scholarly journals Prevalence and Factors Associated With Atrial Fibrillation in Elderly Patients With Obstructive Sleep Apnea

2021 ◽  
Author(s):  
Huanhuan Wang ◽  
JianHua Li ◽  
Yinghui Gao ◽  
Kaibing Chen ◽  
Yan Gao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Regression Analysis ◽  
Elderly Patients ◽  
Cardiac Dysfunction ◽  
Factors Associated ◽  
Chronic Heart Disease ◽  
Obstructive Sleep ◽  
History Of

Abstract Purpose: This study sought to identify the prevalence and factors associated with atrial fibrillation (AF) in elderly patients with obstructive sleep apnea (OSA) in China. Methods: Between January 2015 and October 2017, we recruited 1285 elderly patients with OSA who underwent overnight polysomnography at sleep centers of multiple hospitals. They were assessed using 12-lead ECG or 24-hour dynamic ECG, and their baseline demographics, clinical characteristics, sleep parameters, and medical history were determined. Binary logistic regression analysis was used to investigate the factors related to AF in these elderly patients. Results: The clinician classified 122 (9.5%) patients as having AF. The prevalence of AF significantly increased with age (p＜0.05) but did not significantly differ between the mild, moderate, and severe OSA groups. Additionally, the prevalence of paroxysmal AF was 7.2% among the overall study population, and it increased with OSA severity or advanced age (p＜0.05). Persistent AF was noted in 2.3% participants, and the prevalence also increased with age. The logistic regression analysis showed that age (OR=1.054, 95%CI: 1.027-1.018, p＜0.001) , history of drinking (OR=1.752, 95%CI: 1.070-2.867, p＜0.05), chronic heart disease (OR=1.778, 95%CI: 1.156-2.736, p＜0.01), diabetes mellitus (OR=1.792, 95%CI: 1.183-2.713, p＜0.01), and cardiac dysfunction (OR=2.373, 95%CI=1.298-4.337, p＜0.01) were relevant to AF among participants with OSA.Conclusion: The prevalence of AF is significantly common in elderly patients with OSA. Age, history of drinking, chronic heart disease, diabetes mellitus, and cardiac dysfunction are independently related to AF in these patients.

scholarly journals Application of win ratio methodology in the Global SYMPLICITY Registry for patients with atrial fibrillation or obstructive sleep apnea

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
F Mahfoud ◽  
G Mancia ◽  
C Ukena ◽  
R Schmieder ◽  
K Narkiewicz ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Similar Efficacy ◽  
Ratio Analysis ◽  
Private Company ◽  
Funding Sources ◽  
Obstructive Sleep ◽  
History Of ◽  
Added Benefit

Abstract Background/Introduction The win ratio is a new methodology which utilizes multiple hierarchical endpoints to evaluate clinical outcomes in trials. The win ratio may have added benefit in device therapy trials like renal denervation (RDN) where anti-hypertensive medication burden can influence blood pressure (BP) changes. Purpose In this analysis, we applied the win ratio to patients in the Global SYMPLICITY Registry (GSR) to quantify potential differences in RDN efficacy according to different comorbidities, specifically atrial fibrillation and obstructive sleep apnea. Methods All patients in GSR had an RDN procedure with the Symplicity Flex or Symplicity Spyral catheter. For the win ratio analysis, ambulatory systolic BP (ASBP) measurements, office systolic BP (OSBP) measurements and the number of prescribed anti-hypertensive medications at 6 months were included as hierarchical endpoints. Patients were divided into 1 of 2 groups: with or without atrial fibrillation (AF) at baseline. Each patient was compared with every other patient in the opposing group first according to ASBP to determine “win”, “lose” or “tie” with a threshold of 5 mmHg. Then, ties from the ASBP comparison underwent the comparison using OSBP with a threshold of 10 mmHg. Any tie for a pair comparing OSBP resulted in comparison of number of anti-hypertensive medications with a threshold of 1. Comparisons of ASBP and OSBP were adjusted for baseline SBPs by using residuals from a linear regression. The analysis was repeated for patients grouped according to history of obstructive sleep apnea (OSA) at baseline. Results In March 2020, 336 patients with AF at baseline and 2,394 patients with no AF were compared in GSR, resulting in 336 x 2394 = 804,384 pairwise comparisons for the win ratio analysis. A total of 285,709 “wins”, indicating greater ASBP reduction, OSBP reduction, and/or fewer number of anti-hypertensive medications occurred in the AF group compared to the no AF group. Conversely, 256,511 “losses”, meaning greater BP reduction and/or number of medications occurred in the no AF group. The win ratio was thus calculated as 1.11 (95% CI: 0.98, 1.28, p=0.081) indicating similar BP reduction and medication burden after RDN in patients with or without AF in GSR (Figure). Using these methods, the win ratio for patients with and without OSA was calculated to be 0.98 (95% CI: 0.85, 1.13, p=0.81), also indicating similar RDN efficacy regardless of presence of OSA at baseline (Figure). Previously published results of the win ratio analysis of RDN and sham control patients in the SPRYAL HTN-ON MED trial reported a win ratio in favor of RDN of 2.78 (95% CI: 1.58, 5.48, p<0.001). Conclusions Application of the win ratio methodology to patients in GSR demonstrated similar efficacy of RDN to patients regardless of whether they had comorbidities of atrial fibrillation or obstructive sleep apnea. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): Medtronic

Obstructive Sleep Apnea and Cardiovascular Events Clinical correlations

2018 ◽  
Vol 69 (1) ◽  
pp. 248-250
Author(s):  
Delia Lidia Salaru ◽  
Carmen Elena Plesoianu ◽  
Adina Olaru ◽  
Catalina Arsenescu Georgescu
Keyword(s):  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Cardiovascular Risk ◽  
Sleep Apnea ◽  
Clinical Laboratory ◽  
Predisposing Factors ◽  
Therapeutic Approaches ◽  
Cerebrovascular Events ◽  
Obstructive Sleep ◽  
History Of

Obstructive sleep apnea (OSA) has been described as an independent predictor of mortality and cardiovascular morbidity, and several studies link OAS and atrial fibrillation, although further investigations are needed to fully understand the common physiological mechanisms. The aim of the study was to identify the cardiovascular risk and events of a population diagnosed with OAS and to discover the predisposing factors of the appearance of AF in these patients. Demographic, clinical, laboratory and echocardiographic data were taken from 101 patients previously diagnosed with OSA and admitted to our cardiovascular unit. In a population with cardiovascular risk factors and cerebrovascular events, the prevalence of atrial fibrillation was 63.7%, whereas ventricular arrythmias occurred in 31.4%. The only prediction factor for AF in OSA population was the history of myocardial infarction; other predisposing factors take account for a small number of cases. The presence of a significant association between OSA and markers of cardiovascular disease would warrant the development of a strategy to consider more aggressive therapeutic approaches.

Incidence and mortality of myocardial infarction among Catalonian older adults with and without underlying risk conditions: The CAPAMIS study

2018 ◽  
Vol 25 (17) ◽  
pp. 1822-1830 ◽  
Author(s):  
M José Forcadell ◽  
Angel Vila-Córcoles ◽  
Cinta de Diego ◽  
Olga Ochoa-Gondar ◽  
Eva Satué
Keyword(s):  
Diabetes Mellitus ◽  
Myocardial Infarction ◽  
Atrial Fibrillation ◽  
Acute Myocardial Infarction ◽  
Population Based ◽  
Chronic Heart Disease ◽  
Increased Risk ◽  
Incidence And Mortality ◽  
History Of ◽  
Artery Disease

Background Population-based data about the epidemiology of acute myocardial infarction is limited. This study investigated incidence and mortality of acute myocardial infarction in older adults with specific underlying chronic conditions and evaluated the influence of these conditions in developing acute myocardial infarction. Design and methods This was a population-based cohort study involving 27,204 individuals ≥ 60 years of age in Tarragona (Catalonia, Spain). Data on all cases of hospitalised acute myocardial infarction were collected from 1 December 2008–30 November 2011. Incidence rates and 30-day mortality were estimated according to age, sex, chronic illnesses and underlying conditions. Multivariable Cox regression analysis was used to calculate hazard ratios and to estimate the association between baseline conditions and risk of developing acute myocardial infarction. Results The incidence of acute myocardial infarction was 475 per 100,000 person-years. Maximum rates appeared among individuals with history of coronary artery disease (2839 per 100,000), chronic severe nephropathy (1407 per 100,000), atrial fibrillation (1226 per 100,000), chronic heart disease (1149 per 100,000), history of stroke (1147 per 100,000) and diabetes mellitus (914 per 100,000). Thirty-day mortality was 15.3% overall, reaching 31.6% among patients over 80 years. In the multivariable analysis, history of coronary artery disease, age > 70 years, sex male, chronic heart disease, history of stroke, atrial fibrillation, diabetes mellitus and hypertension emerged as significantly associated with an increased risk of acute myocardial infarction. Conclusions The incidence and mortality of acute myocardial infarction remain considerable in our setting. Considering classical major risk factors, diabetes mellitus and hypertension were the underlying conditions most strongly associated with an increased risk in our study population.

scholarly journals Association Between Obstructive Sleep Apnea and Stroke and Contributory Risk Factors

2021 ◽  
Vol 18 (3) ◽  
pp. 119-126
Author(s):  
Ho Geol Woo ◽  
Kwang Ik Yang ◽  
Tae-Jin Song
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Positive Airway Pressure ◽  
Cardiac Repolarization ◽  
Neurological Outcomes ◽  
The Past ◽  
Obstructive Sleep

Obstructive sleep apnea (OSA), a common comorbidity in patients with stroke, has shown increasing prevalence over the past few decades. OSA is an important risk factor for stroke in addition to other well-known contributors, including hypertension, hyperlipidemia, atrial fibrillation, and diabetes mellitus. Moreover, OSA is an independent predictor of neurological outcomes and mortality. The pathological mechanisms underlying the association between OSA and stroke include autonomic dysfunction, hypertension, cardiac arrhythmia, dyslipidemia, impaired glucose tolerance, hypoxia, and inflammation. Continuous positive airway pressure (CPAP) therapy has proven clinical utility in improvement of neurological symptoms in patients with stroke. Findings from a CPAP withdrawal model have shown increased sympathetic activity in OSA with a consequent significant elevation in blood pressure, relevant cerebral hypoxia, and disturbed cardiac repolarization. In this review, we present an overview of the literature that describes an association between OSA and stroke in addition to the vascular risk factors, including hypertension, hyperlipidemia, atrial fibrillation, and diabetes mellitus. This study highlights the importance of early and accurate diagnosis and management of OSA for stroke prevention and care and will benefit physicians in clinical practice.

845 Progressive external ophthalmoplegia in sleep apnea presenting as floppy eyelid syndrome

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A329-A329
Author(s):  
Pratibha Anne ◽  
Rupa Koothirezhi ◽  
Ugorji Okorie ◽  
Minh Tam Ho ◽  
Brittany Monceaux ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Surgical Treatment ◽  
Sleep Apnea ◽  
Genetic Disorder ◽  
Deltoid Muscle ◽  
Progressive External Ophthalmoplegia ◽  
Ataxic Gait ◽  
Pressure Setting ◽  
Obstructive Sleep ◽  
History Of

Abstract Introduction Floppy eye lid syndrome (FES) is known to be associated with Obstructive sleep apnea (OSA) and chronic progressive external ophthalmoplegia (CPEO) is a rare genetic disorder with mitochondrial myopathy that may present with isolated eye lid ptosis in the initial stages. In a patient with loud snoring and obesity, treating obstructive sleep apnea may improve Floppy eyelid syndrome. Report of case(s) 52-year-old African – American male with past medical history of Hypertension, obesity, glaucoma, CPEO status bilateral blepharoplasty with failed surgical treatment. Patient was referred to Sleep medicine team to rule out Obstructive Sleep Apnea aa a cause of possible underlying FES and residual ptosis. On exam, patient was noted to have bilateral brow and eyelid ptosis and mild ataxic gait. MRI brain with and without contrast was unremarkable. Deltoid muscle biopsy was suggestive of possible congenital myopathy and mild denervation atrophy. Polysomnogram showed severe OSA with AHI of 74.1 per hour and patient was initiated on Auto CPAP at a pressure setting of 7–20 cm H2O. CPAP treatment improved snoring, OSA and subjective symptoms of excessive day time sleepiness but did not improve the residual ptosis. Conclusion Treatment of severe OSA in a patient previously diagnosed with CPEO and failed surgical treatment with bilateral blepharoplasty, did not alter the course of residual ptosis/ floppy eyelids even though his other sleep apnea symptoms have improved. Support (if any) 1. McNab AA. Floppy eyelid syndrome and obstructive sleep apnea. Ophthalmic Plast Reconstr Surg. 1997 Jun;13(2):98–114. doi: 10.1097/00002341-199706000-00005. PMID: 9185193.

scholarly journals Obstructive Sleep Apnea as a Risk Factor of Insulin Resistance in Nondiabetic Adults

Life ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Monika Michalek-Zrabkowska ◽  
Piotr Macek ◽  
Helena Martynowicz ◽  
Pawel Gac ◽  
Grzegorz Mazur ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Insulin Resistance ◽  
Obstructive Sleep Apnea ◽  
Risk Factor ◽  
Sleep Apnea ◽  
Glucose Concentration ◽  
Insulin Concentration ◽  
Fasting Insulin ◽  
Sleep Habits ◽  
Obstructive Sleep

Objective: The aim of this research was to assess the relationship between prevalence and severity of obstructive sleep apnea (OSA) and insulin resistance among patients with increased risk of OSA without diabetes mellitus. Method and materials: our study group involved 102 individuals with suspected OSA, mean age 53.02 ± 12.37 years. Data on medical history, medication usage, sleep habits, sleep quality and daytime sleepiness, were obtained using questionnaires. All patients underwent standardized full night polysomnography. Serum fasting insulin and glucose concentration were analyzed, the homeostatic model assessment-insulin resistance (HOMA-IR) index was calculated. Results: polysomnographic study indicated that in the group with OSA mean values of apnea–hypopnea index (AHI), oxygen desaturation index (ODI), duration of SpO2 < 90% and average desaturation drop were significantly higher compared to the group without OSA, while the minimum SpO2 was significantly lower. The carbohydrate metabolism parameters did not differ within those groups. Significantly higher fasting insulin concentration and HOMA-IR index were found in the group with AHI ≥ 15 compared to the group with AHI < 15 and in the group with AHI ≥ 30 compared to the group with AHI < 30. Higher AHI and ODI were independent risk factors for higher fasting insulin concentration and higher HOMA-IR index. Increased duration of SpO2 < 90% was an independent risk factor for higher fasting glucose concentration. Conclusions: Individuals with moderate to severe OSA without diabetes mellitus had a higher prevalence of insulin resistance.

419 STOP-Bang Score and History of Radiation Predicts Risk of Obstructive Sleep Apnea in Cancer Patients: A Machine Learning Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A166-A166
Author(s):  
Ankita Paul ◽  
Karen Wong ◽  
Anup Das ◽  
Diane Lim ◽  
Miranda Tan
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cancer Patients ◽  
Prediction Accuracy ◽  
Screening Tools ◽  
Cancer Type ◽  
Sleep Study ◽  
Obstructive Sleep ◽  
History Of

Abstract Introduction Cancer patients are at an increased risk of moderate-to-severe obstructive sleep apnea (OSA). The STOP-Bang score is a commonly used screening questionnaire to assess risk of OSA in the general population. We hypothesize that cancer-relevant features, like radiation therapy (RT), may be used to determine the risk of OSA in cancer patients. Machine learning (ML) with non-parametric regression is applied to increase the prediction accuracy of OSA risk. Methods Ten features namely STOP-Bang score, history of RT to the head/neck/thorax, cancer type, cancer stage, metastasis, hypertension, diabetes, asthma, COPD, and chronic kidney disease were extracted from a database of cancer patients with a sleep study. The ML technique, K-Nearest-Neighbor (KNN), with a range of k values (5 to 20), was chosen because, unlike Logistic Regression (LR), KNN is not presumptive of data distribution and mapping function, and supports non-linear relationships among features. A correlation heatmap was computed to identify features having high correlation with OSA. Principal Component Analysis (PCA) was performed on the correlated features and then KNN was applied on the components to predict the risk of OSA. Receiver Operating Characteristic (ROC) - Area Under Curve (AUC) and Precision-Recall curves were computed to compare and validate performance for different test sets and majority class scenarios. Results In our cohort of 174 cancer patients, the accuracy in determining OSA among cancer patients using STOP-Bang score was 82.3% (LR) and 90.69% (KNN) but reduced to 89.9% in KNN using all 10 features mentioned above. PCA + KNN application using STOP-Bang score and RT as features, increased prediction accuracy to 94.1%. We validated our ML approach using a separate cohort of 20 cancer patients; the accuracies in OSA prediction were 85.57% (LR), 91.1% (KNN), and 92.8% (PCA + KNN). Conclusion STOP-Bang score and history of RT can be useful to predict risk of OSA in cancer patients with the PCA + KNN approach. This ML technique can refine screening tools to improve prediction accuracy of OSA in cancer patients. Larger studies investigating additional features using ML may improve OSA screening accuracy in various populations Support (if any):

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