Morphological Prediction of Continuous Positive Airway Pressure–Associated Acute Respiratory Instability
Year: 2024
Type: article
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.
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G122882294, Multimodal Network Connectivity Architecture (MOCA) of the Brain and its Role in the Recovery of Consciousness in Comatose Cardiac Arrest Patients, SCH: INT: Collaborative Research: DeepSense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine, Comparative effectiveness of EEG-guided anti-seizure treatment in acute brain injury, Establishing a Brain Health Index, G580858672, G595859653, G6315715174, Investigation of Sleep, Late-onset Unexplained Epilepsy as a Risk Factor for Dementia, G7993515582, Integrative Motor Activity Biomarker for the Risk of Alzheimer's Risk, G9000066320, G9000081834, G9000137206, Establishing a Brain Health Index, Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality, Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain Injury
OpenAlex ID: W4402566179
