VA's Health Services Research and Development Service (HSR&D), Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, 20420, USA; VA Quality Scholars Coordinating Center, IQuESt, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA; Section of Pulmonary and Critical Care Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.
Section of Pulmonary and Critical Care Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.
Respir Med. 2024 Jun;227:107641. doi: 10.1016/j.rmed.2024.107641. Epub 2024 May 6.
Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes.
To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification.
A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates.
Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality.
We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.
COPD 患者睡眠紊乱会影响生活质量并预测不良预后。
使用客观睡眠参数识别 COPD 患者的不同表型聚类，并评估聚类与全因死亡率之间的关联，以进行风险分层。
这是一项使用全国退伍军人健康管理局 COPD 睡眠障碍患者的纵向观察队列研究。使用经过验证的自然语言处理算法从睡眠多导图医师解读中提取睡眠参数。我们使用无监督机器学习算法（K-均值）进行聚类分析，并使用 Cox 回归分析调整潜在混杂因素后，评估聚类与死亡率之间的关联，并使用 Kaplan-Meier 估计值进行可视化。
在 9992 名接受过临床指示性基线睡眠多导图检查的 COPD 患者中，我们根据年龄、合并症负担和睡眠参数确定了五个不同的聚类。总体死亡率从 9.4%增加到 42%，短期死亡率（<5.3 年）从 3.4%到 5 个聚类的 24.3%不等。在聚类 1 中，年龄较小，在聚类 5 中，合并症负担较高，而在其他三个聚类中，总睡眠时间和睡眠效率与死亡率有显著关联。
我们确定了五个不同的临床聚类，并强调了总睡眠时间和睡眠效率与死亡率之间的显著关联。所确定的聚类突出了客观睡眠参数在确定该人群的死亡率风险和表型特征方面的重要性。
