Blanchard Margaux, Vanbuis Jade, Koka Venkata
CIDELEC, Sainte-Gemmes-sur-Loire, France.
ENT and Sleep Lab, Paris, France.
J Sleep Res. 2025 Jul 11:e70145. doi: 10.1111/jsr.70145.
Obstructive sleep apnea (OSA) diagnosis, whilst primarily reliant on the apnea-hypopnea index (AHI), inadequately reflects the complex underlying mechanisms driving respiratory events. Endotyping, by identifying key physiological traits, enables personalised treatment. In this study, we propose visualising the respiratory and arousal dynamics surrounding respiratory events, providing an overview of patient-specific patterns. We analysed 20 polysomnography recordings from patients with OSA. We calculated ventilation and ventilatory drive curves for each recording during NREM sleep. In the first method, we extracted the known endotype parameters-passive ventilation (Vpassive), active ventilation (Vactive), arousal threshold (AT) and loop gain (LG)-for each respiratory event. In a second method, apnoeas and hypopneas were time-aligned, and a global representation of ventilation and ventilatory drive curves was obtained by averaging curves across events. Using the averaged curves, the same endotype parameters were extracted for comparison. The study involved 12 females and 8 males aged 22-72 with moderate obesity (median BMI 27.5 kg/m) and a median AHI of 37.5 events/h. According to Method 1, patients exhibited relatively high collapsibility (45.6 [36.8-56.0]% eupnoea), good muscle compensation (22.7 [2.9-36.7]% eupnoea), moderate AT (144.4 [131.0-153.5]% eupnoea) and relatively low LG (0.58 [0.54-0.74]). Physiological traits derived from Method 2 differed for Vpassive (higher Vpassive) and LG (lower LG), compared to Method 1. The correlation coefficients between the two methods are rho = 0.2, 0.9, 0.6 and 0.4 for Vpassive, Vactive, AT and LG, respectively. Whilst differing in some aspects, the visualisation provided by these two methods streamlines endotype identification and facilitates successful targeted treatment.
阻塞性睡眠呼吸暂停（OSA）的诊断主要依赖于呼吸暂停低通气指数（AHI），但该指数无法充分反映引发呼吸事件的复杂潜在机制。通过识别关键生理特征进行内型分类，有助于实现个性化治疗。在本研究中，我们建议对呼吸事件周围的呼吸和觉醒动态进行可视化，以概述患者特定的模式。我们分析了20例阻塞性睡眠呼吸暂停患者的多导睡眠图记录。我们计算了非快速眼动睡眠期间每次记录的通气和通气驱动曲线。在第一种方法中，我们为每个呼吸事件提取了已知的内型参数——被动通气（Vpassive）、主动通气（Vactive）、觉醒阈值（AT）和环路增益（LG）。在第二种方法中，对呼吸暂停和低通气进行时间对齐，并通过对各事件的曲线求平均值获得通气和通气驱动曲线的全局表示。使用平均曲线，提取相同的内型参数进行比较。该研究纳入了12名女性和8名男性，年龄在22至72岁之间，患有中度肥胖（BMI中位数为27.5kg/m），AHI中位数为37.5次/小时。根据方法1，患者表现出相对较高的可塌陷性（45.6[36.8 - 56.0]%的正常呼吸）；良好的肌肉代偿能力（22.7[2.9 - 36.7]%的正常呼吸）；中度的觉醒阈值（144.4[131.0 - 153.5]%的正常呼吸）和相对较低的环路增益（0.58[0.54 - 0.74]）。与方法1相比，方法2得出的生理特征在被动通气（较高的Vpassive）和环路增益（较低的LG）方面有所不同。两种方法之间的相关系数分别为：被动通气rho = 0.2、主动通气rho = 0.9、觉醒阈值rho = 0.6和环路增益rho = 0.4。虽然在某些方面存在差异，但这两种方法提供的可视化简化了内型分类，并有助于成功进行靶向治疗。
