Liu You-Ran, Wang Yan, Chen Juan, Luo Shan, Ji Xiaomei, Wang Huadong, Zhang Li
School of Nursing, Bengbu Medical University, Bengbu, People's Republic of China.
Department of Nursing, Tangshan Vocational & Technical College, Tangshan, People's Republic of China.
Patient Prefer Adherence. 2024 Aug 17;18:1741-1753. doi: 10.2147/PPA.S472625. eCollection 2024.
This study aimed to identify the risk predictors of non-adherence to inhaler therapy and construct a nomogram prediction model for use in Chinese elderly patients with chronic obstructive pulmonary disease (COPD).
A cross-sectional study was conducted with 305 participants recruited from a tertiary care hospital in Anhui, China. Adherence was analyzed using the Test of Adherence to Inhalers. Potential predictive factors were incorporated based on the social ecological model, and data were collected through a questionnaire method. R version 4.3.3 was utilized to perform the least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis to identify risk factors and establish a nomogram prediction model.
The results of the multivariable analysis revealed that medication beliefs, illness perception, the COPD Assessment Test score, smoking status, and education level were significant risk factors for non-adherence to inhaler therapy in elderly COPD patients (all P < 0.05). The nomogram prediction model for non-adherence to inhaler therapy in elderly COPD patients demonstrated a good discriminative ability, with an area under the receiver operating characteristic curve of 0.912. The C-index was 0.922 (95% CI: 0.879 to 0.965), and the Brier value was 0.070, indicating good consistency and calibration. Decision curve analysis indicated that the use of the nomogram would be more beneficial in clinical practice when the threshold probability of non-adherence exceeds 17%.
This study identified predictive factors regarding non-adherence among elderly patients with COPD and constructed a predictive nomogram. By utilizing the nomogram model healthcare professionals could swiftly calculate and comprehend the non-compliance level of COPD patients, thus guiding the development of personalized interventions in clinical practice.
本研究旨在确定吸入器治疗不依从的风险预测因素，并构建一个列线图预测模型，用于中国老年慢性阻塞性肺疾病（COPD）患者。
在中国安徽一家三级医院招募了305名参与者进行横断面研究。使用吸入器依从性测试分析依从性。基于社会生态模型纳入潜在的预测因素，并通过问卷调查方法收集数据。利用R版本4.3.3进行最小绝对收缩和选择算子回归模型及多变量逻辑回归分析，以识别风险因素并建立列线图预测模型。
多变量分析结果显示，用药信念、疾病认知、COPD评估测试得分、吸烟状况和教育水平是老年COPD患者吸入器治疗不依从的显著风险因素（所有P<0.05）。老年COPD患者吸入器治疗不依从的列线图预测模型显示出良好的判别能力，受试者操作特征曲线下面积为0.912。C指数为0.922（95%CI：0.879至0.965），Brier值为0.070，表明具有良好的一致性和校准性。决策曲线分析表明，当不依从的阈值概率超过17%时，在临床实践中使用列线图将更有益。
本研究确定了老年COPD患者不依从的预测因素并构建了预测列线图。通过使用列线图模型，医护人员可以快速计算并理解COPD患者的不依从水平，从而指导临床实践中个性化干预措施的制定。
