Development and Validation of a Multivariable Exercise Adherence Prediction Model for Patients with COPD: A Prospective Cohort Study

• Published in: International journal of chronic obstructive pulmonary disease 2023-01, Vol.18, p.385-398
• Main Authors: Ricke, Ellen, Bakker, Eric W
• Format: Article
• Language: English
• Subjects: Analysis; Behavior; Belgium; Care and treatment; Chronic illnesses; Chronic obstructive pulmonary disease; Cohort analysis; Costs; Demographics; Exercise; Humans; Lung diseases, Obstructive; Medical prognosis; Medical research; Medicine, Experimental; Morbidity; Mortality; Original Research; Participation; Patient compliance; Patients; Physical therapy; Primary care; Prospective Studies; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - therapy; pulmonary rehabilitation; Quality of Life; Questionnaires; Rehabilitation; self-management; sustainable healthcare; Systematic review
• ISSN: 1178-2005; 1176-9106; 1178-2005
• DOI: 10.2147/COPD.S401023

Pulmonary rehabilitation (PR) is considered a cost-effective method of improving health-related quality of life in patients with chronic obstructive pulmonary disease (COPD). However, increasing demand and increasing costs of supply demands for sustainable and affordable care. One of the possible solutions to keep care affordable is self-management. A challenge here is non-adherence. Understanding who are adherent and who are non-adherent could be helpful to differentiate between patients who need more or less support. Therefore, the aim of this study was to develop and validate a model to predict adherence to PR in patients with COPD. A multivariable logistic regression model for exercise adherence was developed. Eight candidate predictors, that were prespecified, were obtained in a prospective cohort study from 196 patients with COPD following PR in 53 primary physiotherapy practices in the Netherlands and Belgium, between January 2021 and August 2022. To create a parsimonious model, variable selection using backward selection was performed with a -value of >0.05 for elimination. Model performance was assessed by discrimination, calibration and clinical utility. Internal validation was assessed by bootstrapping (n = 500). The final model included four predictors: intention, depression, MRC-score and alliance. The optimism-corrected AUC after bootstrap internal validation was 0.79 (95% CI, 0.72-0.85). Calibration plots suggested good calibration and decision curve analysis showed great net benefit in a wide range of risk thresholds. The exercise adherence prediction model has potential for clinical utility to predict adherence in patients with COPD. Information from such a model can be used to manage the patient instead of managing the disease, and thereby to determine the treatment frequency for each individual patient. As a result, healthcare capacity might be better distributed, potentially reducing pressure on healthcare without compromising the effectiveness of PR for the individual patient.