Comprehensive Visualisation of Ventilation Curve and Arousal Surrounding Respiratory Events: A Novel Endotyping Approach in OSA

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 2 ) 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.