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.

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages.

OBJECTIVE: The current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages. METHODS: Type II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages. RESULTS: The results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 µv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages. CONCLUSIONS: Technical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

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.

Multicentre sleep-stage scoring agreement in the Sleep Revolution project.

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.

Morbid obesity influences the nocturnal electrocardiogram wave and interval durations among suspected sleep apnea patients.

BACKGROUND: Obesity is a global issue with a major impact on cardiovascular health. This study explores how obesity influences nocturnal cardiac electrophysiology in suspected obstructive sleep apnea (OSA) patients. METHODS: We randomly selected 12 patients from each of the five World Health Organization body mass index (BMI) classifications groups (ntotal = 60) while keeping the group's age and sex matched. We evaluated 1965 nocturnal electrocardiography (ECG) samples (10 s) using modified lead II recorded during normal saturation conditions. R-wave peaks were detected and confirmed using dedicated software, with the exclusion of ventricular extrasystoles and artifacts. The duration of waves and intervals was manually marked. The average electric potential graphs were computed for each segment. Thresholds for abnormal ECG waveforms were P-wave > 120 ms, PQ interval > 200 ms, QRS complex > 120 ms for, and QTc > 440 ms. RESULTS: Obesity was significantly (p < .05) associated with prolonged conduction times. Compared to the normal weight (18.5 ≤ BMI < 25) group, the morbidly obese patients (BMI ≥ 40) had a significantly longer P-wave duration (101.7 vs. 117.2 ms), PQ interval (175.8 vs. 198.0 ms), QRS interval (89.9 vs. 97.7 ms), and QTc interval (402.8 vs. 421.2 ms). We further examined ECG waveform prolongations related to BMI. Compared to other patient groups, the morbidly obese patients had the highest number of ECG segments with PQ interval (44% of the ECG samples), QRS duration (14%), and QTc duration (20%) above the normal limits. CONCLUSIONS: Morbid obesity predisposes patients to prolongation of cardiac conduction times. This might increase the risk of arrhythmias, stroke, and even sudden cardiac death.

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.

Multi-centre arousal scoring agreement in the Sleep Revolution.

We investigated arousal scoring agreement within full-night polysomnography in a multi-centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second-by-second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04-0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second-by-second agreement was fair (Fleiss' kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07-0.68). Fleiss' kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes.

Reaction time in psychomotor vigilance task is related to hypoxic load in males with sleep apnea.

Oxygen saturation (SpO2 )-based parameters are more strongly linked to impaired daytime vigilance than the conventional diagnostic metrics in patients with obstructive sleep apnea (OSA). However, whether the association between SpO2 -based parameters and impaired daytime vigilance is modulated by sex, remains unknown. Hence, we investigated the interplay between sex and detailed SpO2 -based metrics and their association with impaired vigilance in patients with OSA. The study population consisted of 855 (473 males, 382 females) patients with suspected OSA who underwent overnight polysomnography and psychomotor vigilance task (PVT). The population was grouped by sex and divided into quartiles (Q1-Q4) based on median reaction times (RTs) in the PVT. In addition to conventional diagnostic metrics, desaturation severity (DesSev), fall severity (FallSev), and recovery severity (RecovSev) were compared between the sexes and between the best (Q1) and worst (Q4) performing quartiles by using cumulative distribution functions (CDFs). Additionally, sex-specific covariate-adjusted linear regression models were used to investigate the connection between the parameters and RTs. The CDFs showed significantly higher hypoxic load in Q4 in males compared to females. In addition, the DesSev (ß = 8.05, p < 0.01), FallSev (ß = 6.48, p = 0.02), RecovSev (ß = 9.13, p < 0.01), and Oxygen Desaturation Index (ß = 12.29, p < 0.01) were associated with increased RTs only in males. Conversely, the Arousal Index (ß = 10.75-11.04, p < 0.01) was associated with impaired vigilance in females. The severity of intermittent hypoxaemia was strongly associated with longer RTs in males whereas the Arousal Index had the strongest association in females. Thus, the impact of hypoxic load on impaired vigilance seems to be stronger in males than females.

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.

Obstructive Sleep Apnea Patients With Atrial Arrhythmias Suffer From Prolonged Recovery From Desaturations.

OBJECTIVE: We aimed to investigate how acute and long-term effects of atrial arrhythmias affect the desaturation severity and characteristics determined from the oxygen saturation signal in obstructive sleep apnea (OSA) patients. METHODS: 520 suspected OSA patients were included in retrospective analyses. Eight desaturation area and slope parameters were calculated from blood oxygen saturation signals recorded during polysomnographic recordings. Patients were grouped based on whether they had previously diagnosed atrial arrhythmia (i.e., atrial fibrillation (AFib) or atrial flutter) or not. Furthermore, patients with a previous atrial arrhythmia diagnosis were sub-grouped based on whether they had continuous AFib or sinus rhythm during the polysomnographic recordings. Empirical cumulative distribution functions and linear mixed models were utilized to investigate the connection between diagnosed atrial arrhythmia and the desaturation characteristics. RESULTS: Patients with previous atrial arrhythmia diagnosis had greater desaturation recovery area when the 100% oxygen saturation baseline reference was considered (ß = 0.150--0.127, p ≤ 0.039) and more gradual recovery slopes (ß = -0.181 to -0.199, p < 0.004) than patients without a previous atrial arrhythmia diagnosis. Furthermore, patients with AFib had more gradual oxygen saturation fall and recovery slopes than patients with sinus rhythm. CONCLUSION: Desaturation recovery characteristics in the oxygen saturation signal contains essential information about the cardiovascular response to hypoxemic periods. SIGNIFICANCE: More comprehensive consideration of the desaturation recovery section could provide more detailed information about OSA severity, for example when developing new diagnostic parameters.

A Comparison of Signal Combinations for Deep Learning-Based Simultaneous Sleep Staging and Respiratory Event Detection.

OBJECTIVE: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, but many of them do not consider the individual sleep stages or events. In this study, we aimed to develop a deep learning model to simultaneously score both sleep stages and respiratory events. The hypothesis was that the scoring and subsequent AHI calculation could be performed utilizing pulse oximetry data only. METHODS: Polysomnography recordings of 877 individuals with suspected OSA were used to train the deep learning models. The same architecture was trained with three different input signal combinations (model 1: photoplethysmogram (PPG) and oxygen saturation (SpO 2); model 2: PPG, SpO 2, and nasal pressure; model 3: SpO 2, nasal pressure, electroencephalogram (EEG), oronasal thermocouple, and respiratory belts). RESULTS: Model 1 reached comparative performance with models 2 and 3 for estimating the AHI (model 1 intraclass correlation coefficient (ICC) = 0.946; model 2 ICC = 0.931; model 3 ICC = 0.945), and REM-AHI (model 1 ICC = 0.912; model 2 ICC = 0.921; model 3 ICC = 0.883). The automatic sleep staging accuracies (wake/N1/N2/N3/REM) were 69%, 70%, and 79% with models 1, 2, and 3, respectively. CONCLUSION: AHI can be estimated using pulse oximetry-based automatic scoring. Explicit scoring of sleep stages and respiratory events allows visual validation of the automatic analysis, and provides information on OSA phenotypes. SIGNIFICANCE: Automatic scoring of sleep stages and respiratory events with a simple pulse oximetry setup could allow cost-effective, large-scale screening of OSA.

Obstructive sleep apnea-related intermittent hypoxaemia is associated with impaired vigilance.

Obstructive sleep apnea (OSA)-related intermittent hypoxaemia is a potential risk factor for different OSA comorbidities, for example cardiovascular disease. However, conflicting results are found as to whether intermittent hypoxaemia is associated with impaired vigilance. Therefore, we aimed to investigate how desaturation characteristics differ between the non-impaired vigilance and impaired vigilance patient groups formed based on psychomotor vigilance task (PVT) performance and compared with traditional OSA severity parameters. The study population comprised 863 patients with suspected OSA who underwent a PVT test before polysomnography. The conventional OSA parameters, for example, the apnea-hypopnea index, oxygen desaturation index, and arousal index were computed. Furthermore, the median desaturation area, fall area, recovery area, and desaturation depth were computed with the pre-event baseline reference and with reference to the 100% oxygen saturation level. Patients were grouped into best- and worst-performing quartiles based on the number of lapses in PVT (Q1: PVT lapses <5 and Q4: PVT lapses >36). The association between parameters and impaired vigilance was evaluated by cumulative distribution functions (CDFs) and binomial logistic regression. Based on the CDFs, patients in Q4 had larger desaturation areas, recovery areas, and deeper desaturations when these were referenced to 100% saturation compared with Q1. The odds ratio (OR) of the median desaturation area (OR = 1.56), recovery area (OR = 1.71), and depth (OR = 1.65) were significantly elevated in Q4 in regression models. However, conventional OSA parameters were not significantly associated with impaired vigilance (ORs: 0.79-1.09). Considering desaturation parameters with a 100% SpO2 reference in the diagnosis of OSA could provide additional information on the severity of OSA and related daytime vigilance impairment.

Pulse Oximetry: The Working Principle, Signal Formation, and Applications.

Pulse oximeters are routinely used in various medical-grade and consumer-grade applications. They can be used to estimate, for example, blood oxygen saturation, autonomic nervous system activity and cardiac function, blood pressure, sleep quality, and recovery through the recording of photoplethysmography signal. Medical-grade devices often record red and infra-red light-based photoplethysmography signals while smartwatches and other consumer-grade devices usually rely on a green light. At its simplest, a pulse oximeter can consist of one or two photodiodes and a photodetector attached, for example, a fingertip or earlobe. These sensors are used to record light absorption in a medium as a function of time. This time-varying absorption information is used to form a photoplethysmography signal. In this chapter, we discuss the working principles of pulse oximeters and the formation of the photoplethysmography signal. We will further discuss the advantages and disadvantages of pulse oximeters, which kind of applications exist in the medical field, and how pulse oximeters are utilized in daily health monitoring.

Desaturation event scoring criteria affect the perceived severity of nocturnal hypoxic load.

OBJECTIVES/BACKGROUND: Interest in using blood oxygen desaturations in the diagnostics of sleep apnea has risen in recent years. However, no standardized criteria for desaturation scoring exist which complicates the drawing of solid conclusions from literature. PATIENTS/METHODS: We investigated how different desaturation scoring criteria affect the severity of nocturnal hypoxic load and the prediction of impaired daytime vigilance in 845 patients. Desaturations were scored based on three features: 1) minimum oxygen saturation drop during the event (2-20%, 1% interval), 2) minimum duration of the event (2-20s, 1s interval), and 3) maximum plateau duration within the event (5-60s, 5s interval), resulting in 4332 different scoring criteria. The hypoxic load was described with oxygen desaturation index (ODI), desaturation severity (DesSev), and desaturation duration (DesDur) parameters. Association between hypoxic load and impaired vigilance was investigated with covariate-adjusted area under curve (AUC) analyses by dividing patients into normal (≤5 lapses) and impaired (≥36 lapses) vigilance groups based on psychomotor vigilance task performance. RESULTS: The severity of hypoxic load varied greatly between different scoring criteria. For example, median ODI ranged between 0.4 and 12.9 events/h, DesSev 0.01-0.23 %-point, and DesDur 0.3-9.6 %-point when the minimum transient drop criterion of 3% was used and other two features were altered. Overall, the minimum transient drop criterion had the largest effect on parameter values. All models with differently determined parameters predicted impaired vigilance moderately (AUC = 0.722-0.734). CONCLUSIONS: Desaturation scoring criteria greatly affected the severity of hypoxic load. However, the difference in the prediction of impaired vigilance between different criteria was rather small.

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.

STAR sleep recording export software for automatic export and anonymization of sleep studies.

Sleep research often relies on large retrospective clinical datasets. However, as the data is usually stored in proprietary formats specific for each sleep software, the raw data cannot be easily accessed and analyzed with external tools. While the raw data can usually be exported to more common data formats, this is often a cumbersome and labor-intensive task as it is not required for clinical purposes. Additionally, the recordings often include sensitive patient information which must be removed before the data can be shared or analyzed externally. This anonymization can be difficult to perform manually without the correct tools or knowledge of the file types and how they internally store the data. The STAR sleep recording export software provides a simple tool that can be used to perform the sleep study exports automatically. This allows the user to easily export a batch of sleep studies with minimal effort. In addition, the software can also be used to automatically anonymize the exported sleep recordings allowing researchers to save time and personnel resources as these do not need to be allocated for exporting and anonymizing sleep studies. The software supports Noxturnal, RemLogic, Profusion PSG and Sleepware G3 and it is free and openly available for anyone to download and use.

QTc prolongation is associated with severe desaturations in stroke patients with sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is associated with vascular diseases from which stroke and sudden cardiac death are the most significant ones. It is known that disturbances of the autonomic nervous system and electrocardiographic changes are seen in patients with a previous cerebrovascular event. However, the pathophysiological cascade between breathing cessations, autonomic regulation, and cardiovascular events is not fully understood. METHODS: We aimed to investigate the acute effect of desaturation on repolarisation in OSA patients with a previous stroke. We retrospectively analysed heart-rate corrected QT (QTc) intervals before, within, and after 975 desaturations in OSA patients with a stroke history and at least moderate sleep apnea (apnea-hypopnea index ≥ 15 events/h, n = 18). For the control population (n = 18), QTc intervals related to 1070 desaturation were analysed. Desaturations were assigned to groups according to their length and duration. Groupwise comparisons and regression analyses were further executed to investigate the influence of desaturation features on repolarization. RESULTS: In the stroke population the QTc prolonged at least 11 ms during 27.1% of desaturations, and over 20 ms during 12.2% of desaturations. QTc was significantly prolonged during longer (> 30 s, p < 0.04) and deeper (> 7%, p < 0.03) desaturations. Less severe desaturations didn't influence QTc. In median, QTc prolonged 7.5 ms during > 45 s desaturations and 7.4 ms during > 9% deep desaturations. In the control population, QTc prolongation was observed but to a significantly lesser extent than in stroke patients. In addition, desaturation duration was found to be an independent predictor of QTc prolongation (ß = 0.08, p < 0.001) among all study patients. CONCLUSIONS: We demonstrated that longer (> 30 s) and deeper (> 7%) desaturations prolong QTc in patients with stroke history. A significant proportion of desaturations produced clinically relevant QTc prolongation. As it is known that a long QTc interval is associated with lethal arrhythmias, this finding might in part explain the pathophysiological sequelae of cardiovascular mortality in OSA patients with a history of stroke.

Novel oxygen desaturation parameters are associated with cardiac troponin I: Data from the Akershus Sleep Apnea Project.

Novel diagnostic markers for obstructive sleep apnea beyond the apnea-hypopnea index (AHI) have been introduced. There are no studies on their association with markers of subclinical myocardial injury. We assessed the association between novel desaturation parameters and elevated cardiac troponin I and T. Participants with polysomnography (498) from the Akershus Sleep Apnea study were divided into normal and elevated biomarker groups based on sex-specific concentration thresholds (cardiac troponin I: ≥4 ng/L for women, ≥6 ng/L for men; and cardiac troponin T: ≥7 ng/L for women, ≥8 ng/L for men). Severity of obstructive sleep apnea was evaluated with the AHI, oxygen desaturation index, total sleep time with oxygen saturation below 90% (T90), lowest oxygen saturation (Min SpO2 %), and novel oxygen desaturation parameters: desaturation duration and desaturation severity. How the AHI and novel desaturation parameters predicted elevated cardiac troponin I and cardiac troponin T levels was assessed by the area under the curve (AUC). Based on multivariable-adjusted linear regression, the AHI (ß = 0.004, p = 0.012), desaturation duration (ß = 0.007, p = 0.004), and desaturation severity (ß = 0.147, p = 0.002) were associated with cardiac troponin I levels but not cardiac troponin T. T90 was associated with cardiac troponin I (ß = 0.006, p = 0.009) and cardiac troponin T (ß = 0.005, p = 0.007). The AUC for the AHI 0.592 (standard error 0.043) was not significantly different from the AUC of T90 (SD 0.640, p = 0.08), desaturation duration 0.609 (SD 0.044, p = 0.42) or desaturation severity 0.616 (SD 0.043, p = 0.26) in predicting myocardial injury as assessed by cardiac troponin I. Oxygen desaturation parameters and the AHI were associated with cardiac troponin I levels but not cardiac troponin T levels. Novel oxygen desaturation parameters did not improve the prediction of subclinical myocardial injury compared to the AHI.

Diabetes and cardiovascular diseases are associated with the worsening of intermittent hypoxaemia.

Intermittent hypoxaemia is a risk factor for numerous diseases. However, the reverse pathway remains unclear. Therefore, we investigated whether pre-existing hypertension, diabetes or cardiovascular diseases are associated with the worsening of intermittent hypoxaemia. Among the included 2,535 Sleep Heart Health Study participants, hypertension (n = 1,164), diabetes (n = 170) and cardiovascular diseases (n = 265) were frequently present at baseline. All participants had undergone two polysomnographic recordings approximately 5.2 years apart. Covariate-adjusted linear regression analyses were utilized to investigate the difference in the severity of intermittent hypoxaemia at baseline between each comorbidity group and the group of participants free from all comorbidities (n = 1,264). Similarly, we investigated whether the pre-existing comorbidities are associated with the progression of intermittent hypoxaemia. Significantly higher oxygen desaturation index (ß = 1.77 [95% confidence interval: 0.41-3.13], p = 0.011), desaturation severity (ß = 0.07 [95% confidence interval: 0.00-0.14], p = 0.048) and desaturation duration (ß = 1.50 [95% confidence interval: 0.31-2.69], p = 0.013) were observed in participants with pre-existing cardiovascular diseases at baseline. Furthermore, the increase in oxygen desaturation index (ß = 3.59 [95% confidence interval: 1.78-5.39], p < 0.001), desaturation severity (ß = 0.08 [95% confidence interval: 0.02-0.14], p = 0.015) and desaturation duration (ß = 2.60 [95% confidence interval: 1.22-3.98], p < 0.001) during the follow-up were higher among participants with diabetes. Similarly, the increase in oxygen desaturation index (ß = 2.73 [95% confidence interval: 1.15-4.32], p = 0.001) and desaturation duration (ß = 1.85 [95% confidence interval: 0.62-3.08], p = 0.003) were higher among participants with cardiovascular diseases. These results suggest that patients with pre-existing diabetes or cardiovascular diseases are at increased risk for an expedited worsening of intermittent hypoxaemia. As intermittent hypoxaemia is an essential feature of sleep apnea, these patients could benefit from the screening and follow-up monitoring of sleep apnea.