scholarly journals Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study

10.2196/17252 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e17252
Author(s):  
Sina Akbarian ◽  
Nasim Montazeri Ghahjaverestan ◽  
Azadeh Yadollahi ◽  
Babak Taati
Keyword(s):  
Sleep Apnea ◽  
Optical Flow ◽  
Central Sleep Apnea ◽  
Video Camera ◽  
Video Data ◽  
Apnea Hypopnea Index ◽  
Respiratory Disorder ◽  
Obstructive Sleep ◽  
Infrared Video ◽  
Supervised Classifiers

Background Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). Objective The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. Methods Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. Results Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an F1 score of 89% in differentiating OA vs CA. Conclusions In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition.

scholarly journals Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study (Preprint)

2019 ◽  
Author(s):  
Sina Akbarian ◽  
Nasim Montazeri Ghahjaverestan ◽  
Azadeh Yadollahi ◽  
Babak Taati
Keyword(s):  
Sleep Apnea ◽  
Optical Flow ◽  
Central Sleep Apnea ◽  
Video Camera ◽  
Video Data ◽  
Apnea Hypopnea Index ◽  
Respiratory Disorder ◽  
Obstructive Sleep ◽  
Infrared Video ◽  
Supervised Classifiers

BACKGROUND Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). OBJECTIVE The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. METHODS Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. RESULTS Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m<sup>2</sup>; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an <i>F</i><sub>1</sub> score of 89% in differentiating OA vs CA. CONCLUSIONS In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition.

scholarly journals Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

10.2196/26524 ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. e26524
Author(s):  
Sina Akbarian ◽  
Nasim Montazeri Ghahjaverestan ◽  
Azadeh Yadollahi ◽  
Babak Taati
Keyword(s):  
Sleep Apnea ◽  
Gold Standard ◽  
Learning Algorithm ◽  
Video Data ◽  
Apnea Hypopnea Index ◽  
Respiratory Disorder ◽  
Sleeping Position ◽  
Respiratory Events ◽  
Infrared Video ◽  
3D Cnn

Background Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

scholarly journals The Profile of Romanian Patient with Central Sleep Apnea and Heart Failure

2021 ◽  
Vol 18 (5) ◽  
pp. 39-52
Author(s):  
Corina-Ioana Borcea ◽  
Oana Claudia Deleanu ◽  
Florin-Dumitru Mihălţan
Keyword(s):  
Heart Failure ◽  
Risk Factor ◽  
Sleep Apnea ◽  
Central Sleep Apnea ◽  
Apnea Hypopnea Index ◽  
High Morbidity ◽  
Respiratory Disorder ◽  
Patient Profile ◽  
Obstructive Sleep

Abstract Sleep-related breathing disorders are highly prevalent in patients with established cardiovascular disease, especially Heart failure (HF). Central sleep apnea (CSAS) share several pathophysiological features with obstructive sleep apnea, but each with a unique pathology and specific treatment. There are considerably fewer published patient profile reports in association with CSAS-HF. The treatment for association CSAS-HF varies and depends on the etiology of respiratory disorder and leaves considerable room for improvement for future investigations. Despite progress over the last 3 decades, HF continues to have high morbidity and mortality rates. At this time, it is also uncertain whether CSAS is a consequence of HF with reduced ejection fraction or it is in fact a risk factor for the evolution of underlying cardiac pathology. Therefore, this retrospective study highlights the interaction between CSA and HF, with particular attention to age differences, a frequent reported risk factor, in a Romanian large cohort. Were included adults > 18 years old, with sleep apnea syndrome (apnea-hypopnea index-AHI>5 per hour of sleep with at least 50% of central on polygraphy-PG and after on polysomnographic-PSGsleep study) in the presence of sleep symptoms, with known HF with preserved LVEF (>40%) in a previous internal/cardiology department. Were excluded those < 18years old, with other sleep apneas (obstructive, mixed or complex), other sleep disorders (by PSG), inadequate PSG records,or patient refusal. Anthropometric data, clinical findings, vital parameters, comorbidities, treatments and investigations (see below) were analyzed in the population and also in subgroups. The majority of this study population (12 patients) were male, older, with normal weight, symptomatic and comorbidities. As many as 90% of the patients presented severe CSAS and 77.8% associated Cheyne–Stokes respiration (CSR). The comparative analysis of the subgroups in which according to the literature the associated pathologies manifest differently showed that there have not been noted major differences or statistically significant correlation between these two groups and cardiac outcomes.Still, in the group over 65 years we found that people were more predisposed to suffer from high BP, judging by the elevated level of the systolic blood pressure value, and another relevant comorbidities were atrial fibrillation, had more apneas and hypopneas during sleep and severe CSA were the most common. Following titration, AHI, central apnea index, desaturation index decreased with clinically significant. This study appeals to the importance of sleep health, an even more important aspect for Romania, where this associations underreported and even unrecognized, and thus the general condition of patients can worsen. Further research, based on other criteria of difference, is needed as the evidence is still lacking regarding the long-term consequences of CSA and long-term impact of current strategies in HF population.

scholarly journals Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation (Preprint)

2020 ◽  
Author(s):  
Sina Akbarian ◽  
Nasim Montazeri Ghahjaverestan ◽  
Azadeh Yadollahi ◽  
Babak Taati
Keyword(s):  
Sleep Apnea ◽  
Gold Standard ◽  
Learning Algorithm ◽  
Video Data ◽  
Apnea Hypopnea Index ◽  
Respiratory Disorder ◽  
Sleeping Position ◽  
Respiratory Events ◽  
Infrared Video ◽  
3D Cnn

BACKGROUND Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. OBJECTIVE The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. METHODS A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. RESULTS The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, <i>P</i>&lt;.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. CONCLUSIONS This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

393 Comparison of Oxygenation Abnormalities Between Obstructive Sleep Apnea and Central Sleep Apnea

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A156-A157
Author(s):  
Sikawat Thanaviratananich ◽  
Hao Cheng ◽  
Maria Pino ◽  
Krishna Sundar
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Demographic Data ◽  
Central Sleep Apnea ◽  
Nrem Sleep ◽  
Rate Of Change ◽  
Apnea Hypopnea Index ◽  
P Value ◽  
Obstructive Sleep ◽  
Cheyne Stokes Breathing

Abstract Introduction The apnea-hypopnea index (AHI) is used as a generic index to quantify both central sleep apnea (CSA) and obstructive sleep apnea (OSA) syndromes. Patterns of oxygenation abnormalities seen in CSA and OSA may be key to understanding differing clinical impacts of these disorders. Oxygen desaturation and resaturation slopes and durations in OSA and CSA were compared between OSA and CSA patients. Methods Polysomnographic data of patients aged 18 years or older with diagnosis of OSA and CSA, at University of Iowa Hospitals and Clinics, were analyzed and demographic data were collected. Oximetric changes during hypopneas and apneas were studied for desaturation/resaturation durations and desaturation/resaturation slopes. Desaturation and resaturation slopes were calculated as rate of change in oxygen saturation (ΔSpO2/Δtime). Comparison of hypoxemia-based parameters between patients with OSA and CSA was performed using unpaired t-test. Results 32 patients with OSA with median AHI of 15.4 (IQR 5.1 to 30.55) and median ODI of 15.47 (IQR 9.50 to 29.33) were compared to 15 patients with CSA with a median AHI of 20.4 (IQR 12.6 to 47.8) and median ODI of 27.56 (IQR 17.99 to 29.57). The mean number of desaturation and resaturation events was not significantly different between patients with OSA and CSA (OSA - 106.81±87.93; CSA - 130.67±76.88 with a p-value 0.1472). 4/15 CSA patients had Cheyne-Stokes breathing, 2/15 had treatment emergent central sleep apnea, 1/15 had methadone-associated CSA and for 8/15, no etiologies for CSA were found. Mean desaturation durations was significantly longer in OSA (20.84 s ± 5.67) compared to CSA (15.94 s ± 4.54) (p=0.0053) and consequently the desaturation slopes were steeper in CSA than OSA (-0.35%/sec ±0.180 vs. -0.243 ± 0.073; p=0.0064). The resaturation duration was not significantly longer in OSA (9.76 s ± 2.02) than CSA (9.057 s ± 2.17) (p=0.2857). Differences between desaturation duration and slopes between CSA and OSA persisted during REM and NREM sleep, and in supine sleep. Conclusion As compared to OSA, patients with CSA have different patterns of desaturations and resaturations with lesser hypoxic burden with CSA. This may have implications on the clinical outcomes seen between these two disorders. Support (if any):

scholarly journals What is the remaining status of adaptive servo-ventilation? The results of a real-life multicenter study (OTRLASV-study)

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Dany Jaffuel ◽  
Carole Philippe ◽  
Claudio Rabec ◽  
Jean-Pierre Mallet ◽  
Marjolaine Georges ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Real Life ◽  
Group Versus ◽  
Central Sleep Apnea ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Apnea Hypopnea Index ◽  
Ventricular Ejection Fraction ◽  
Life Conditions ◽  
Obstructive Sleep

Abstract Backgrounds As a consequence of the increased mortality observed in the SERVE-HF study, many questions concerning the safety and rational use of ASV in other indications emerged. The aim of this study was to describe the clinical characteristics of ASV-treated patients in real-life conditions. Methods The OTRLASV-study is a prospective, 5-centre study including patients who underwent ASV-treatment for at least 1 year. Patients were consecutively included in the study during the annual visit imposed for ASV-reimbursement renewal. Results 177/214 patients were analysed (87.57% male) with a median (IQ25–75) age of 71 (65–77) years, an ASV-treatment duration of 2.88 (1.76–4.96) years, an ASV-usage of 6.52 (5.13–7.65) hours/day, and 54.8% were previously treated via continuous positive airway pressure (CPAP). The median Epworth Scale Score decreased from 10 (6–13.5) to 6 (3–9) (p < 0.001) with ASV-therapy, the apnea-hypopnea-index decreased from 50 (38–62)/h to a residual device index of 1.9 (0.7–3.8)/h (p < 0.001). The majority of patients were classified in a Central-Sleep-Apnea group (CSA; 59.3%), whereas the remaining are divided into an Obstructive-Sleep-Apnea group (OSA; 20.3%) and a Treatment-Emergent-Central-Sleep-Apnea group (TECSA; 20.3%). The Left Ventricular Ejection Fraction (LVEF) was > 45% in 92.7% of patients. Associated comorbidities/etiologies were cardiac in nature for 75.7% of patients (neurological for 12.4%, renal for 4.5%, opioid-treatment for 3.4%). 9.6% had idiopathic central-sleep-apnea. 6.2% of the patients were hospitalized the year preceding the study for cardiological reasons. In the 6 months preceding inclusion, night monitoring (i.e. polygraphy or oximetry during ASV usage) was performed in 34.4% of patients, 25.9% of whom required a subsequent setting change. According to multivariable, logistic regression, the variables that were independently associated with poor adherence (ASV-usage ≤4 h in duration) were TECSA group versus CSA group (p = 0.010), a higher Epworth score (p = 0.019) and lack of a night monitoring in the last 6 months (p < 0.05). Conclusions In real-life conditions, ASV-treatment is often associated with high cardiac comorbidities and high compliance. Future research should assess how regular night monitoring may optimize devices settings and patient management. Trial registration The OTRLASV study is registered on ClinicalTrials.gov (Identifier: NCT02429986) on 1 April 2015.

scholarly journals Prevalence of Sleep-Related Disordered Breathing among Acromegaly Patients and its Correlation to Cephalometric Parameters: An Indian Perspective

2020 ◽  
Vol 56 (01) ◽  
pp. 09-14
Author(s):  
Sreejith M. ◽  
Mohd Ashraf Ganie ◽  
Ravinder Goswami ◽  
Nikhil Tandon ◽  
Randeep Guleria ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Soft Tissues ◽  
Central Sleep Apnea ◽  
Human Growth ◽  
Apnea Hypopnea Index ◽  
Sleep Related Breathing Disorders ◽  
Obstructive Sleep ◽  
Long Term Follow Up ◽  
Treatment Naïve ◽  
Magnetic Resonance Imaging Mri

Abstract Introduction Sleep-related breathing disorders (SRBDs) including obstructive sleep apnea (OSA) and central sleep apnea (CSA) are quite common and are the leading causes of mortality in acromegaly. OSA in acromegaly is generally attributed to changes in oropharyngeal soft tissues. Data on OSA in Indian acromegaly are scant, especially cephalometric findings. The aim of this study is to evaluate the burden of SRBDs in acromegaly and its correlation to cephalometric parameters. Materials and Methods A total of 32 subjects (20 men and 12 women), diagnosed with acromegaly on the basis of standard clinical, biochemical, and hormonal measurements were recruited. In addition to the above parameters, polysomnography and magnetic resonance imaging (MRI) of the pharynx were performed in all subjects. Results The mean age of the subjects was 42.66 ± 11.13 years (range = 26–66) and mean duration of study after first presentation was 7.6 ± 6.3 years (range = 0.25–32). A total of 28 of 32 (93.3%) subjects had sellar MRI documented macroadenomas while 20 (62.5%) patients were treatment naive at the time of assessment. Twenty-nine (90.6%) patients had evidence of SRBD and all of them had OSA subtype. The Apnea–Hypopnea Index (AHI) indicating severity of OSA (mild 21.8%, moderate 34.4%, and severe 34.4%) correlated positively with tongue length, uvula length, and uvula thickness on MRI. However, AHI had no correlation with the severity of GH excess or disease activity or individual parameters such as weight, body mass index, blood pressure, hemoglobin A1c, serum human growth hormone, and insulin-like growth factor-1 level. Conclusion SRBD, the generally overlooked comorbidity, is highly prevalent in subjects with acromegaly and is almost always due to OSA, the severity of which correlates positively with tongue and uvula size. Well-designed, long-term follow-up study on a large cohort of acromegalic patients is required to improve our understanding on the subject.

scholarly journals 1124 Central Sleep Apnea and Traumatic Brain Injury: A NIDILRR and VA TBI Model System Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A428-A428
Author(s):  
G Gulati ◽  
D J Schwartz ◽  
S Nallu ◽  
K Bell ◽  
L Wittine ◽  
...  
Keyword(s):  
Brain Injury ◽  
Sleep Apnea ◽  
Comparative Effectiveness ◽  
Central Sleep Apnea ◽  
Inpatient Rehabilitation ◽  
Apnea Hypopnea Index ◽  
Effectiveness Trial ◽  
Sleep Related Breathing Disorders ◽  
Obstructive Sleep ◽  
Medicine Physician

Abstract Introduction Sleep-related breathing disorders are common after TBI. To date, two single site studies have reported divergent findings in post-TBI patients with one reporting predominantly obstructive sleep apnea (OSA, Holcomb et al., 2016) and the other central sleep apnea (CSA, Webster and Bell, 1998). The purpose of this analysis is to explore prevalence, demographics, and injury characteristics of patients with a clinical diagnosis of CSA in a recently-completed multicenter comparative-effectiveness trial during inpatient rehabilitation following moderate to severe TBI. Methods Participants in a six-center diagnostic comparative effectiveness trial underwent Level-1 polysomnography (PSG) during inpatient rehabilitation for TBI. Studies were scored at a centralized scoring center by one of two certified PSG technicians with final interpretation by a board-certified sleep medicine physician. Results 21 of 248 (8.5%) participants evidenced elevated CSA indices &gt;5. Predominant CSA was rare (n=3 [1.2%], age range: 36-59; 100% male; 33-52 days post-TBI). One participant was on opioid, anti-depressant and antiepileptic drugs, one was on an antiepileptic, and another was on an opioid. PAP therapy was not initiated during PSG thus there was no treatment-emergent CSA. All had a central apnea-hypopnea index (AHI) in the moderate to severe range (29-49). Two out of the three had a GCS &lt;8 and one participant had a GCS of 14. Conclusion In this multi-center clinical trial, predominant CSA was rare. The common practice of reducing polypharmacy in order to minimize sedation and optimize mental status in specialized inpatient brain-injury rehabilitation programs may contribute to the low CSA incidence in this cohort. Attention to medication side-effects and their influence on sleep-related breathing should be routinely considered. Support PCORI (CER-1511-33005), GDHS (W91YTZ-13-C-0015) for DVBIC, NIDILRR (90DPTB0008-03-00; 90DPTB0013-01-00).

Abstract 17143: Obstructive and Central Sleep Apnea and Risk of Incident Atrial Fibrillation

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Patricia Tung ◽  
Yamini S Levitzky ◽  
Rui Wang ◽  
Stuart F Quan ◽  
Daniel J Gottlieb ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Sleep Apnea ◽  
Central Sleep Apnea ◽  
Health Study ◽  
Apnea Hypopnea Index ◽  
Community Based ◽  
Obstructive Sleep ◽  
History Of

INTRODUCTION: Prior studies have documented a higher prevalence of atrial fibrillation (AF) in those with obstructive sleep apnea (OSA). OSA has been associated with AF recurrence following cardioversion and ablation, and with prevalent and incident AF in cross-sectional and retrospective studies. Central sleep apnea (CSA) also has been associated with AF in patients with heart failure. However, data from prospective cohorts are sparse and few studies have evaluated the association of CSA with AF in population studies. METHODS: We assessed the association of OSA and CSA with incident AF among 3,420 subjects without a history of AF in the Sleep Heart Health Study (SHHS), a prospective, community-based study designed to evaluate the cardiovascular consequences of sleep disordered breathing. Subjects underwent overnight polysomnography at baseline and were followed over time for the development of incident AF, documented at any time after baseline polysomnogram until the end of follow-up. OSA was defined as an obstructive apnea-hypopnea index ≥ 5 and CSA was defined as a central apnea index ≥ 5. RESULTS: At baseline, the sample include 1499 men (44.4%) with a mean age of 62.4 (±10.9); 1569 (45.9%) subjects met criteria for mild to severe OSA and 54 (1.6%) for CSA. Over a mean follow-up of 8.2 years, 382 cases of incident AF were identified. The prevalence of both OSA and CSA was higher among those who developed AF compared to those who did not (OSA 49% vs 44%, p=0.001 and CSA 5% vs 1.2%, p=0.001). After adjustment for multiple AF risk factors, CSA was associated with an approximately 2-fold increased odds of incident AF (RR=2.38, 95% CI, 1.15-4.94; p = 0.02). The association persisted after exclusion of 258 subjects with a history of heart failure (RR=2.78, 95% CI, 1.28-6.04; p = 0.01). We did not find a significant association of OSA with incident AF (Table). CONCLUSION: In our prospective, community-based cohort baseline CSA was associated with incident AF.

Abstract 2360: Central Sleep Apnea with Nocturnal Hypoxia Promotes Increased Circulating Erythropoietin in Heart Failure

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Andrew D Calvin ◽  
Virend K Somers ◽  
Jennifer M Miller ◽  
David P Steensma ◽  
Lyle J Olson
Keyword(s):  
Heart Failure ◽  
Sleep Apnea ◽  
Nyha Class ◽  
Arterial Oxygen Saturation ◽  
Central Sleep Apnea ◽  
Arterial Oxygen ◽  
Apnea Hypopnea Index ◽  
Simple Linear Regression ◽  
Class Iii ◽  
Obstructive Sleep

Central sleep apnea (CSA) with nocturnal hypoxia is frequent in heart failure (HF). Hypoxia causes increased circulating erythropoietin (EPO) in healthy normals. EPO promotes increased vasoconstriction and exogenous EPO administration is associated with adverse cardiovascular events. No prior studies have related EPO concentration to apnea or hypoxia due to CSA. EPO is elevated in HF patients with nocturnal hypoxia due to CSA. Ambulatory, non-anemic HF patients (n = 29) with LVEF < 45% and healthy controls (n = 18) underwent polysomnography (PSG). Subjects with obstructive sleep apnea (OSA) were excluded. CSA was defined as apnea-hypopnea index (AHI) ≥ 15. Hypoxia was quantified as the proportion of sleep with arterial oxygen saturation < 90% (T90%). Blood for EPO was drawn post-PSG. Other clinical characteristics were summarized from the medical record. HF subjects and controls were similar age (54 vs 60 y, p = 0.09). CSA was present in 14 HF subjects; 13 were men compared to 8 of 15 without CSA (p = 0.04). HF subjects had 42% higher mean EPO than controls (p < 0.01) despite similar hemoglobin (13.9 vs 14.0 g/dL, p = 0.8). NYHA class III–IV HF subjects had 42% higher mean EPO than class I–II HF subjects (p = 0.05, figure ). EPO concentration was correlated with severity of nocturnal hypoxia by simple linear regression (r = 0.4; p = 0.02). By multivariate analysis, elevated EPO was associated with NYHA class III–IV HF and elevated AHI (p = 0.01 and 0.03, respectively; r = 0.6) after adjusting for age, gender, LVEF, renal function and hemoglobin. Nocturnal hypoxia due to CSA promotes increased endogenous EPO concentration in HF patients.

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