共纳入206例接受吸入药物治疗的慢性气道疾病患者。根据医疗专业人员评估的吸入装置掌握情况，将患者分为正确组（n = 129）和不正确组（n = 77）。通过问卷调查和病历收集数据。采用最小绝对收缩和选择算子法（LASSO）和多因素逻辑回归分析来确定吸入技术不正确的风险因素。然后，分别应用校准曲线、Harrell氏C指数、受试者操作特征曲线下面积（AUC）、决策曲线分析（DCA）和自助法验证来评估预测模型的表观性能、临床有效性和内部验证。
最终确定年龄、教育水平、药物认知、疗效自我评估、治疗前吸入装置使用指导、指导后评估和复诊评估这7个风险因素作为列线图预测模型的预测指标。该模型获得的ROC曲线显示AUC为0.814，灵敏度为0.78，特异度为0.75。此外，C指数为0.814，Z值为10.31（P < 0.001）。经自助法验证为0.783，表明该模型具有良好的区分度和校准度。此外，DCA分析表明列线图具有良好的临床有效性。
应用所开发的列线图预测随访期间吸入技术不正确的风险是可行的。
