Sleep apnea symptoms and accident risk factors in Persian commercial vehicle drivers

Background : Sleep Apnea Syndrome (SAS) is a cause of hypertension, increasingcardiovascular risk and cardiovascular disease such as stroke, coronary artery disease, myocardial infarction, arrhythmias, heart failure, increasing the risk of death in patients with heart disease, independent of other causative factors. So far, in Vietnam there are very few studies on Obstructive Sleep Apnea Syndrome (OSAS) and cardiovascular risk factors . Self-making SASD07 is trustly for detecting OSAS with statistical significiant in comparision with StarDustII (gold criteria). Subjects and Methods: Cross sectional study, comparision with control group: 136 peoples (68 in disease group and 68 in control group). Patients were parallelly measured with StarDustII and SASD07 to detect OSAS and find the corellation with cardiovascular risk factors. Results: There is a positive correlation between SBP with the severity of OSAS (r = 0.459, p < 0.001), positive correlation between DBP with the severity of OSAS (r = 0.352, p < 0.003). No statistically significant differences between severe OSAS and fasting blood glucose, cholesterol, HDL Cholesterol, Non - HDL Cholesterol, LDL Cholesterol and TG median (p > 0.05). There is a positive correlation between AHI and neck circumference (r = 0.511, p < 0.001), waist circumference (r = 0.585, p < 0.001), BMI (r = 0.380, p < 0.01). SASD07 diagnostic value of detecting OSAS compared with StarDustII have Kappa = 0.72, (standard error 0.06, p <0.001). Conclusion: The risk factors related to OSAS in our study is neck circumference, waist circumference, systolic blood pressure, diastolic blood pressure. SASD07 have a good value in diagnosing of OSAS compared with polysomnography StarDustII. Key words: Sleep Apnea Syndrome, cardiovascular risk factor, SASD07.

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699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽

10.1093/sleep/zsab072.697 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A273-A273

Author(s):

Xi Zheng ◽

Ma Cherrysse Ulsa ◽

Peng Li ◽

Lei Gao ◽

Kun Hu

Keyword(s):

Risk Factors ◽

Sleep Apnea ◽

Sleep Duration ◽

Sex Education ◽

Mortality Risk ◽

Self Report ◽

Severe Illness ◽

Uk Biobank ◽

Sleep Health ◽

The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (>9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

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Predicting On-Road Driving Skills, Fitness to Drive, and Prospective Accident Risk in Older Drivers and Drivers with Mild Cognitive Impairment: The Importance of Non-Cognitive Risk Factors

Journal of Alzheimer s Disease ◽

10.3233/jad-200943 ◽

2021 ◽

Vol 79 (1) ◽

pp. 401-414

Author(s):

Max Toepper ◽

Philipp Schulz ◽

Thomas Beblo ◽

Martin Driessen

Keyword(s):

Risk Factors ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Older Drivers ◽

Accident Risk ◽

Fitness To Drive ◽

Cognitive Predictors ◽

Driving Skills ◽

Cognitive Risk ◽

Cognitive Risk Factors

Background: On-road driving behavior can be impaired in older drivers and particularly in drivers with mild cognitive impairment (MCI). Objective: To determine whether cognitive and non-cognitive risk factors for driving safety may allow an accurate and economic prediction of on-road driving skills, fitness to drive, and prospective accident risk in healthy older drivers and drivers with MCI, we examined a representative combined sample of older drivers with and without MCI (N = 74) in an observational on-road study. In particular, we examined whether non-cognitive risk factors improve predictive accuracy provided by cognitive factors alone. Methods: Multiple and logistic hierarchical regression analyses were utilized to predict different driving outcomes. In all regression models, we included cognitive predictors alone in a first step and added non-cognitive predictors in a second step. Results: Results revealed that the combination of cognitive and non-cognitive risk factors significantly predicted driving skills (R2adjusted = 0.30) and fitness to drive (81.2% accuracy) as well as the number (R2adjusted = 0.21) and occurrence (88.3% accuracy) of prospective minor at-fault accidents within the next 12 months. In all analyses, the inclusion of non-cognitive risk factors led to a significant increase of explained variance in the different outcome variables. Conclusion: Our findings suggest that a combination of the most robust cognitive and non-cognitive risk factors may allow an economic and accurate prediction of on-road driving performance and prospective accident risk in healthy older drivers and drivers with MCI. Therefore, non-cognitive risk factors appear to play an important role.

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Sleep Apnea-Plus: Prevalence, Risk Factors, And Association With Cardiovascular Diseases Using United States Population-Level Data

10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a5040 ◽

2012 ◽

Author(s):

Nicholas T. Vozoris

Keyword(s):

United States ◽

Risk Factors ◽

Sleep Apnea ◽

Cardiovascular Diseases ◽

Population Level ◽

United States Population ◽

Level Data

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Epidemiology, Risk Factors, and Consequences of Obstructive Sleep Apnea and Short Sleep Duration

Progress in Cardiovascular Diseases ◽

10.1016/j.pcad.2008.08.001 ◽

2009 ◽

Vol 51 (4) ◽

pp. 285-293 ◽

Cited By ~ 176

Author(s):

Nabil M. Al Lawati ◽

Sanjay R. Patel ◽

Najib T. Ayas

Keyword(s):

Risk Factors ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Sleep Duration ◽

Short Sleep Duration ◽

Short Sleep ◽

Obstructive Sleep

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Obstructive Sleep Apnea: Epidemiology, Causes, and Consequences

10.2310/im.1366 ◽

2020 ◽

Author(s):

Diane C Lim ◽

Richard J Schwab

Keyword(s):

Risk Factors ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Cushing Syndrome ◽

Upper Airway ◽

Fat Distribution ◽

Epidemiologic Studies ◽

Obstructive Sleep ◽

Upper Airway Muscles ◽

Underlying Diseases

As part one of the three chapters on sleep-disordered breathing, this chapter reviews obstructive sleep apnea (OSA) epidemiology, causes, and consequences. When comparing OSA prevalence between 1988 to 1994 and 2007 to 2010, we observe that OSA is rapidly on the rise, paralleling increasing rates in obesity. Global epidemiologic studies indicate that there are differences specific to ethnicity with Asians presenting with OSA at a lower body mass index than Caucasians. We have learned that structural and physiologic factors increase the risk of OSA and both can be influenced by genetics. Structural risk factors include craniofacial bony restriction, changes in fat distribution, and the size of the upper airway muscles. Physiologic risk factors include airway collapsibility, loop gain, pharyngeal muscle responsiveness, and arousal threshold. The consequences of OSA include daytime sleepiness and exacerbation of many underlying diseases. OSA has been associated with cardiovascular diseases including hypertension, coronary heart disease, stroke, atrial fibrillation, and other cardiac arrhythmias; pulmonary hypertension; metabolic disorders such as type 2 diabetes, hypothyroidism, acromegaly, Cushing syndrome, and polycystic ovarian syndrome; mild cognitive impairment or dementia; and cancer. This review contains 4 figures, 1 table and 48 references. Key Words: cardiac consequences, craniofacial bony restriction, epidemiology, fat distribution, metabolic disease, neurodegeneration, obesity, obstructive sleep apnea

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Abstract P195: Lack of Association between Sleep Duration and ECG Arrhythmia Risk Measures: Chicago Area Sleep Study

Circulation ◽

10.1161/circ.127.suppl_12.ap195 ◽

2013 ◽

Vol 127 (suppl_12) ◽

Author(s):

Jason Ng ◽

Phyllis C Zee ◽

Jeffrey J Goldberger ◽

Kristen L Knutson ◽

Kiang Liu ◽

...

Keyword(s):

Risk Factors ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Sleep Duration ◽

Low Risk ◽

Qtc Interval ◽

Sleep Study ◽

Chicago Area ◽

Obstructive Sleep ◽

Pr Interval

Introduction Sleep duration is significantly associated with cardiovascular disease risk factors such as hypertension, diabetes, and obesity in adults at low risk for obstructive sleep apnea. Although it is known that apnea increases the risk for sudden cardiac death, it is not known whether adults with short sleep duration independent of apnea have a higher risk for cardiac arrhythmias Hypothesis We tested the hypothesis that sleep duration in adults at low risk for obstructive sleep apnea would be associated with ECG measures that are known risk factors for ventricular arrhythmias. Methods The Chicago Area Sleep Study recruited 610 participants via commercially available telephone listings. Participants were screened using in-home apnea detection equipment (ApneaLinkTM) for one night to exclude subjects with apnea/hypopnea index ≥ 15. Participants wore wrist actigraphs for 7 days to objectively determine sleep duration. A 10-minute 12-lead ECG was recorded for each subject. Standard measures of heart rate, PR interval, and QTc interval were obtained along with markers of ventricular repolarization, Tpeak to Tend interval (Tpe) and spatial QRS-T angle. Signal-averaged ECG analysis was performed to measure filtered QRS duration (fQRSd), RMS voltage of terminal 40 ms (RMS), and duration of terminal QRS signals <40μV (LAS). Participants with atrial fibrillation, >20% ectopic beats and those using antihypertensive and sleep medications were excluded from analysis. The effect of sleep duration on the ECG parameters was estimated using a multiple linear regression model adjusting for demographics (sex, age, and race) and cardiovascular risk factors (BMI, hypertension, coronary heart disease, and diabetes). Results ECGs from a total of 504 participants (200 male, 48±8 years old) were analyzed. Mean sleep duration was 7±1 hrs, heart rate was 64±9 bpm, PR interval was 165±18 ms, and QTc interval was 424±23 ms. Mean Tpe interval was 83±14 ms and spatial QRS-T angle was 29±26 deg. The signal-averaged ECG measures of fQRSd, RMS, and LAS had mean values of 78±12 ms, 58±34 μV, and 24±9 ms, respectively. In an unadjusted model, there was a borderline association between sleep duration and QTc (β=0.004 ms/hr, SE=0.0023, p=0.08). However, that association was no longer significant following adjustment with demographics and cardiovascular risk factors. No other ECG measures were associated with sleep duration. Conclusions In a population at low risk of obstructive sleep apnea, ECG-based measures of cardiovascular risks were not associated with sleep duration. Previously reported associations between short sleep and cardiovascular events may not be arrhythmic in origin.

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