Department of Social Psychology, University of Groningen, Groningen, the Netherlands.
Department of Epidemiology and Data Science | Division EBM, Academic Medical Centre, Amsterdam, the Netherlands.
Int J Chron Obstruct Pulmon Dis. 2023 Mar 22;18:385-398. doi: 10.2147/COPD.S401023. eCollection 2023.
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.
肺康复（PR）被认为是改善慢性阻塞性肺疾病（COPD）患者健康相关生活质量的一种具有成本效益的方法。然而，需求的增加和供应成本的增加需要可持续和负担得起的护理。保持护理负担得起的一种可能解决方案是自我管理。这里的一个挑战是不遵守。了解哪些患者是遵守的，哪些患者是不遵守的，可以帮助区分需要更多或更少支持的患者。因此，本研究的目的是开发和验证一种预测 COPD 患者 PR 依从性的模型。
开发了一种用于运动依从性的多变量逻辑回归模型。在荷兰和比利时的 53 个初级物理治疗实践中，对 196 名接受 PR 后的 COPD 患者进行了一项前瞻性队列研究，获得了 8 个预先指定的候选预测因素。为了创建一个简约的模型，使用向后选择进行了变量选择，消除的 p 值>0.05。通过判别、校准和临床实用性评估模型性能。通过自举（n=500）评估内部验证。
最终模型包括四个预测因素：意向、抑郁、MRC 评分和联盟。Bootstrap 内部验证后的校正 AUC 为 0.79（95%CI，0.72-0.85）。校准图表明校准良好，决策曲线分析表明在广泛的风险阈值范围内具有很大的净收益。
运动依从性预测模型具有预测 COPD 患者依从性的临床应用潜力。此类模型的信息可用于管理患者，而不是管理疾病，从而为每个个体患者确定治疗频率。因此，医疗保健能力可能得到更好的分配，这可能会减轻对医疗保健的压力，而不会影响 PR 对个体患者的有效性。
