Department of Respiratory Diseases, University of Lille, CHU Lille, Lille.
Department of Respiratory Diseases, AP-HP, Hôpital Cochin,tmen EA2511, Université Paris Descartes, Sorbonne Paris Cité, Paris.
Chest. 2018 May;153(5):1106-1115. doi: 10.1016/j.chest.2017.10.009. Epub 2017 Oct 17.
The COPD "frequent exacerbator" phenotype is usually defined by at least two treated exacerbations per year and is associated with a huge impact on patient health. However, existence of this phenotype and corresponding thresholds still need to be formally confirmed by statistical methods analyzing exacerbation profiles with no specific a priori hypothesis. The aim of this study was to confirm the existence of the frequent exacerbator phenotype with an innovative unbiased statistical analysis of prospectively recorded exacerbations.
Data from patients with COPD from the French cohort in Exacerbations of COPD Patients (EXACO) were analyzed using the KmL method designed to cluster longitudinal data and receiver operating characteristic (ROC) curve analysis to determine the best threshold to allocate patients to identified clusters. Univariate and multivariate analyses were performed to study characteristics associated with different clusters.
Two clusters of patients were identified based on exacerbation frequency over time, with 2.89 exacerbations per year on average in the first cluster (n = 348) and 0.71 on average in the second cluster (n = 116). The best threshold to distinguish these clusters was two moderate to severe exacerbations per year. Frequent exacerbators had more airflow limitation, symptoms, and health-related quality of life impairment. A simple clinical score was derived to help identify patients at risk of exacerbations.
These analyses confirmed the existence and clinical relevance of a frequent exacerbator subgroup of patients with COPD and the currently used threshold to define this phenotype.
COPD“频繁加重者”表型通常定义为每年至少发生两次治疗后加重，对患者健康有巨大影响。然而，这种表型的存在及其相应的阈值仍需要通过分析无特定先验假设的加重情况的统计方法来正式确认。本研究的目的是通过对前瞻性记录的加重情况进行创新的无偏统计分析，确认频繁加重者表型的存在。
使用 KmL 方法对来自法国 COPD 患者加重（EXACO）队列的 COPD 患者数据进行分析，该方法旨在对纵向数据进行聚类，并进行接收者操作特征（ROC）曲线分析，以确定将患者分配到确定聚类的最佳阈值。进行单变量和多变量分析以研究与不同聚类相关的特征。
根据随时间推移的加重频率确定了两个患者聚类，第一个聚类中每年平均有 2.89 次加重（n=348），第二个聚类中每年平均有 0.71 次加重（n=116）。区分这些聚类的最佳阈值是每年两次中度至重度加重。频繁加重者的气流受限、症状和健康相关生活质量受损更为严重。得出了一个简单的临床评分，以帮助识别有加重风险的患者。
这些分析证实了 COPD 患者中存在频繁加重亚组和目前用于定义这种表型的阈值，以及其临床相关性。
