Unravelling Sleep Apnea Dynamics: Quantifying Loop Gain Using Dynamical Modelling of Ventilatory Control.

STUDY OBJECTIVES

Loop gain (LG) is a critical parameter for assessing ventilatory control stability in sleep apnea, with implications for personalized treatment. Existing LG estimation methods are hindered by complex processing and specialized equipment, limiting clinical applicability. This study aims to develop an automated method to quantify LG from respiratory inductance plethysmography (RIP) signals to enhance precision management of sleep apnea.

METHODS

Polysomnography data from Massachusetts General Hospital, high-altitude studies at Beth Israel Deaconess Medical Centre, and heart failure patients were analysed. Cases included an apnea-hypopnea index >15 and ≥ 4 hours of recorded sleep. RIP signals were filtered, normalized, and segmented into 8-minute windows. LG estimation employed an augmented Mackey-Glass equation and an expectation-maximization algorithm. Simulation experiments on synthetic breathing data with known parameter values quantified the accuracy of our parameter estimates.

RESULTS

Data from 465 patients were analysed, including 400 patients from the Massachusetts General Hospital dataset and 65 heart failure patients. The method accurately estimated LG across diverse apnea phenotypes. Patients with a higher central apnea index, high self-similarity or heart failure exhibited significantly higher median LG values (0.19, 0.27 and 0.41 respectively) compared to those with obstructive apnea (median LG = 0.11-0.14; p<.001). In addition, LG was significantly elevated during non-rapid eye movement sleep and at higher altitudes.

CONCLUSIONS

The automated LG estimation method developed in this study provides a scalable, non-invasive tool for endotyping in sleep apnea. By accurately modelling patient-specific ventilatory control, this approach supports personalized management strategies in apnea and broader clinical contexts.