Unraveling sleep apnea dynamics: quantifying loop gain using dynamical modeling of ventilatory control

The automated LG estimation method developed in this study provides a scalable, non-invasive tool for endotyping in sleep apnea. By accurately modeling patient-specific ventilatory control, this approach supports personalized management strategies in apnea and broader clinical contexts. Statement of Significance This study presents an innovative method for estimating ventilatory control stability using respiratory inductance plethysmography signals, offering a practical, scalable solution for routine clinical use. By enabling detailed characterization of ventilatory control dynamics, the method can differentiate sleep apnea phenotypes and identify patients at elevated risk of ventilatory instability. This has direct clinical implications, such as guiding personalized treatment strategies, predicting continuous positive airway pressure tolerance, and flagging patients for possible adjunctive therapies like oxygen supplementation or carbonic anhydrase inhibitors. Furthermore, the fully automated nature of our approach enables repeated assessments over time, facilitating longitudinal monitoring of treatment efficacy and disease progression. By advancing diagnostic precision and treatment tailoring, this innovation has the potential to improve the management of sleep-disordered breathing and related conditions.