Distinct polysomnographic and ECG-spectrographic phenotypes embedded within obstructive sleep apnea

摘要

BackgroundThe primary metric extracted from the polysomnogram in patients with sleep apnea is the apnea-hypopnea index (or respiratory disturbance index) and its derivatives. Other phenomena of possible importance such as periods of stable breathing, features suggestive of high respiratory control loop gain, and sleep fragmentation phenotypes are not commonly generated in clinical practice or research. A broader phenotype designation can provide insights into biological processes, and possibly clinical therapy outcome effects. MethodsThe dataset used for this study was the archived baseline diagnostic polysomnograms from the Apnea Positive Pressure Long-term Efficacy Study (APPLES). The electrocardiogram (ECG)-derived cardiopulmonary coupling sleep spectrogram was computed from the polysomnogram. Sleep fragmentation phenotypes used thresholds of sleep efficiency (SE)?≤?70%, non-rapid eye movement (NREM) sleep N1?≥?30%, wake after sleep onset (WASO)?≥?60?min, and high frequency coupling (HFC) on the ECG-spectrogram?≤?30%. Sleep consolidation phenotypes used thresholds of SE?≥?90%, WASO?≤?30?min, HFC?≥?50% and N1?≤?10%. Multiple and logistic regression analysis explored cross-sectional associations with covariates and across phenotype categories. NREM vs. REM dominant apnea categories were identified when the NREM divided by REM respiratory disturbance index (RDI) was?>?1. ResultsThe data was binned first into mild, moderate, severe and extreme categories based on the respiratory disturbance index of p = ConclusionsDistinct phenotypes are readily seen at all severities of sleep apnea, and can be identified from conventional polysomnography. The ECG-spectrogram analysis provides further phenotypic differentiation.