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Abstract Study Objectives Precision medicine for obstructive sleep apnea (OSA) requires noninvasive estimates of each patient’s pathophysiological “traits.” Here, we provide the first automated technique to quantify the respiratory arousal threshold—defined as the level of ventilatory drive triggering arousal from sleep—using diagnostic polysomnographic signals in patients with OSA. Methods Ventilatory drive preceding clinically scored arousals was estimated from polysomnographic studies by fitting a respiratory control model (Terrill et al.) to the pattern of ventilation during spontaneous respiratory events. Conceptually, the magnitude of the airflow signal immediately after arousal onset reveals information on the underlying ventilatory drive that triggered the arousal. Polysomnographic arousal threshold measures were compared with gold standard values taken from esophageal pressure and intraoesophageal diaphragm electromyography recorded simultaneously (N = 29). Comparisons were also made to arousal threshold measures using continuous positive airway pressure (CPAP) dial-downs (N = 28). The validity of using (linearized) nasal pressure rather than pneumotachograph ventilation was also assessed (N = 11). Results Polysomnographic arousal threshold values were correlated with those measured using esophageal pressure and diaphragm EMG (R = 0.79, p < .0001; R = 0.73, p = .0001), as well as CPAP manipulation (R = 0.73, p < .0001). Arousal threshold estimates were similar using nasal pressure and pneumotachograph ventilation (R = 0.96, p < .0001). Conclusions The arousal threshold in patients with OSA can be estimated using polysomnographic signals and may enable more personalized therapeutic interventions for patients with a low arousal threshold.

Background Despite considerable progress, it remains unclear why some patients admitted for COVID-19 develop adverse outcomes while others recover spontaneously. Clues may lie with the predisposition to hypoxemia or unexpected absence of dyspnea (‘silent hypoxemia’) in some patients who later develop respiratory failure. Using a recently-validated breath-holding technique, we sought to test the hypothesis that gas exchange and ventilatory control deficits observed at admission are associated with subsequent adverse COVID-19 outcomes (composite primary outcome: non-invasive ventilatory support, intensive care admission, or death). Methods Patients with COVID-19 ( N  = 50) performed breath-holds to obtain measurements reflecting the predisposition to oxygen desaturation ( mean desaturation after 20-s) and reduced chemosensitivity to hypoxic-hypercapnia (including maximal breath-hold duration ). Associations with the primary composite outcome were modeled adjusting for baseline oxygen saturation, obesity, sex, age, and prior cardiovascular disease. Healthy controls ( N  = 23) provided a normative comparison. Results The adverse composite outcome (observed in N  = 11/50) was associated with breath-holding measures at admission (likelihood ratio test, p  = 0.020); specifically, greater mean desaturation (12-fold greater odds of adverse composite outcome with 4% compared with 2% desaturation, p  = 0.002) and greater maximal breath-holding duration (2.7-fold greater odds per 10-s increase, p  = 0.036). COVID-19 patients who did not develop the adverse composite outcome had similar mean desaturation to healthy controls. Conclusions Breath-holding offers a novel method to identify patients with high risk of respiratory failure in COVID-19. Greater breath-hold induced desaturation (gas exchange deficit) and greater breath-holding tolerance (ventilatory control deficit) may be independent harbingers of progression to severe disease.

Therapies for obstructive sleep apnea (OSA) could be administered on the basis of a patient's own phenotypic causes (\"traits\") if a clinically applicable approach were available. Here we aimed to provide a means to quantify two key contributors to OSA-pharyngeal collapsibility and compensatory muscle responsiveness-that is applicable to diagnostic polysomnography. Based on physiological definitions, pharyngeal collapsibility determines the ventilation at normal (eupneic) ventilatory drive during sleep, and pharyngeal compensation determines the rise in ventilation accompanying a rising ventilatory drive. Thus, measuring ventilation and ventilatory drive (e.g., during spontaneous cyclic events) should reveal a patient's phenotypic traits without specialized intervention. We demonstrate this concept in patients with OSA (N = 29), using a novel automated noninvasive method to estimate ventilatory drive (polysomnographic method) and using \"gold standard\" ventilatory drive (intraesophageal diaphragm EMG) for comparison. Specialized physiological measurements using continuous positive airway pressure manipulation were employed for further comparison. The validity of nasal pressure as a ventilation surrogate was also tested (N = 11). Polysomnography-derived collapsibility and compensation estimates correlated favorably with those quantified using gold standard ventilatory drive (R = 0.83, P < 0.0001; and R = 0.76, P < 0.0001; respectively) and using continuous positive airway pressure manipulation (R = 0.67, P < 0.0001; and R = 0.64, P < 0.001; respectively). Polysomnographic estimates effectively stratified patients into high versus low subgroups (accuracy, 69-86% vs. ventilatory drive measures; P < 0.05). Traits were near-identical using nasal pressure versus pneumotach (N = 11, R ≥ 0.98, both traits; P < 0.001). Phenotypes of pharyngeal dysfunction in OSA are evident from spontaneous changes in ventilation and ventilatory drive during sleep, enabling noninvasive phenotyping in the clinic. Our approach may facilitate precision therapeutic interventions for OSA.

Upper airway surgery is often recommended to treat patients with obstructive sleep apnea (OSA) who cannot tolerate continuous positive airways pressure. However, the response to surgery is variable, potentially because it does not improve the nonanatomical factors (ie, loop gain [LG] and arousal threshold) causing OSA. Measuring these traits clinically might predict responses to surgery. Our primary objective was to test the value of LG and arousal threshold to predict surgical success defined as 50% reduction in apnea-hypopnea index (AHI) and AHI <10 events/hour post surgery. We retrospectively analyzed data from patients who underwent upper airway surgery for OSA (n = 46). Clinical estimates of LG and arousal threshold were calculated from routine polysomnographic recordings presurgery and postsurgery (median of 124 [91-170] days follow-up). Surgery reduced both the AHI (39.1 ± 4.2 vs. 26.5 ± 3.6 events/hour; p < .005) and estimated arousal threshold (-14.8 [-22.9 to -10.2] vs. -9.4 [-14.5 to -6.0] cmH2O) but did not alter LG (0.45 ± 0.08 vs. 0.45 ± 0.12; p = .278). Responders to surgery had a lower baseline LG (0.38 ± 0.02 vs. 0.48 ± 0.01, p < .05) and were younger (31.0 [27.3-42.5] vs. 43.0 [33.0-55.3] years, p < .05) than nonresponders. Lower LG remained a significant predictor of surgical success after controlling for covariates (logistic regression p = .018; receiver operating characteristic area under curve = 0.80). Our study provides proof-of-principle that upper airway surgery most effectively resolves OSA in patients with lower LG. Predicting the failure of surgical treatment, consequent to less stable ventilatory control (elevated LG), can be achieved in the clinic and may facilitate avoidance of surgical failures.

Abstract Study Objectives The presence of flow limitation during sleep is associated with adverse health consequences independent of obstructive sleep apnea (OSA) severity (apnea-hypopnea index, AHI), but remains extremely challenging to quantify. Here we present a unique library and an accompanying automated method that we apply to investigate flow limitation during sleep. Methods A library of 117,871 breaths (N = 40 participants) were visually classified (certain flow limitation, possible flow limitation, normal) using airflow shape and physiological signals (ventilatory drive per intra-esophageal diaphragm EMG). An ordinal regression model was developed to quantify flow limitation certainty using flow-shape features (e.g. flattening, scooping); breath-by-breath agreement (Cohen’s ƙ); and overnight flow limitation frequency (R2, %breaths in certain or possible categories during sleep) were compared against visual scoring. Subsequent application examined flow limitation frequency during arousals and stable breathing, and associations with ventilatory drive. Results The model (23 features) assessed flow limitation with good agreement (breath-by-breath ƙ = 0.572, p < 0.001) and minimal error (overnight flow limitation frequency R2 = 0.86, error = 7.2%). Flow limitation frequency was largely independent of AHI (R2 = 0.16) and varied widely within individuals with OSA (74[32–95]%breaths, mean[range], AHI > 15/h, N = 22). Flow limitation was unexpectedly frequent but variable during arousals (40[5–85]%breaths) and stable breathing (58[12–91]%breaths), and was associated with elevated ventilatory drive (R2 = 0.26–0.29; R2 < 0.01 AHI v. drive). Conclusions Our method enables quantification of flow limitation frequency, a key aspect of obstructive sleep-disordered breathing that is independent of the AHI and often unavailable. Flow limitation frequency varies widely between individuals, is prevalent during arousals and stable breathing, and reveals elevated ventilatory drive. Clinical trial registration: The current observational physiology study does not qualify as a clinical trial.

Background This study aimed to identify characteristics associated with cognitive impairment in older individuals with obstructive sleep apnoea (OSA) using the Addenbrooke’s Cognitive Examination-Revised (ACE-R) that could aid in stratifying those at higher risk for impairment. Methods We analysed existing cross-sectional datasets that measured the performance of 89 adult patients (aged 50–85 years) with OSA on the ACE-R cognitive test. Receiver operating characteristic curves and logistic regression analysis were utilised to identify associations between impairment status and various factors, including demographic characteristics, self-reported sleepiness, cognitive complaints, and OSA severity. Results According to established thresholds (ACE-R  ≤  88), 36% of participants were cognitively impaired. When adjusted for age and education, the strongest factors associated with impairment status were prior measures of arousal index (cut-off: ≥ 28events/hr, OR: 5.67, p  < 0.01), sleep mean SpO 2 (cut-off: ≤ 92%, OR: 3.52, p  < 0.05), 3% oxygen desaturation index (cut-off: ≥ 27events/hr, OR: 3.75, p  < 0.05), and sleep time spent under 90% SpO 2 (cut-off: ≥ 9%, OR: 3.16, p  < 0.05). Combining these factors achieved a high sensitivity ( ≥  93%) of detecting impairment within this cohort. Conversely, the apnoea-hypopnoea index, daytime sleepiness, and cognitive complaints were not associated with impairment status. Conclusions The ACE-R identified a significant proportion of patients with OSA as having cognitive impairment. Traditional indices of sleep fragmentation and hypoxaemia may allow clinicians to identify at-risk patients for cognitive evaluation, however further studies are needed to validate these findings and explore whether poor cognitive performance can be remediated via OSA treatment.

Key words: flow limitation; vigilance; response speed; sleepiness; ventilatory burden; apnea-hypopnea index; hypoxic burden; polysomnography; obstructive sleep apnea; respiratory events

Abstract Study Objectives The severity of obstructive sleep apnea (OSA) is known to vary according to sleep stage; however, the pathophysiology responsible for this robust observation is incompletely understood. The objective of the present work was to examine how ventilatory control system sensitivity (i.e. loop gain) varies during sleep in patients with OSA. Methods Loop gain was estimated using signals collected from standard diagnostic polysomnographic recordings performed in 44 patients with OSA. Loop gain measurements associated with nonrapid eye movement (NREM) stage 2 (N2), stage 3 (N3), and REM sleep were calculated and compared. The sleep period was also split into three equal duration tertiles to investigate how loop gain changes over the course of sleep. Results Loop gain was significantly lower (i.e. ventilatory control more stable) in REM (Mean ± SEM: 0.51 ± 0.04) compared with N2 sleep (0.63 ± 0.04; p = 0.001). Differences in loop gain between REM and N3 (p = 0.095), and N2 and N3 (p = 0.247) sleep were not significant. Furthermore, N2 loop gain was significantly lower in the first third (0.57 ± 0.03) of the sleep period compared with later second (0.64 ± 0.03, p = 0.012) and third (0.64 ± 0.03, p = 0.015) tertiles. REM loop gain also tended to increase across the night; however, this trend was not statistically significant [F(2, 12) = 3.49, p = 0.09]. Conclusions These data suggest that loop gain varies between REM and NREM sleep and modestly increases over the course of sleep. Lower loop gain in REM is unlikely to contribute to the worsened OSA severity typically observed in REM sleep, but may explain the reduced propensity for central sleep apnea in this sleep stage.

Periodic breathing (PB) is common in newborns and is an obvious manifestation of ventilatory control instability. However, many infants without PB may still have important underlying ventilatory control instabilities that go unnoticed using standard clinical monitoring. Methods to detect infants with \"subclinical\" ventilatory control instability are therefore required. The current study aimed to assess the degree of ventilatory control instability using simple bedside recordings in preterm infants. Respiratory inductance plethysmography (RIP) recordings were analyzed from ~20 minutes of quiet sleep in 20 preterm infants at 36 weeks post-menstrual age (median [range]: 36 [34-40]). The percentage time spent in PB was also calculated for each infant (%PB). Spontaneous sighs were identified and breath-by-breath measurements of (uncalibrated) ventilation were derived from RIP traces. Loop gain (LG, a measure of ventilatory control instability) was calculated by fitting a simple ventilatory control model (gain, time-constant, delay) to the post-sigh ventilatory pattern. For comparison, periodic inter-breath variability was also quantified using power spectral analysis (ventilatory oscillation magnitude index [VOMI]). %PB was strongly associated with LG (r2 = 0.77, p < 0.001) and moderately with the VOMI (r2 = 0.21, p = 0.047). LG (0.52 ± 0.05 vs. 0.30 ± 0.03; p = 0.0025) and the VOMI (-8.2 ± 1.1 dB vs. -11.8 ± 0.9 dB; p = 0.026) were both significantly higher in infants that displayed PB vs. those without. LG and VOMI determined from the ventilatory responses to spontaneous sighs can provide a practical approach to assessing ventilatory control instability in preterm infants. Such simple techniques may help identify infants at particular risk for ventilatory instabilities with concomitant hypoxemia and its associated consequences.

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.