Mortality-risk-based apnea-hypopnea index thresholds for diagnostics of obstructive sleep apnea

Body mass index mediates the association between plasma lipid concentrations and the prevalence of obstructive sleep apnea among US adults: a cross-sectional study

Background

The association between obstructive sleep apnea (OSA) and plasma lipid concentrations is not consistent. This study aimed to investigate the association of plasma lipid concentrations with the prevalence of OSA among US adults, with an additional examination of the mediating effect of body mass index (BMI).

Methods

This cross-sectional study included 8,086 individuals who participated in the National Health and Nutrition Examination Survey (NHANES), conducted from 2005 to 2008 and 2015-2018. Multivariable logistic regression analysis was conducted to compute the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between plasma lipid concentrations and the prevalence of OSA. Additionally, subgroup analysis was used to explore the potential interactions. Generalized additive models (GAM) were constructed to evaluate the nonlinear relationships between lipid concentrations and OSA. Furthermore, mediation analysis was performed to assess the potential mediating role of BMI.

Results

In the fully adjusted model, when comparing the lowest quartile, the ORs for the prevalence of OSA among participants in the highest quartile were 1.367 (95% CI, 1.107-1.688) for triglyceride and 1.212 (95% CI, 1.004-1.462) for low-density lipoprotein cholesterol (LDL-C). However, total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) were not associated with OSA. Notably, the relationship between triglyceride and OSA differed in the subgroups of gender, race, and body mass index (BMI) (P for interaction <0.05). Furthermore, we discovered an inverted U-shaped association between triglyceride and OSA (inflection point: 0.813 mmol/L). Causal mediation analysis revealed that BMI significantly mediated the relationship between triglyceride and the prevalence of OSA.

Conclusions

This study revealed that an elevated level of triglyceride increased the prevalence of OSA, and this effect was potentially mediated through BMI. Lowering triglyceride concentration may help to reduce the prevalence of OSA.

Association of obstructive sleep apnea syndrome with polycystic ovary syndrome through bidirectional Mendelian randomization

Background

Observational studies have established a link between polycystic ovary syndrome (PCOS) and obstructive sleep apnea syndrome (OSAS), with obesity being a significant confounding factor that complicates the understanding of causality. This study seeks to clarify the causal relationship by utilizing bidirectional two-sample Mendelian randomization (MR) analysis.

Methods

A bidirectional MR strategy was implemented to investigate the potential causal relationship between PCOS and OSAS. Instrumental variables (IVs) for PCOS were sourced from a dataset comprising 3,609 cases and 229,788 controls. For OSAS, statistical data were obtained from a genome-wide association study (GWAS) involving 38,998 subjects, alongside a control group of 336,659 individuals. Our MR analysis utilized several methods, including inverse variance weighted (IVW), weighted mode, weighted median, simple mode, and MR-Egger, primarily focusing on the IVW technique. Sensitivity tests were conducted to ensure the robustness of our findings.

Results

Utilizing the IVW method, we identified a notable causal association from OSAS to PCOS, with an odds ratio (OR) of 1.463 and a 95% confidence interval (CI) of 1.086-1.971 (p = 0.012). In the opposite direction, PCOS also appeared to significantly affect OSAS development, indicated by an OR of 1.041 and a 95% CI of 1.012-1.072 (p = 0.006). The MR-Egger intercept test showed no evidence of directional pleiotropy, affirming the credibility of our causal findings (p > 0.05).

Conclusion

This study suggests a bidirectional causal relationship between PCOS and an increased risk of OSAS. These insights could guide future screening and prevention strategies for both conditions.

Review and perspective on sleep-disordered breathing research and translation to clinics

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

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.

Utilizing Envelope Analysis of a Nasal Pressure Signal for Sleep Apnea Severity Estimation

Obstructive sleep apnea (OSA) severity assessment is based on manually scored respiratory events and their arbitrary definitions. Thus, we present an alternative method to objectively evaluate OSA severity independently of the manual scorings and scoring rules. A retrospective envelope analysis was conducted on 847 suspected OSA patients. Four parameters were calculated from the difference between the nasal pressure signal's upper and lower envelopes: average (AV), median (MD), standard deviation (SD), and coefficient of variation (CoV). We computed the parameters from the entirety of the recorded signals to perform binary classifications of patients using three different apnea-hypopnea index (AHI) thresholds (5-15-30). Additionally, the calculations were undertaken in 30-second epochs to estimate the ability of the parameters to detect manually scored respiratory events. Classification performances were assessed with areas under the curves (AUCs). As a result, the SD (AUCs ≥ 0.86) and CoV (AUCs ≥ 0.82) were the best classifiers for all AHI thresholds. Furthermore, non-OSA and severe OSA patients were separated well with SD (AUC = 0.97) and CoV (AUC = 0.95). Respiratory events within the epochs were identified moderately with MD (AUC = 0.76) and CoV (AUC = 0.82). In conclusion, envelope analysis is a promising alternative method by which to assess OSA severity without relying on manual scoring or the scoring rules of respiratory events.

The Burden of Respiratory Alterations during Sleep on Comorbidities in Obstructive Sleep Apnoea (OSA)

BACKGROUND

Obstructive sleep apnoea (OSA) has an important impact on the risk of morbidity and mortality, so we have designed the present study to understand which factor is most involved in the risk of developing a comorbidity between OSA severity and nocturnal hypoxemia.

METHODS

A retrospective study was conducted on 617 adult subjects who were referred to our unit for a suspicion of OSA between January 2018 and January 2020.

RESULTS

Sleep investigations performed by participants (72% male and obese in 70% of cases) reported an overall mean apnoea-hypopnoea index (AHI) of 44.0 ± 24.8 events·h-1. Overall, 66% were classified as severe OSA and 76% experienced nocturnal hypoxemia. By analysing the burden of OSA severity and nocturnal hypoxemia on the comorbidities risk, multivariate analysis highlighted the predominant role of age and obesity. Accordingly, after the exclusion of the older and obese participants from the analyses, we noticed that severe OSA was related to the risk of hypertension (odds ratio (OR) = 3.0 [95% confidence interval [CI] 1.4-6.2], p = 0.004) as well as nocturnal hypoxemia (OR = 2.6 [95% CI 1.2-5.4], p = 0.01).

CONCLUSIONS

The study seems to suggest that in young, non-obese subjects, OSA is a predisposing factor for the risk of developing hypertension.

Associations of sleep apnea risk and oxygen desaturation indices with cerebral small vessel disease burden in patients with stroke

Background

Obstructive sleep apnea (OSA) is associated with cerebral small vessel disease (CSVD). Nonetheless, whether OSA-risk determined by a simple screening questionnaire or indices quantifying nocturnal hypoxemia other than the conventional apnea–hypopnea index (AHI) by the home sleep apnea test (HSAT) associated with CSVD burden remains uncertain.

Methods

From 2018 to 2021, we recruited patients with transient ischemic attack (TIA)/minor stroke from the Queen Mary Hospital Acute Stroke Unit and TIA/Stroke Outpatient Clinics. Logistic regression models were applied to determine the association of baseline OSA-risk (on STOP-BANG questionnaire) or HSAT-derived indices quantifying nocturnal hypoxemia with global burden/individual markers of CSVD on MRI. Indices included oxygen desaturation (≥3%) index (ODI), minimum oxygen saturation (SpO₂), percentage of total sleep time with an oxygen saturation <90% (CT90%), and desaturation duration (≥3%, DesDur).

Results

In 283 patients with TIA/minor stroke (mean age 65 years, 64% men), OSA-risk was significantly associated with total CSVD score (multivariate-adjusted odds ratio: 1.23, 95% confidence interval 1.01–1.51), presence of lacunes [1.39 (1.09–1.79)] and burden of basal ganglia PVSs [1.32 (1.06–1.67)]. In 85/283 patients who completed HSAT, neither AHI, minimum SpO₂ nor CT90% was associated with CSVD burden. Nonetheless, ODI and DesDur remained significantly associated with total CSVD score after covariate adjustment: ODI [1.04 (1.01–1.07)] and DesDur [1.04 (1.01–1.08)].

Conclusion

In patients with TIA/minor stroke, high OSA-risk was associated with a greater CSVD burden. Oxygen desaturation indices (ODI and DesDur) rather than AHI were independently associated with global CSVD burden, indicating that longer and more severe desaturations may contribute to the pathogenesis of CSVD.

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea

The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.

Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults

Overnight pulse oximetry has shown usefulness to simplify obstructive sleep apnea (OSA) diagnosis when combined with machine-learning approaches. However, the development and evaluation of a single model with ability to reach high diagnostic performance in both community-based non-referral and clinical referral cohorts are still pending. Since ensemble-learning algorithms are known for their generalization ability, we propose a least-squares boosting (LSBoost) model aimed at estimating the apnea-hypopnea index (AHI), as the correlate clinical measure of disease severity. A thorough characterization of 8,762 nocturnal blood-oxygen saturation signals (SpO2) obtained at home was conducted to extract the oximetric information subsequently used in the training, validation, and test stages. The estimated AHI derived from our model achieved high diagnostic ability in both referral and non-referral cohorts reaching intra-class correlation coefficients within 0.889-0.924, and Cohen's κ within 0.478-0.663 when considering the four OSA severity categories. These resulted in accuracies ranging 87.2%-96.6%, 81.1%-87.6%, and 91.6%-94.6% when assessing the three typical AHI severity thresholds, 5 events/hour (e/h), 15 e/h, and 30 e/h, respectively. Our model also revealed the importance of the SpO2 predictors, thereby minimizing the 'black box' perception traditionally attributed to the machine-learning approaches. Furthermore, a decision curve analysis emphasized the clinical usefulness of our proposal. Therefore, we conclude that the LSBoost-based model can foster development of clinically applicable and cost saving protocols for detection of patients attending primary care services, or to avoid full polysomnography in specialized sleep facilities, thus demonstrating the diagnostic usefulness of SpO2 signals obtained at home.

Self-Applied Home Sleep Recordings: The Future of Sleep Medicine

Sleep disorders form a massive global health burden and there is an increasing need for simple and cost-efficient sleep recording devices. Recent machine learning-based approaches have already achieved scoring accuracy of sleep recordings on par with manual scoring, even with reduced recording montages. Simple and inexpensive monitoring over multiple consecutive nights with automatic analysis could be the answer to overcome the substantial economic burden caused by poor sleep and enable more efficient initial diagnosis, treatment planning, and follow-up monitoring for individuals suffering from sleep disorders.

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentation of obstructive sleep apnea (OSA) patients. To test this hypothesis, we applied deep learning-based sleep staging to identify sleep stages with the traditional approach and by using overlapping 30-second epochs with 15-, 5-, 1-, or 0.5-second epoch-to-epoch duration. A dataset of 446 patients referred for polysomnography due to OSA suspicion was used to assess differences in the sleep architecture between OSA severity groups. The amount of wakefulness increased while REM and N3 decreased in severe OSA with shorter epoch-to-epoch duration. In other OSA severity groups, the amount of wake and N1 decreased while N3 increased. With the traditional 30-second epoch-to-epoch duration, only small differences in sleep continuity were observed between the OSA severity groups. With 1-second epoch-to-epoch duration, the hazard ratio illustrating the risk of fragmented sleep was 1.14 (p = 0.39) for mild OSA, 1.59 (p < 0.01) for moderate OSA, and 4.13 (p < 0.01) for severe OSA. With shorter epoch-to-epoch durations, total sleep time and sleep efficiency increased in the non-OSA group and decreased in severe OSA. In conclusion, more detailed sleep analysis emphasizes the highly fragmented sleep architecture in severe OSA patients which can be underestimated with traditional sleep staging. The results highlight the need for a more detailed analysis of sleep architecture when assessing sleep disorders.

A validation study of an esophageal probe-based polygraph against polysomnography in obstructive sleep apnea

STUDY OBJECTIVES

The aim of this study was to validate the automatically scored results of an esophageal probe-based polygraph system (ApneaGraph® Spiro) against manually scored polysomnography (Nox A1, PSG) results. We compared the apnea-hypopnea index, oxygen saturation index, and respiratory disturbance index of the devices.

METHODS

Consenting patients, referred for obstructive sleep apnea workup, were tested simultaneously with the ApneaGraph® Spiro and Nox A1® polysomnograph. Each participant made one set of simultaneous registrations for one night. PSG results were scored independently. Apnea-hypopnea index, oxygen desaturation index, and respiratory disturbance index were compared using Pearson's correlation and scatter plots. Sensitivity, specificity, and positive likelihood ratio of all indices at 5, 15, and 30 were calculated.

RESULTS

A total of 83 participants had successful registrations. The apnea-hypopnea index showed sensitivity of 0.83, specificity of 0.95, and a positive likelihood ratio of 5.11 at an index cutoff of 15. At a cutoff of 30, the positive likelihood ratio rose to 31.43. The respiratory disturbance index showed high sensitivity (> 0.9) at all cutoffs, but specificity was below 0.5 at all cutoffs. Scatterplots revealed overestimation in mild OSA and underestimation in severe OSA for all three indices.

CONCLUSIONS

The ApneaGraph® Spiro performed acceptably when OSA was defined by an AHI of 15. The equipment overestimated mild OSA and underestimated severe OSA, compared to the PSG.

The Impact of Obstructive Sleep Apnea on the Sleep of Critically Ill Patients

Obstructive sleep apnea is becoming increasingly prevalent in society and thus critical care practitioners need to be prepared to care for these patients in the intensive care unit. Preparation begins with equipping the critical care nurse with the knowledge necessary to provide interventions which can enhance patient outcomes and mitigate complications.

Prevalence of Obstructive Sleep Apnea in Orthodontic Patients with Different Skeletal Classes Using STOP-BANG Questionnaire: An Observational Study

AIM: The aim of the study was to determine the prevalence of obstructive sleep apnea (OSA) in orthodontic adult patients with different skeletal classes and no pathology in the airway as detected from the lateral cephalometric radiographs using the STOP-BANG questionnaire. METHODS: The sample comprised 309 subjects (152 males and 157 females) collected from the Egyptian population presented to the orthodontic follicular unit extraction for treatment. The sample was divided into three groups based on the anteroposterior relationship between maxilla and mandible (ANB angle) as evident from pretreatment lateral cephalometric. Patients were asked to fill out general medical history, sign a consent form, and fill in the STOP-BANG questionnaire. Patient’s neck size and height were measured using tape measuring tool and weighing scale, respectively. Body mass index (BMI) was obtained. After submission of the questionnaire, scores were measured for each patient to evaluate the severity of OSA. RESULTS: Comparison between OSA risk in the three classes showed no statistically significant difference (p = 0.791, effect size = 0.052). CONCLUSIONS: STOP-BANG questionnaire showed that there was no statistically significant difference in the prevalence of OSA between different skeletal classes.

Magnitude of Horizontal Advancement is Associated With Apnea Hypopnea Index Improvement and Counter-Clockwise Maxillary Rotation After Subcranial Distraction for Syndromic Synostosis

PURPOSE

Subcranial midface distraction is used to treat central midface deficiency in syndromic synostosis. Our aim was to determine which maxillary movements were associated with improvement in measures of obstructive sleep apnea.

METHODS

This was a retrospective cohort study that reviewed patients with syndromic midface retrusion and documented sleep apnea who underwent subcranial midface distraction via either Le Fort 3 osteotomy or Le Fort 2 osteotomy with zygomatic repositioning. The predictor variables measured on cephalograms were the magnitude and direction of midface and mandibular movements. The primary outcome was the change in the apnea hypopnea index (AHI) from polysomnography before and after surgery. The secondary outcomes were volumes of upper airway containing bone spaces calculated from computed tomography scans. Data analysis included linear regression to estimate the effect of distraction vectors on bone space volumes and AHI changes.

RESULTS

We included 18 patients primarily with Crouzon or Apert syndrome. The magnitude of distraction in a horizontal direction was the most significant factor for AHI improvement and primarily expanded the nasopharyngeal space, but with a smaller impact on the oral cavity space. Clockwise palate rotation was most influenced by a downward direction of distraction, with 24° below horizontal creating a neutral advancement. The greater the magnitude of advancement, the more likely a counterclockwise rotation was observed.

CONCLUSIONS

Horizontal magnitude of advancement had the greatest impact on AHI improvement. Vertical lengthening and closure of anterior open bite deformities can be done without compromising airway results as long as total advancement is not compromised. Palate rotation is best controlled by a downward distraction vector, but counterclockwise rotation increases with greater advancement.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

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 (OR_range 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 (OR_range 1.26–1.52, p<0.05) associated with prolonged median reaction time. Female sex (OR_range 2.21–6.02, p<0.01), Epworth Sleepiness Scale score (OR_range 1.05–1.07, p<0.01) and older age (OR_range 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.

Parameters considering characteristic properties of desaturations have a significant association with impaired vigilance, highlighting the importance of developing methods beyond the AHI for a more detailed assessment of OSA severityhttp://bit.ly/2veqxD9

Polysomnographic characteristics of severe obstructive sleep apnea vary significantly between hypertensive and normotensive patients of both genders

PURPOSE

Hypertension is a common finding in patients with obstructive sleep apnea (OSA), but it has remained unclear whether or not the amount of disturbed breathing and characteristics of individual respiratory events differ between hypertensive and normotensive patients with severe OSA.

METHODS

Full polysomnographic recordings of 323 men and 89 women with severe OSA were analyzed. Differences in the duration of individual respiratory events, total apnea and hypopnea times, and the percentage of disturbed breathing from total sleep time (AHT%) were compared between normotensive and hypertensive patients separately by genders. Furthermore, differences in the respiratory event characteristics were assessed between three AHT% groups (AHT% ≤ 30%, 30% < AHT% ≤ 45%, and AHT% > 45%).

RESULTS

Hypertensive women had lower percentage apnea time (15.2% vs. 18.2%, p = 0.003) and AHT% (33.5% vs. 36.5%, p = 0.021) when compared with normotensive women. However, these differences were not observed between hypertensive and normotensive men. Percentage hypopnea time was higher in hypertensive men (13.5% vs. 11.2%, p = 0.043) but not in women (15.2% vs. 12.2%, p = 0.130) compared with their normotensive counterparts. The variation in AHI explained 60.5% (ρ = 0.778) and 65.0% (ρ = 0.806) of the variation in AHT% in normotensive and hypertensive patients, respectively. However, when AHT% increased, the capability of AHI to explain the variation in AHT% declined.

CONCLUSIONS

There is a major inter- and intra-gender variation in percentage apnea and hypopnea times between hypertensive and normotensive patients with severe OSA. OSA is an important risk factor for hypertension and thus, early detection and phenotyping of OSA would allow timely treatment of patients with the highest risk of hypertension.

The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study

BACKGROUND

Autonomic arousals can be considered as surrogates of electroencephalography (EEG) arousals when calculating respiratory disturbance index (RDI). The main objective of this proof of concept study was to evaluate the use of heart rate acceleration (HRa) arousals associated with sleep respiratory events in a population undergoing full polysomnography (type 1) and in another undergoing portable monitor study (type 3). Our hypothesis is that when compared to other commonly used indexes, RDI based on HRa will capture more events in both types of recording.

MATERIALS AND METHODS

A retrospective analysis was performed in two different populations of patients with suspected OSA: a) 72 patients undergoing one night of type 1 recording and b) 79 patients undergoing one night of type 3 recording. Variables for type 1 were 4% oxygen desaturation index (ODI), apnea/hypopnea index (AHI), RDI based on EEG arousals (RDIe), and RDI based on HRa with threshold of 5bpm (RDIa5). For type 3, variables were 4% ODI, AHI, and RDIa5 (it is not possible to calculate RDIe due to the absence of EEG). Calculated data were 1) Mean values for each sleep disturbance index in type 1 and 3 recordings; 2) Frequency of migration from lower to higher OSA severity categories using RDIa5 in comparison to AHI (thresholds: ≥5/h mild, ≥15/h moderate, ≥30/h severe); and 3) Bland-Altman plots to assess agreement between AHI vs RDIe and RDIa5 in type 1 population, and AHI vs RDIa5 in type 3 populations.

RESULTS

More respiratory disturbance events were captured with RDIa5 index in both type 1 and type 3 recordings when compared to the other indexes. In type 1 recording, when using RDIa5 37% of patients classified as not having OSA with AHI were now identified as having OSA, and a total of 59% migrated to higher severity categories. In type 3 recording, similar results were obtained, as 37% of patients classified as not having OSA with AHI were now identified as having OSA using RDIa5, and a total of 55% patients migrated to higher severity categories. Mean differences for RDIa5 and AHI in type 1 and 3 populations were similar.

CONCLUSION

The use of autonomic arousals such as HRa can help to detect more respiratory disturbance events when compared to other indexes, being a variable that may help to capture borderline mild cases. This becomes especially relevant in type 3 recordings. Future research is needed to determine its validity, optimization, and its clinical significance.