Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure

REM sleep breathing: Insights beyond conventional respiratory metrics

Breathing and sleep state are tightly linked. The traditional approach to evaluation of breathing in rapid eye movement sleep has been to focus on apneas and hypopneas, and associated hypoxia or hypercapnia. However, rapid eye movement sleep breathing offers novel insights into sleep physiology and pathology, secondary to complex interactions of rapid eye movement state and cardiorespiratory biology. In this review, morphological analysis of clinical polysomnogram data to assess respiratory patterns and associations across a range of health and disease is presented. There are several relatively unique insights that may be evident by assessment of breathing during rapid eye movement sleep. These include the original discovery of rapid eye movement sleep and scoring of neonatal sleep, control of breathing in rapid eye movement sleep, rapid eye movement sleep homeostasis, sleep apnea endotyping and pharmacotherapy, rapid eye movement sleep stability, non-electroencephalogram sleep staging, influences on cataplexy, mimics of rapid eye movement behaviour disorder, a reflection of autonomic health, and insights into cardiac arrhythmogenesis. In summary, there is rich clinically actionable information beyond sleep apnea encoded in the respiratory patterns of rapid eye movement sleep.

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study

PURPOSE

Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals.

METHODS

Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments.

RESULTS

Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor.

CONCLUSIONS

Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.

Factors Associated With Residual Apnea-Hypopnea Index Variability During CPAP Treatment

BACKGROUND

CPAP is the first-line therapy for OSA. A high or variable residual apnea-hypopnea index (rAHI) reflects treatment failure and potentially is triggered by exacerbation of cardiovascular comorbidities. Previous studies showed that high rAHI and large rAHI variability are associated with underlying comorbidities, OSA characteristics at diagnosis, and CPAP equipment, including mask type and settings.

RESEARCH QUESTION

What factors are associated with predefined groups with low to high rAHI variability?

STUDY DESIGN AND METHODS

This registry-based study included patients with a diagnosis of OSA who were receiving CPAP treatment with at least 90 days of CPAP remote monitoring. We applied the hidden Markov model to analyze the day-to-day trajectories of rAHI variability using telemonitoring data. An ordinal logistic regression analysis identified factors associated with a risk of having a higher and more variable rAHI with CPAP treatment.

RESULTS

The 1,126 included patients were middle-aged (median age, 66 years; interquartile range [IQR], 57-73 years), predominantly male (n = 791 [70.3%]), and obese (median BMI, 30.6 kg/m² (IQR, 26.8-35.2 kg/m²). Three distinct groups of rAHI trajectories were identified using hidden Markov modeling: low rAHI variability (n = 393 [35%]), moderate rAHI variability group (n = 420 [37%]), and high rAHI variability group (n = 313 [28%]). In multivariate analysis, factors associated with high rAHI variability were age, OSA severity at diagnosis, heart failure, opioids and alcohol consumption, mental and behavioral disorders, transient ischemic attack and stroke, an oronasal mask, and level of leaks when using CPAP.

INTERPRETATION

Identifying phenotypic traits and factors associated with high rAHI variability will allow early intervention and the development of personalized follow-up pathways for CPAP treatment.

Effect of Widespread Sleep Apnea Screening on Progression of Atrial Fibrillation

Sleep apnea (SA) is recognized as a predictor of incident atrial fibrillation (AF) and AF recurrence after treatment. However, data on the prevalence of SA phenotypes in patients with AF and the effect of widespread SA screening on AF outcomes are scarce. We conducted a retrospective study of patients with AF referred for SA testing between March 2018 and April 2020. The screening was performed using home sleep testing or polysomnography. AF outcomes were examined by assessment of AF progression as defined by a change from paroxysmal AF to persistent AF, change in antiarrhythmic drug, having an ablation or cardioversion. Of 321 patients evaluated for AF, 251 patients (78%) completed SA testing. A total of 185 patients with complete follow-up data and SA testing were included in our analysis: 172 patients (93%) had SA; 90 of those (49%) had primarily obstructive sleep apnea, 77 patients (42%) had mixed apnea, and 5 patients (3%) had pure central apnea. Time from AF diagnosis to SA testing was associated with AF progression; after 2 years, the risk of AF progression increased (p <0.008). Continuous positive airway pressure treatment did not affect AF progression (p = 0.99). In conclusion, SA is highly prevalent in an unselected population of patients with AF, with mixed apnea being present in over 40% of the population. Early SA testing was associated with decreased rates of AF progression, likely because of earlier and potentially more aggressive pursuit of rhythm control.

Point-of-care prediction model of loop gain in patients with obstructive sleep apnea: development and validation

Background

High loop gain (unstable ventilatory control) is an important—but difficult to measure—contributor to obstructive sleep apnea (OSA) pathogenesis, predicting OSA sequelae and/or treatment response. Our objective was to develop and validate a clinical prediction tool of loop gain.

Methods

A retrospective cohort of consecutive adults with OSA (apnea–hypopnea index, AHI > 5/hour) based on in-laboratory polysomnography 01/2017–12/2018 was randomly split into a training and test-set (3:1-ratio). Using a customized algorithm (“reference standard”) loop gain was quantified from raw polysomnography signals on a continuous scale and additionally dichotomized (high > 0.7). Candidate predictors included general patient characteristics and routine polysomnography data. The model was developed (training-set) using linear regression with backward selection (tenfold cross-validated mean square errors); the predicted loop gain of the final linear regression model was used to predict loop gain class. More complex, alternative models including lasso regression or random forests were considered but did not meet pre-specified superiority-criteria. Final model performance was validated on the test-set.

Results

The total cohort included 1055 patients (33% high loop gain). Based on the final model, higher AHI (beta = 0.0016; P < .001) and lower hypopnea-percentage (beta = −0.0019; P < .001) predicted higher loop gain values. The predicted loop gain showed moderate-to-high correlation with the reference loop gain (r = 0.48; 95% CI 0.38–0.57) and moderate discrimination of patients with high versus low loop gain (area under the curve = 0.73; 95% CI 0.67–0.80).

Conclusion

To our knowledge this is the first prediction model of loop gain based on readily-available clinical data, which may facilitate retrospective analyses of existing datasets, better patient selection for clinical trials and eventually clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01950-y.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).