Cao Wenhao, Luo Jinmei, Xiao Yi
Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Nat Sci Sleep. 2020 Nov 18;12:1023-1031. doi: 10.2147/NSS.S275252. eCollection 2020.
Obstructive sleep apnea (OSA) is a common and heterogeneous disease characterized by episodic collapse within the upper airways, which leads to reduced ventilation and adverse consequences, including hypoxia, hypercapnia, sleep fragmentation, and long-term effects such as cardiovascular comorbidities. The clinical diagnosis of OSA and its severity classification are often determined based on the apnea-hypopnea index (AHI), defining the number of apneic and hypopnea events per hour of sleep. However, the limitations of the AHI to assess disease severity have necessitated the exploration of other metrics for additional information to reflect the complexity of OSA. Novel parameters such as the hypoxic burden have the potential to better capture the main features of OSA by maximizing the information available from the polysomnogram. These emerging measures have described multidimensional qualities of sleep-disordered breathing events and breathing irregularity and will ultimately result in better management of OSA.
阻塞性睡眠呼吸暂停(OSA)是一种常见的异质性疾病,其特征是上气道间歇性塌陷,导致通气减少及包括低氧血症、高碳酸血症、睡眠片段化等不良后果,以及心血管合并症等长期影响。OSA的临床诊断及其严重程度分级通常基于呼吸暂停低通气指数(AHI)来确定,该指数定义为每小时睡眠中呼吸暂停和低通气事件的数量。然而,AHI在评估疾病严重程度方面存在局限性,因此有必要探索其他指标以获取更多信息,来反映OSA的复杂性。诸如低氧负荷等新参数有可能通过最大化多导睡眠图提供的信息,更好地捕捉OSA的主要特征。这些新兴指标描述了睡眠呼吸紊乱事件和呼吸不规则的多维度特征,并最终将实现对OSA的更好管理。