呼吸事件持续时间揭示了阻塞性睡眠呼吸暂停中超越呼吸暂停低通气指数的新表型。

文献信息

中文摘要

背景： 阻塞性睡眠呼吸暂停（OSA）是一种异质性疾病，其特征为睡眠期间反复发生上气道塌陷。尽管呼吸暂停低通气指数（AHI）仍是OSA诊断和严重程度评估的基石，但它主要反映呼吸事件的频率，可能无法充分捕捉呼吸事件持续时间或相关生理负担的差异。我们旨在通过将呼吸事件持续时间纳入基于多导睡眠监测的聚类分析，来识别新的OSA表型。
方法： 我们回顾性分析了连续接受整夜多导睡眠监测的成年OSA患者。收集了人口学、人体测量学和多导睡眠监测特征。将年龄、体重指数（BMI）、颈围、AHI、最低血氧饱和度（LowSpO₂）、最长呼吸暂停持续时间（LAP）和最长低通气持续时间（LHP）纳入聚类分析。首先采用Ward法进行层次聚类以确定最佳聚类数，随后采用K-means聚类以识别不同的OSA表型。
结果： 在999例连续患者中，959例具有完整聚类变量的患者被纳入最终分析。层次聚类后接K-means聚类识别出四种不同的OSA表型。聚类1代表一种轻度表型，其特征为最低的AHI、最短的LAP和保留的夜间氧合。聚类2代表一种肥胖相关重度表型，具有最高的BMI、最大的颈围、最高的AHI和最严重的夜间氧饱和度下降。聚类3的特征是尽管AHI仅为中度，但呼吸暂停持续时间显著延长；而聚类4则表现出最长的呼吸暂停和低通气持续时间，并伴有明显的夜间低氧血症。四种表型在人口学特征、肥胖相关指标、疾病严重程度、夜间氧合和呼吸事件持续时间方面均观察到显著差异（所有P < 0.001）。重要的是，呼吸事件持续时间在传统基于频率的严重程度评估之外，提供了跨表型的额外时间特征描述。
结论： 呼吸事件持续时间增强的聚类识别出四种具有不同时间特征的探索性OSA表型。这些发现提示，呼吸事件持续时间可能为传统的基于AHI的严重程度评估提供补充信息，但其临床效用仍需进一步验证。

英文摘要

BACKGROUND: Obstructive sleep apnea (OSA) is a heterogeneous disorder characterized by recurrent upper airway collapse during sleep. Although the apnea-hypopnea index (AHI) remains the cornerstone of OSA diagnosis and severity assessment, it primarily reflects the frequency of respiratory events and may not adequately capture differences in respiratory event duration or the associated physiological burden. We aimed to identify novel OSA phenotypes by incorporating respiratory event duration into polysomnography-based cluster analysis.
METHODS: We retrospectively analyzed consecutive adult patients with OSA who underwent overnight polysomnography. Demographic, anthropometric, and polysomnographic characteristics were collected. Age, body mass index (BMI), neck circumference, AHI, lowest oxygen saturation (LowSpO₂), longest apnea duration (LAP), and longest hypopnea duration (LHP) were included in the clustering analysis. Hierarchical clustering using Ward's method was first performed to determine the optimal number of clusters, followed by K-means clustering to identify distinct OSA phenotypes.
RESULTS: Among 999 consecutive patients, 959 with complete clustering variables were included in the final analysis. Hierarchical clustering followed by K-means clustering identified four distinct OSA phenotypes. Cluster 1 represented a mild phenotype characterized by the lowest AHI, the shortest LAP, and preserved nocturnal oxygenation. Cluster 2 represented an obesity-associated severe phenotype with the highest BMI, the largest neck circumference, the highest AHI, and the greatest degree of nocturnal oxygen desaturation. Cluster 3 was characterized by markedly prolonged apnea duration despite only moderate AHI, whereas Cluster 4 exhibited the longest apnea and hypopnea durations accompanied by pronounced nocturnal hypoxemia. Significant differences were observed among the four phenotypes in demographic characteristics, obesity-related measures, disease severity, nocturnal oxygenation, and respiratory event duration (all P < 0.001). Importantly, respiratory event duration provided additional temporal characterization across phenotypes beyond conventional frequency-based severity assessment.
CONCLUSIONS: Respiratory event duration-enhanced clustering identified four exploratory OSA phenotypes with distinct temporal characteristics. These findings suggest that respiratory event duration may provide complementary information to conventional AHI-based severity assessment, while its clinical utility requires further validation.