Abstract
STUDY OBJECTIVES: Multilevel upper airway surgery is effective for some patients with obstructive sleep apnea (OSA), but predicting the response to surgery remains a challenge. The underlying endotypes of OSA include upper airway collapsibility, muscle compensation, loop gain, and the arousal threshold. This study aimed to explore the effect of surgery on polysomnography (PSG)-derived OSA endotypes and establish a surgical response prediction model. METHODS: Our study included 54 Chinese patients with OSA who underwent multilevel upper airway surgery. Participants underwent PSG before and after surgery with a median follow-up time of 6.5 months. Using AHIBaseline/AHIpost-surgery ≥ 2 and AHIpost-surgery < 10 events/h as criteria, participants were classified as surgery responders and non-responders. The surgical success rate was 26%. These endotypic traits were derived from a standard PSG data by validated methods. RESULTS: The surgery altered both anatomical and non-anatomical endotypic traits, including increased Vpassive (baseline vs post-surgery: 51.5 [18.7-84.2] vs 86.8 [67.4-93.7] %Veupnea, p < .001), decreased loop gain (baseline vs post-surgery: 0.7 [0.7-0.8] vs 0.6 [0.5-0.6]; p < .001), and a higher arousal threshold (baseline vs post-surgery: 202.9 [183.7-222.0] vs 160.7 [143.9-177.4] %Veupnea; p < .001). However, it did not significantly affect muscle compensation. Fully adjusted logistic regression analyses indicated that a favorable response to surgery was independently associated with a lower LG (OR [CI 95%], 0.1 [0.0-0.5], p = .032). In patients with improved muscle compensation or a more collapsible airway (lower Vpassive), a lower loop gain was more strongly indicative of success. However, when muscle compensation was lower or collapsibility was less severe (higher Vpassive), a lower loop gain was less predictive of success. CONCLUSIONS: This study demonstrated that multilevel upper airway surgery altered both anatomical and non-anatomical endotypes in Chinese patients with OSA. An endotype based regression model may meaningfully predict surgical success.