Morphological Prediction of Continuous Positive Airway Pressure–Associated Acute Respiratory Instability
Thijs E. Nassi, Eline Oppersma, Gonzalo Labarca, Dirk W. Donker, M. Brandon Westover, Robert J. Thomas
Abstract
Abstract Rationale Multiple mechanisms are involved in the pathogenesis of obstructive sleep apnea (OSA). Increased loop gain (LG) is a key target for precision OSA care and may be associated with treatment intolerance when the upper airway is the sole therapeutic target. Morphological or computational estimation of LG is not yet widely available or fully validated, and there is a need for improved phenotyping and/or endotyping of apnea to advance its therapy and prognosis. Objectives This study proposes a new algorithm to assess self-similarity (SS) as a signature of increased LG using respiratory effort signals and presents its use to predict the probability of acute failure (i.e., high residual event counts) of continuous positive airway pressure therapy. Methods Effort signals from 2,145 split-night polysomnography studies from the Massachusetts General Hospital were analyzed for SS and used to predict acute continuous positive airway pressure therapy effectiveness. Logistic regression models were trained and evaluated using fivefold cross-validation. Results Receiver operating characteristic and precision-recall curves with area under the curve values of 0.82 and 0.84, respectively, were obtained. SS combined with the central apnea index (CAI) and hypoxic burden outperformed CAI alone. Even in those with a low CAI by conventional scoring criteria or only mild desaturation, SS was related to poor therapy outcomes. Conclusions The proposed algorithm for assessing SS as a measure of expressed high LG is accurate and noninvasive and has the potential to improve phenotyping and/or endotyping of apnea, leading to more precise OSA treatment strategies.