Endotypic traits of supine position and supine-predominant obstructive sleep apnoea in Asian patients.

BACKGROUND: Over half of all cases of obstructive sleep apnoea (OSA) are classified as supine-related OSA; however, the pathological endotype during supine position is not fully understood. This study aims to investigate the endotypic traits of supine-predominant OSA and explore the variations in endotypic traits between the supine and lateral positions. METHODS: We prospectively recruited 689 adult patients with OSA from a single sleep centre between April 2020 and December 2022. Endotypic traits, namely arousal threshold, collapsibility, loop gain and upper airway muscle compensation, were retrieved from polysomnographic signals. We identified spOSA by a supine to non-supine apnoea-hypopnoea index (AHI) ratio >2. We cross-sectionally compared demographic and endotypic traits between supine-predominant OSA and non-positional OSA and examined the associations between supine-predominant OSA and endotypic traits. Additionally, we compared the changes in endotypic traits between supine and lateral positions in patients with supine-predominant OSA and non-positional OSA. RESULTS: In our study sample, 75.8% of patients were identified as having supine-predominant OSA. Compared to non-positional OSA, supine-predominant OSA was associated with low collapsibility (ß= -3.46 %eupnoea, 95% CI -5.93- -1.00 %eupnoea) and reduced compensation (ß= -6.79 %eupnoea, 95% CI -10.60- -2.99 %eupnoea). When transitioning from the lateral to supine position, patients with supine-predominant OSA had a substantial decrease in compensation compared to those with non-positional OSA (-11.98 versus -6.28 %eupnoea). CONCLUSIONS: Supine-predominant OSA is the prevalent phenotype of OSA in Asian patients. Inadequate upper airway compensation appears to be a crucial underlying pathology in patients with supine-predominant OSA.

Continuous positive airway pressure and adherence in patients with different endotypes of obstructive sleep apnea.

Determining the endotypes of obstructive sleep apnea (OSA) has potential implications for precision interventions. Here we assessed whether continuous positive airway pressure (CPAP) treatment outcomes differ across endotypic subgroups. We conducted a retrospective analysis of data obtained from 225 patients with moderate-to-severe OSA from a single sleep centre. Polysomnographic and CPAP titration study data were collected between May 2020 and January 2022. One-month CPAP treatment adherence was followed. Obstructive sleep apnea endotypes, namely arousal threshold, collapsibility, loop gain, and upper airway gain were estimated from polysomnography and dichotomised as high versus low. We examined associations between endotypic subgroups and (1) optimal CPAP titration pressure, (2) CPAP-related improvements in sleep architecture (proportions of slow-wave and rapid eye movement (REM) sleep), and (3) CPAP adherence. We observed that patients with high collapsibility required a higher CPAP pressure than those with low collapsibility (∆ = 0.4 cmH2 O, 95% confidence interval [CI] = 0.3-1.7). A larger increase in slow-wave sleep and in REM sleep proportions after CPAP treatment were observed in patients with a high arousal threshold, high collapsibility, high loop gain, or high upper airway gain than in those with low levels of endotypes. High loop gain and high collapsibility were independently associated with longer CPAP use hours per night (∆ = 0.6 h, 95% CI = 0.2-1.5 and ∆ = 0.3 h, 95% CI = 0.03-1.5, respectively). In conclusion, different endotypic subgroups of OSA exhibit a difference in outcomes of CPAP treatment. Knowledge of endotypes may help clinicians to understand which patients are expected to benefit most from CPAP therapy prior to its administration.

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis.

Rationale: Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. Objectives: To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. Methods: We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Results: Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Conclusions: Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

The effect of hyperoxia on ventilation during recovery from general anesthesia: A randomized pilot study for a parallel randomized controlled trial.

STUDY OBJECTIVE: While supplemental O2 inhalation corrects hypoxemia, its effect on post-anesthesia ventilation remains unknown. This pilot trial tested the hypothesis that hyperoxia increases the time spent with a transcutaneous PCO2 (TcPCO2) > 45 mmHg, compared with standard O2 supplementation. DESIGN: Single-blinded, parallel two-arm randomized pilot trial. SETTING: University hospital. PATIENTS: 20 patients undergoing robotic-assisted laparoscopic nephrectomy. MEASUREMENTS: After institutional approval and informed consent, patients were randomized to receive O2 titrated to arterial saturation (SpO2): 90-94% (Conservative O2, N =10), or to SpO2 > 96% (Liberal O2, N = 10) for up to 90 min after anesthesia. Continuous TcPCO2, respiratory inductance plethysmography (RIP), and SpO2, were recorded. We calculated the percentage of time at TcPCO2 > 45 mmHg for each patient and compared the two groups using analysis of covariance, adjusting for sex, age, and body mass index. We also estimated the sample size required to detect the between-group difference observed in this pilot trial. RIP signals were used to calculate apnea/hypopnea index (AHI), which was then compared between two groups. MAIN RESULTS: The mean percentage of time with a TcPCO2 > 45 mmHg was 80.6% for the Conservative O2 (N=9) and 61.2% for the Liberal O2 (N=10) group [between-group difference of 19.4% (95% CI: -18.7% to 57.6%), P = 0.140]. With an observed effect size of 0.73, we estimated that 30 participants per group are required, to demonstrate this difference with a power of 80% at a two-sided alpha of 5%. Means SpO2 were 94.5% and 99.9% for the Conservative O2 and the Liberal O2 groups, respectively. AHI was significantly higher in the Conservative O2, compared with the Liberal O2 group (median AHI: 16 vs. 3; P = 0.0014). CONCLUSIONS: Hyperoxia in the post-anesthesia period reduced the time spent at TcPCO2 > 45 mmHg and significantly decreased AHI, while mean SpO2 ranged inside the a priori defined limits. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04723433.

Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography.

PURPOSE: In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleepTM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). METHODS: Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). RESULTS: We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem. CONCLUSION: The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.

A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study.

Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently, the methods Sands et al. developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors' code, which is computationally expensive and requires technological expertise to run. We present a reimplementation and validation of the integrity of the original authors' code by reproducing the endo-Phenotyping Using Polysomnography (PUP) method of Sands et al. The original MATLAB methods were reprogrammed in Python; efficient algorithms were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p < 10-6 for all): ventilation at eupnea V̇ passive (ICC = 0.97), ventilation at arousal onset V̇ active (ICC = 0.97), loop gain (ICC = 0.96), and arousal threshold (ICC = 0.90). We successfully implemented the original PUP method by Sands et al. providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.