Developing and Validating a Nomogram for Non-Adherence to Inhaler Therapy Among Elderly Chronic Obstructive Pulmonary Disease Patients Based on the Social Ecological Model

Purpose: 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).<br /> Patients and Methods: 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.<br /> Results: 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%.<br /> Conclusion: 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.