Sleep stage continuity is associated with objective daytime sleepiness in patients with suspected obstructive sleep apnea.

STUDY OBJECTIVES: Excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA) is poorly explained by standard clinical sleep architecture metrics. We hypothesized that reduced sleep stage continuity mediates this connection independently from standard sleep architecture metrics. METHODS: 1,907 patients with suspected OSA with daytime sleepiness complaints underwent in-lab diagnostic polysomnography and next-day Multiple Sleep Latency Test (MSLT). Sleep architecture was evaluated with novel sleep-stage continuity quantifications (mean sleep stage duration and probability of remaining in each sleep stage), and conventional metrics (total N1, N2, N3 and REM times; and sleep onset latency). Multivariate analyses were utilized to identify variables associated with moderate EDS (5 ≤ mean daytime sleep latency (MSL) ≤ 10 minutes) and severe EDS (MSL < 5 minutes). RESULTS: Compared to those without EDS, participants with severe EDS had lower N3 sleep continuity (mean N3 period duration 10.4 vs 13.7 minutes, p<0.05), less N3 time (53.8 vs 76.5 minutes, p<0.05); greater total sleep time (374.0 vs 352.5 minutes, p<0.05) and greater N2 time (227.5 vs 186.8 minutes, p<0.05). After adjusting for standard sleep architecture metrics using multivariate logistic regression, decreased mean wake and N3 period duration, and the decreased probability of remaining in N2 and N3 sleep remained significantly associated with severe EDS, while the decreased probability of remaining in wake and N2 sleep were associated with moderate EDS. CONCLUSIONS: Patients with OSA with EDS experience lower sleep continuity, noticeable especially during N3 sleep and wake. Sleep-stage continuity quantifications assist in characterizing the sleep architecture and are associated with objective daytime sleepiness highlighting the need for more detailed evaluations of sleep quality.

Excessive daytime sleepiness is associated with relative delta frequency power among patients with mild OSA.

Background: Excessive daytime sleepiness (EDS) is a cause of low quality of life among obstructive sleep apnoea (OSA) patients. Current methods of assessing and predicting EDS are limited due to time constraints or differences in subjective experience and scoring. Electroencephalogram (EEG) power spectral densities (PSDs) have shown differences between OSA and non-OSA patients, and fatigued and non-fatigued patients. Therefore, polysomnographic EEG PSDs may be useful to assess the extent of EDS among patients with OSA. Methods: Patients presenting to Israel Loewenstein hospital reporting daytime sleepiness who recorded mild OSA on polysomnography and undertook a multiple sleep latency test. Alpha, beta, and delta relative powers were assessed between patients categorized as non-sleepy (mean sleep latency (MSL) ≥10 min) and sleepy (MSL <10 min). Results: 139 patients (74% male) were included for analysis. 73 (53%) were categorized as sleepy (median MSL 6.5 min). There were no significant differences in demographics or polysomnographic parameters between sleepy and non-sleepy groups. In multivariate analysis, increasing relative delta frequency power was associated with increased odds of sleepiness (OR 1.025 (95% CI 1.024-1.026)), while relative alpha and beta powers were associated with decreased odds. The effect size of delta PSD on sleepiness was significantly greater than that of either alpha or beta frequencies. Conclusion: Delta PSD during polysomnography is significantly associated with a greater degree of objective daytime sleepiness among patients with mild OSA. Further research is needed to corroborate our findings and identify the direction of potential causal correlation between delta PSD and EDS.

Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index.

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHITAT) and TST-based REI (REITST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHITAT, and REITST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHITAT, the accuracies were 68.4% and 85.9%, and based on REITST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REITST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REITST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity.

Positional sleep apnea phenotyping using machine learning and digital oximetry biomarkers.

Study Objectives. To examine the feasibility of using digital oximetry biomarkers (OBMs) and body position to identify positional obstructive sleep apnea (POSA) phenotypes.Methods. A multiclass extreme gradient boost (XGBoost) was implemented to classify between three POSA phenotypes, i.e., positional patients (PP), including supine-predominant OSA (spOSA), and supine-isolated OSA (siOSA), and non-positional patients (NPP). A total of 861 individuals with OSA from the multi ethnic study of atherosclerosis (MESA) dataset were included in the study. Overall, 43 OBMs were computed for supine and non-supine positions and used as input features together with demographic and clinical information (META). Feature selection, using mRMR, was implemented, and nested cross validation was used for the model's performance evaluation.Results. The best performance for the multiclass classification yielded a median weighted F1 of 0.79 with interquartile range (IQR) of 0.06. Binary classification between PP to NPP achieved weighted F1 of 0.87 (0.04).Conclusion. Using OBMs computed in PP and NPP with OSA, it is possible to distinguish between the different phenotypes of POSA. This data-driven algorithm may be embedded in portable home sleep tests.

Power spectral analysis of the sleep electroencephalogram in positional obstructive sleep apnea.

OBJECTIVE/BACKGROUND: Previous studies have shown that obstructive sleep apnoea (OSA) is associated with reduced delta EEG and increased beta EEG power and increased EEG slowing ratio. There are however no studies that explore differences in sleep EEG between positional obstructive sleep apnoea (pOSA) and non-positional obstructive sleep apnoea (non-pOSA) patients. PATIENTS/METHODS: 556 of 1036 consecutive patients (246 of 556 were female) undertaking polysomnography (PSG) for the suspicion of OSA met the inclusion criteria for this study. We calculated power spectra of each sleep epoch using Welch's method with ten, 4-s overlapping windows. Outcome measures such as Epworth Sleepiness Scale, SF-36 Quality of Life, Functional Outcomes of Sleep Questionnaire and Pyschomotor Vigilance Task were compared between the groups. RESULTS: Patients with pOSA had greater delta EEG power in NREM and greater N3 proportions compared to their non-pOSA counterparts. There were no differences in theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz) or beta (15-25Hz) EEG power or EEG slowing ratio between the two groups. There were also no differences in the outcome measures between these two groups. The division of pOSA into spOSA and siOSA groups showed better sleep parameters in siOSA but with no difference in sleep power spectra. CONCLUSIONS: This study partially supports our hypothesis in showing that pOSA, compared to non-pOSA, is associated with increased delta EEG power but did not show any variation to beta EEG power or EEG slowing ratio. This limited improvement in sleep quality did not translate to measurable changes to outcomes, suggesting beta EEG power or EEG slowing ratio may be key factors.

Duration of respiratory events in obstructive sleep apnea: In search of paradoxical results.

Research related to the duration of respiratory events in obstructive sleep apnea (OSA) has been scarce, perhaps due to the dominant role played by the apnea-hypopnea index (AHI) in the diagnosis and severity estimation of OSA. Lately, however, researchers and clinicians have started to acknowledge the importance of this overlooked parameter. Intuitively, 40-s-long apneas have more harmful physiological and health consequences than 10-s-long apneas. But is this the case? Here, we review the research-based evidence showing physiological, hemodynamic, clinical, sleep quality, and health consequences of long vs. short respiratory events. Most of the reviewed studies support the idea that longer respiratory events have more severe physiological and clinical consequences than shorter events, most probably due to the higher hypoxic burden associated with longer respiratory events. However, a few but highly qualified studies provide clear evidence that short respiratory events have also a deleterious effect on sleep and the physiological and clinical aspects of OSA. The somewhat paradoxical findings that short respiratory events are also associated with a high risk of all-cause mortality is a serious concern. From these results, it is therefore evident that the duration of respiratory events should be quantified when diagnosing and assessing the severity of OSA.

Characteristics of patients with positional OSA according to ethnicity and the identification of a novel phenotype-lateral positional patients: a Multi-Ethnic Study of Atherosclerosis (MESA) study.

STUDY OBJECTIVES: We investigated the characteristics of obstructive sleep apnea (OSA) positional patients' (PP) phenotypes among different ethnic groups in the Multi-Ethnic Study of Atherosclerosis (MESA) dataset. Moreover, we hypothesized the existence of a new OSA PP phenotype we coined "Lateral PP," for whom the lateral apnea-hypopnea index is at least double the supine apnea-hypopnea index. METHODS: From 2,273 adults with sleep information, we analyzed data of 1,323 participants who slept more than 4 hours and had at least 30 minutes of sleep in both the supine and the nonsupine positions. Demographics and clinical information were compared for the different PP and ethnic groups. RESULTS: 861 (65.1%) patients had OSA, and 35 (4.1%) were Lateral PP. Lateral PP patients were mainly females (62.9%), obese (median body mass index: 31.4 kg/m2), had mild-moderate OSA (94.3%), and mostly were non-Chinese American (97.1%). Among all patients with OSA, 550 (63.9%) were Supine PP and 17.7% were supine-isolated OSA. Supine PP and Lateral PP were present in 73.1% and 1.0% of Chinese Americans, 61.0% and 3.4% of Hispanics, 68.3% and 4.7% of White/Caucasian, and 56.2% and 5.2% of Black/African-American patients with OSA. CONCLUSIONS: Chinese Americans have the highest prevalence of Supine PP, whereas Black/African-American patients lean toward less Supine PP and higher Lateral PP. Lateral PP appears to be a novel OSA phenotype. However, Lateral PP was observed in a small group of patients with OSA and thus its existence should be further validated. CITATION: Ben Sason Y, Oksenberg A, Sobel JA, Behar JA. Characteristics of patients with positional OSA according to ethnicity and the identification of a novel phenotype-lateral positional patients: a Multi-Ethnic Study of Atherosclerosis (MESA) study. J Clin Sleep Med. 2023;19(3):529-538.

Duration of respiratory events in obstructive sleep apnea: Factors influencing the duration of respiratory events.

Several factors influence respiratory event duration during sleep. In general, women have shorter respiratory events compared to men as it appears that women have a more reactive upper airway contributing to the occurrence of short events. In addition, the increased amount of adipose tissue in the upper airways should make the reopening of the upper airways more difficult, leading to long respiratory events. Nevertheless, an increase in body mass index decreases the median duration of apneas, hypopneas, and desaturations in all OSA severity categories. Also, respiratory events are longer in older adults compared to younger ones, and the most likely mechanism explaining this phenomenon appears to be the increased circulatory delay associated with aging. Several studies have also shown that apnea events are longer in rapid eye movement sleep compared to non-rapid eye movement sleep. The main mechanism behind these differences appears to be the greater pharyngeal muscle relaxation during rapid eye movement sleep. Finally, sleeping position affects the duration of respiratory events; apneas and hypopneas are longer in the supine compared to lateral postures regardless of the severity of OSA. In the present report, we discuss the best-known factors influencing the duration of abnormal breathing events during sleep.

ABOSA - Freely available automatic blood oxygen saturation signal analysis software: Structure and validation.

BACKGROUND AND OBJECTIVE: Many sleep recording software used in clinical settings have some tools to automatically analyze the blood oxygen saturation (SpO2) signal by detecting desaturations. However, these tools are often inadequate for scientific research as they do not provide SpO2 signal-based parameters which are superior in the estimation of sleep apnea severity and related medical consequences. In addition, these software require expensive licenses and they lack batch analysis tools. Thus, we developed the first freely available automatic blood oxygen saturation analysis software (ABOSA) that provides sophisticated SpO2 signal-based parameters and enables batch analysis of large datasets. METHODS: ABOSA was programmed with MATLAB. ABOSA automatically detects desaturation and recovery events from the SpO2 signals (EDF files) and calculates numerous parameters, such as oxygen desaturation index (ODI) and desaturation severity (DesSev). The accuracy of the ABOSA software was evaluated by comparing its desaturation scorings to manual scorings in Kuopio (n = 1981) and Loewenstein (n = 930) sleep apnea patient datasets. Validation was performed in a second-by-second manner by calculating Matthew's correlation coefficients (MCC) and median differences in parameter values. Finally, the performance of the ABOSA software was compared to two commercial software, Noxturnal and Profusion, in 100 patient subpopulations. As Noxturnal or Profusion does not calculate novel desaturation parameters, these were calculated with custom-made functions. RESULTS: The agreements between ABOSA and manual scorings were great in both Kuopio (MCC = 0.801) and Loewenstein (MCC = 0.898) datasets. However, ABOSA slightly overestimated the desaturation parameter values. The median differences in ODIs were 0.8 (Kuopio) and 0.0 (Loewenstein) events/h. Similarly, the median differences in DesSevs were 0.02 (Kuopio) and 0.01 (Loewenstein) percentage points. In a second-by-second analysis, ABOSA performed very similarly to Noxturnal and Profusion software in both Kuopio (MCCABOSA = 0.807, MCCNoxturnal = 0.807, MCCProfusion = 0.811) and Loewenstein (MCCABOSA = 0.904, MCCNoxturnal = 0.911, MCCProfusion = 0.871) datasets. Based on Noxturnal and Profusion scorings, the desaturation parameter values were similarly overestimated compared to ABOSA. CONCLUSIONS: ABOSA is an accurate and freely available software that calculates both traditional clinical parameters and novel parameters, provides a detailed characterization of desaturation and recovery events, and enables batch analysis of large datasets. These are features that no other software currently provides making ABOSA uniquely suitable for scientific research use.

How sleepy patients differ from non-sleepy patients in mild obstructive sleep apnea?

To aim is investigate whether demographic, polysomnographic or sleep behaviour data differ between non-sleepy, sleepy and very sleepy patients with mild obstructive sleep apnea. The study population consisted of 439 consecutive adult patients diagnosed with mild obstructive sleep apnea (5 ≤ apnea-hypopnea index < 15) after a complete polysomnographic evaluation. The patients were divided into three groups based on subjective sleepiness: very sleepy (Epworth Sleepiness Scale ≥ 16, n = 59); sleepy (10 < Epworth Sleepiness Scale < 16, n = 102); and non-sleepy (Epworth Sleepiness Scale ≤ 10, n = 278). Demographic, polysomnographic and sleep behaviour data were compared between the groups. There were no statistically significant differences in breathing abnormality indices and most of the demographic features between the groups. The number of arousals was significantly higher in the very sleepy group compared with the non-sleepy group (140.8 ± 105.2 versus 107.6 ± 72.2). Very sleepy patients reported feeling sleepy during the daytime more often (42.4% versus 31.7%) and sleeping significantly less during the week compared with non-sleepy patients. Also, a significantly higher proportion of sleepy (47.1%) and very sleepy patients (44.1%) reported taking naps during weekends compared with non-sleepy patients (35.6%). In a regression analysis, also total sleep time (ß = 0.045), sleep efficiency (ß = -0.160), apnea index (ß = -0.397), apnea-hypopnea index in supine position (ß = 0.044), periodic limb movement index (ß = 0.196) and periodic limb movement-related arousal index (ß = -0.210) affected subjective daytime sleepiness. The results suggest that excessive daytime sleepiness in patients with mild obstructive sleep apnea appears to be related to inadequate sleeping habits (i.e. insufficient sleep during working days) and decreased sleep quality rather than differences in breathing abnormalities.

Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography.

STUDY OBJECTIVES: To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an automated deep learning-based solution to assess OSA-related sleep fragmentation based on photoplethysmography (PPG) signal. METHODS: A combination of convolutional and recurrent neural networks was used for PPG-based sleep staging. The models were trained using two large clinical datasets from Israel (n = 2149) and Australia (n = 877) and tested separately on three-class (wake/NREM/REM), four-class (wake/N1 + N2/N3/REM), and five-class (wake/N1/N2/N3/REM) classification. The relationship between OSA severity categories and sleep fragmentation was assessed using survival analysis of mean continuous sleep. Overlapping PPG epochs were applied to artificially obtain denser hypnograms for better identification of fragmented sleep. RESULTS: Automatic PPG-based sleep staging achieved an accuracy of 83.3% on three-class, 74.1% on four-class, and 68.7% on five-class models. The hazard ratios for decreased mean continuous sleep compared to the non-OSA group obtained with Cox proportional hazards models with 5-s epoch-to-epoch intervals were 1.70, 3.30, and 8.11 for mild, moderate, and severe OSA, respectively. With EEG-based hypnograms scored manually with conventional 30-s epoch-to-epoch intervals, the corresponding hazard ratios were 1.18, 1.78, and 2.90. CONCLUSIONS: PPG-based automatic sleep staging can be used to differentiate between OSA severity categories based on sleep continuity. The differences between the OSA severity categories become more apparent when a shorter epoch-to-epoch interval is used.

Power spectral densities of nocturnal pulse oximetry signals differ in OSA patients with and without daytime sleepiness.

BACKGROUND: As nocturnal hypoxemia and heart rate variability are associated with excessive daytime sleepiness (EDS) related to OSA, we hypothesize that the power spectral densities (PSD) of nocturnal pulse oximetry signals could be utilized in the assessment of EDS. Thus, we aimed to investigate if PSDs contain features that are related to EDS and whether a convolutional neural network (CNN) could detect patients with EDS using self-learned PSD features. METHODS: A total of 915 OSA patients who had undergone polysomnography with multiple sleep latency test on the following day were investigated. PSDs for nocturnal blood oxygen saturation (SpO2), heart rate (HR), and photoplethysmogram (PPG), as well as power in the 15-35 mHz band in SpO2 (PSPO2) and HR (PHR), were computed. Differences in PSD features were investigated between EDS groups. Additionally, a CNN classifier was developed for identifying severe EDS patients based on spectral data. RESULTS: SpO2 power content increased significantly (p < 0.002) with increasing severity of EDS. Furthermore, a significant (p < 0.001) increase in HR-PSD was found in severe EDS (mean sleep latency < 5 min). Elevated odds of having severe EDS was found in PSPO2 (OR = 1.19-1.29) and PHR (OR = 1.81-1.83). Despite these significant spectral differences, the CNN classifier reached only moderate sensitivity (49.5%) alongside high specificity (80.4%) in identifying patients with severe EDS. CONCLUSIONS: We conclude that PSDs of nocturnal pulse oximetry signals contain features significantly associated with OSA-related EDS. However, CNN-based identification of patients with EDS is challenging via pulse oximetry.

Estimating daytime sleepiness with previous night electroencephalography, electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network.

A common symptom of obstructive sleep apnea (OSA) is excessive daytime sleepiness (EDS). The gold standard test for EDS is the multiple sleep latency test (MSLT). However, due to its high cost, MSLT is not routinely conducted for OSA patients and EDS is instead evaluated using sleep questionnaires. This is problematic however, since sleep questionnaires are subjective and correlate poorly with the MSLT. Therefore, new objective tools are needed for reliable evaluation of EDS. The aim of this study was to test our hypothesis that EDS can be estimated with neural network analysis of previous night polysomnographic signals. We trained a convolutional neural network (CNN) classifier using electroencephalography, electrooculography, and chin electromyography signals from 2,014 patients with suspected OSA. The CNN was trained to classify the patients into four sleepiness categories based on their mean sleep latency (MSL); severe (MSL < 5min), moderate (5 ≤ MSL < 10), mild (10 ≤ MSL < 15), and normal (MSL ≥ 15). The CNN classified patients to the four sleepiness categories with an overall accuracy of 60.6% and Cohen's kappa value of 0.464. In two-group classification scheme with sleepy (MSL < 10 min) and non-sleepy (MSL ≥ 10) patients, the CNN achieved an accuracy of 77.2%, with sensitivity of 76.5%, and specificity of 77.9%. Our results show that previous night's polysomnographic signals can be used for objective estimation of EDS with at least moderate accuracy. Since the diagnosis of OSA is currently confirmed by polysomnography, the classifier could be used simultaneously to get an objective estimate of the daytime sleepiness with minimal extra workload.

Severe desaturations increase psychomotor vigilance task-based median reaction time and number of lapses in obstructive sleep apnoea patients.

Current diagnostic parameters estimating obstructive sleep apnoea (OSA) severity have a poor connection to the psychomotor vigilance of OSA patients. Thus, we aimed to investigate how the severity of apnoeas, hypopnoeas and intermittent hypoxaemia is associated with impaired vigilance.We retrospectively examined type I polysomnography data and corresponding psychomotor vigilance tasks (PVTs) of 743 consecutive OSA patients (apnoea-hypopnoea index (AHI) ≥5 events·h-1). Conventional diagnostic parameters (e.g. AHI and oxygen desaturation index (ODI)) and novel parameters (e.g. desaturation severity and obstruction severity) incorporating duration of apnoeas and hypopnoeas as well as depth and duration of desaturations were assessed. Patients were grouped into quartiles based on PVT outcome variables. The odds of belonging to the worst-performing quartile were assessed. Analyses were performed for all PVT outcome variables using binomial logistic regression.A relative 10% increase in median depth of desaturations elevated the odds (ORrange 1.20-1.37, p<0.05) of prolonged mean and median reaction times as well as increased lapse count. Similarly, an increase in desaturation severity (ORrange 1.26-1.52, p<0.05) associated with prolonged median reaction time. Female sex (ORrange 2.21-6.02, p<0.01), Epworth Sleepiness Scale score (ORrange 1.05-1.07, p<0.01) and older age (ORrange 1.01-1.05, p<0.05) were significant risk factors in all analyses. In contrast, increases in conventional AHI, ODI and arousal index were not associated with deteriorated PVT performance.These results show that our novel parameters describing the severity of intermittent hypoxaemia are significantly associated with increased risk of impaired PVT performance, whereas conventional OSA severity and sleep fragmentation metrics are not. These results underline the importance of developing the assessment of OSA severity beyond the AHI.

Prevalence and characteristics of positional obstructive sleep apnea (POSA) in patients with severe OSA.

PURPOSE: We assessed the prevalence of positional patients (PPs) and the main predictors of positional dependency in severe obstructive sleep apnea (OSA). A simulated effect of positional therapy (PT) vs. continuous positive airway pressure (CPAP) was also assessed. METHODS: Polysomnographic recordings of 292 consecutive patients with severe OSA (Apnea-Hypopnea Index (AHI) ≥ 30) who slept > 4 h and had ≥ 30 min sleep in both supine and lateral positions were assessed. PPs were defined to have a supine AHI/lateral AHI ratio ≥ two and non-positional patients (NPPs) a supine AHI/lateral AHI ratio < two. RESULTS: A total of 35.3% of the severe OSA patients were PPs. They were less obese and had less severe OSA (p < 0.001) compared with NPPs. The percentage of total apnea-hypopnea time from total sleep time (AHT%) was the most significant predictor for positional dependency. By sleeping in the lateral posture (i.e. after simulated PT), 78 (75.7%) PPs obtained significant improvement of their OSA severity and 9 (8.7%) of them became "non-OSA". Moreover, if CPAP was used only for 50% of total sleep time, 53 patients (18.2%) gained more benefit from avoiding the supine posture than from CPAP therapy. CONCLUSIONS: More than a third of the studied severe OSA patients were PPs. These patients could achieve a significant decrease in the number and severity of apneas and hypopneas by adopting the lateral posture, suggesting that PT may be a valuable therapy for a significant portion of these severe OSA patients who for whatever reason are not being treated by CPAP. TRIAL REGISTRY: ClinicalTrials.gov Identifier: NCT03232658.

Obstructive Sleep Apnea: Do Positional Patients Become Nonpositional Patients With Time?

OBJECTIVES: Obstructive sleep apnea (OSA) patients with breathing abnormalities only or mainly in the supine posture are designated positional patients (PPs), whereas nonpositional patients (NPPs) have many breathing abnormalities in both lateral and supine postures. Positional therapy (PT), the avoidance of the supine posture during sleep, is the obvious treatment for PPs. The stability over time of being PP and leading factors that are involved in converting a PP to an NPP are addressed. METHODS: We analyzed polysomnographic (PSG) recordings of 81 consecutive adult patients with OSA who were judged to be PPs at the first PSG evaluation, and their follow-up PSGs were obtained after an average period of 6.6 years. RESULTS: The follow-up PSGs indicated that 57 PPs (70.4%) remained PPs, whereas 24 (29.6 %) converted to NPPs. Among PPs and NPPs, body mass index (P ≤ 0.05), overall Apnea-Hypopnea Index (AHI, P ≤ 0.087), and lateral AHI (P ≤ 0.046) increased and minimum SpO2 during rapid eye movement (REM) sleep (P ≤ 0.028) decreased significantly during the follow-up. However, among patients who became NPPs, the changes in these parameters were significantly (P ≤ 0.05) more pronounced compared to the patients who remained PPs. CONCLUSION: After an average of 6.6 years, 70.4% of PPs remained PPs. Therefore, if adherence for PT is good, they could continue to benefit from this therapy. For those who turned to NPPs, PT will not be the optimal treatment anymore; thus, these patients should be frequently monitored. Furthermore, an early treatment of PPs with PT would be highly beneficial to prevent worsening of their OSA. LEVEL OF EVIDENCE: 2b Laryngoscope, 130:2263-2268, 2020.

Severity of Desaturations Reflects OSA-Related Daytime Sleepiness Better Than AHI.

STUDY OBJECTIVES: The aim was to investigate how the severity of apneas, hypopneas, and related desaturations is associated with obstructive sleep apnea (OSA)-related daytime sleepiness. METHODS: Multiple Sleep Latency Tests and polysomnographic recordings of 362 patients with OSA were retrospectively analyzed and novel diagnostic parameters (eg, obstruction severity and desaturation severity), incorporating severity of apneas, hypopneas, and desaturations, were computed. Conventional statistical analysis and multivariate analyses were utilized to investigate connection of apnea-hypopnea index (AHI), oxygen desaturation index (ODI), conventional hypoxemia parameters, and novel diagnostic parameters with mean daytime sleep latency (MSL). RESULTS: In the whole population, 10% increase in values of desaturation severity (risk ratio = 2.01, P < .001), obstruction severity (risk ratio = 2.18, P < .001) and time below 90% saturation (t90%) (risk ratio = 2.05, P < .001) induced significantly higher risk of having mean daytime sleep latency ≤ 5 minutes compared to 10% increase in AHI (risk ratio = 1.63, P < .05). In severe OSA, desaturation severity had significantly (P < .02) stronger negative correlation (ρ = -.489, P < .001) with mean daytime sleep latency compared to AHI (ρ = -.402, P < 0.001) and ODI (ρ = -.393, P < .001). Based on general regression model, desaturation severity and male sex were the most significant factors predicting daytime sleep latency. CONCLUSIONS: Severity of sleep-related breathing cessations and desaturations is a stronger contributor to daytime sleepiness than AHI or ODI and therefore should be included in the diagnostics and severity assessment of OSA. CITATION: Kainulainen S, Töyräs J, Oksenberg A, Korkalainen H, Sefa S, Kulkas A, Leppänen T. Severity of desaturations reflects OSA-related daytime sleepiness better than AHI. J Clin Sleep Med. 2019;15(8):1135-1142.

Required CPAP usage time to normalize AHI in obstructive sleep apnea patients: a simulation study.

OBJECTIVE: Adherence to continuous positive airway pressure (CPAP) is often limited. The aim of the current work was to create a simulation tool to enable determination of the individual CPAP therapy time required to normalize apnea-hypopnea index (AHI) (<5 events h-1) in a cohort of OSA patients. APPROACH: Polygraphic studies of 1989 consecutive patients were analyzed. CPAP therapy was simulated in 5 min intervals starting from the beginning of the night and continuing until the end. In simulation, events inside the simulated CPAP therapy periods were considered to be prevented. The cutoff points where AHI reached a normal level of < 5 events h-1 were determined for mild, moderate and severe OSA categories. MAIN RESULTS: The median values of the required simulated CPAP usage times to normalize the AHI values were 3.3 h, 5.6 h and 6.5 h in the mild, moderate and severe OSA categories, respectively. However, there were great differences between individuals in the CPAP usage times required to normalize AHI. SIGNIFICANCE: An arbitrary threshold for CPAP adherence (e.g. 4 h) leaves many OSA patients with a significant residual AHI, which could explain why some clinical trials fail to show significant benefits. Thresholds for adherence should be adjusted based on the patient-specific distribution of obstruction events during the night.