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Prevalence of risk factors for atrial fibrillation and stroke among 1210 patients with sleep disordered breathing
International Journal of Cardiology 174 (2014) 73–76
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Prevalence of risk factors for atrial fibrillation and stroke among 1210 patients with sleep disordered breathing☆,☆☆ Marie Carmen Valenza a,⁎, Adrian Baranchuk b, Gerald Valenza-Demet a, Tomás Muñoz-Casaubon c, Jose Antonio Martin-Navajas d, Jeff Healey e a
Department of Physical Therapy, Universidad de Granada, Granada, Spain Division of Cardiology (Arrhythmia Service), Kingston General Hospital, Queen's University, Kingston, Ontario, Canada Sleep Laboratory, Department of Pulmonology, San Cecilio Hospital, Granada, Spain d Division of Cardiology, San Cecilio Hospital, Granada, Spain e Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada b c
a r t i c l e
i n f o
Article history: Received 18 December 2013 Received in revised form 13 February 2014 Accepted 22 March 2014 Available online 29 March 2014 Keywords: Sleep apnea Atrial fibrillation Stroke
a b s t r a c t Aims: This study sought to identify the prevalence of risk factors for atrial fibrillation and stroke in a sleep apnea population. Methods: Study participants included 1210 consecutive adults who were referred with suspicion of sleep apnea. Statistical analysis was used to determine the relationship between sleep apnea syndrome and risk factors for atrial fibrillation and stroke. Results: Among 1210 enrolled patients, 65.8% had severe sleep apnea (Apnea/hypopnea Index — AHI N 30), 25.2% had mild to moderate sleep apnea (AHI 5 to 30), and 8.8% had no sleep apnea (AHI b 5). At baseline, the mean apnea–hypopnea index in patients with sleep apnea syndrome was 35. Compared to patients with an AHI b5, those with an AHI N 30 were older (47.3 ± 11.4 vs. 52.74 ± 12.4, p b 0.001) and had a higher body mass index (BMI) (30.7 ± 7.3 vs. 33.83 ± 10.1, p b 0.001), a higher prevalence of hypertension (38 vs. 16%, p b 0.001), and a higher CHADS2 (congestive heart failure, hypertension, age, diabetes and prior stroke) score (0.59 ± 0.8 vs. 0.28 ± 0.64, p b 0.001). Conclusions: Patients with severe sleep apnea have a higher prevalence of risk factors for atrial fibrillation and stroke when compared with subjects without sleep apnea. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction After insomnia, sleep apnea (SA) is the most common sleep disorder, affecting approximately 5% of the adult population [1]. Sleep apnea has been independently linked to adverse cardiovascular outcomes such as hypertension [2–4], stroke [2–4], myocardial ischemia [5–8], arrhythmias [9,10], and both cardiovascular [2,3] and all-cause mortality [3, 11,12]. Patients with sleep apnea are frequently diagnosed with coexisting autonomic imbalance [13] and diastolic heart dysfunction [14]. It is considered that these two mechanisms may potentially promote the development of atrial fibrillation (AF), which is the most
common arrhythmia encountered in clinical practice and an important cause of stroke [15,16]. Earlier studies have assessed the prevalence of sleep-disordered breathing among patients with cardiovascular disease in individuals undergoing cardiac assessment [17] or cardiac interventions [18], for example. The main objective of this study was to assess the prevalence of cardiovascular risk factors for AF and stroke in an unbiased sample of patients referred for the assessment of sleep apnea and to determine whether there is a correlation between the severity of sleep apnea and the prevalence of AF and stroke.
2. Methodology ☆ Financial support: This study was partially funded by a project grant from the Granada Research of Excellence Initiative on BioHealth, Spain. ☆☆ The data of the patients was developed in Granada (Spain) and the analysis of the data and article writing was done in Canada with the collaboration of Dr. Baranchuk and Dr. Healey during an observership. ⁎ Corresponding author at: Departmento de Fisioterapia, Facultad de Ciencias de la Salud, Universidad de Granada, Av. de Madrid SN, 18071 Granada, Spain. Tel./fax: +34 958242360. E-mail address: [email protected] (M.C. Valenza).
http://dx.doi.org/10.1016/j.ijcard.2014.03.156 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
We conducted a cross-sectional study that included consecutive patients referred to the Sleep Laboratory of San Cecilio Hospital specifically for the evaluation of sleep-disordered breathing. All patients underwent a baseline clinical evaluation and diagnostic polysomnography. The presence of clinical risk factors for AF, including: age, gender, hypertension, body mass index (BMI), congestive heart failure, prior stroke and CHADS2 score; were compared between three patient groups: those
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M.C. Valenza et al. / International Journal of Cardiology 174 (2014) 73–76
without sleep apnea (AHI b 5), those with mild to moderate sleep apnea (AHI N 5 and b 30) and those with severe sleep apnea (AHI N 30). The study was approved by the Human Investigation Committee of San Cecilio University Hospital and all subjects gave their written informed consent prior to their inclusion in the study. 3. Baseline assessment Data on demographic characteristics, sleep, medical history, medication use, and habits were obtained with the use of a standardized questionnaire administered by a trained researcher. Patients with documented history of AF were excluded from the study. The interview was conducted in the presence of participants' most closely related person, preferably their spouse. After completion of this assessment, subjects underwent overnight polysomnography. Patients' height and weight were recorded at the time of the polysomnography and used to calculate their body mass index (BMI). Sleep-history data included the Berlin questionnaire [19], the Epworth Sleepiness Scale [20], and self-reported habitual snoring, which was defined as loud snoring occurring “frequently” or “constantly.” Data regarding medication use included the daily use of beta-blockers, diuretics, other antihypertensive medications, antiplatelet therapy, and anticoagulants. Data on risk factors included a history of hypertension, diabetes mellitus and chronic obstructive pulmonary disease (COPD), either reported by the patient on the baseline medical questionnaire or noted by the referring physician. In addition, the pulmonary function test, electrocardiography, and plethysmography performed during polysomnography were considered sufficient evidence to establish the diagnosis of sleep apnea. Patients were classified according to whether they were current or former smokers or had never smoked. If applicable, data were based on the number of pack-years of smoking. 4. Polysomnography Participants attended overnight polysomnography using Grass dataacquisition systems (Astro-Med, West Warwick, RI) on the basis of a protocol described previously [21]. A single polysomnography study conducted during an entire night was used to establish the presence of sleep apnea [22]. Sleep stages were scored over 30-second intervals according to standard criteria [23]. Total cessation of airflow in the nose and mouth for at least 10 s was classified as apnea (obstructive apnea if respiratory efforts were present and central apnea if respiratory efforts were absent). Partial airway closure resulting in a reduction of more than 30% of airflow for at least 10 s and associated with 4% or more oxygen desaturation was considered hypopnea [24]. The polysomnographic variables assessed included the apnea–hypopnea index (AHI), that is, the number of apneas–hypopneas per hour of sleep. 5. Statistical analysis We used SPSS statistical software, version 16.0 for descriptive statistics and data analysis. Subjects were distributed into 3 groups according to their polysomnography results: subjects with severe SA (AHI N 30), subjects with mild to moderate SA (AHI: 5–30), and subjects with no SA (AHI b 5). All continuous data were examined for normality using the Shapiro–Wilk W statistic. An ANOVA was used to compare numerical variables among the three groups. The χ2 test was used to compare categorical variables among three groups. A p value b0.05 was considered statistically significant. 6. Results The study population consisted of 1210 consecutive subjects referred for polysomnography. Severe SA (AHI N 30) was present in 65.8% of participants, mild to moderate SA (AHI: 5–30) was found in
25.2% of them, and no SA (AHI b 5) was found in 8.8% of the sample. The age, BMI, gender, and medical history characteristics of subjects according to their AHI results are reported in Table 1. Patients with severe SA were older, more likely to be male, and had a higher BMI (Table 1). Patients with severe SA were also more likely to have a history of hypertension and a higher CHADS2 score (Table 1). Smoking history and daily alcohol consumption were frequent in this population, but no significant differences were found among patients with severe SA, mild to moderate SA, and no SA. No significant differences were found either in medication use between the SA groups. Compared to patients without SA, those with severe SA had a higher systolic blood pressure and more abnormal Berlin questionnaire results. However, no differences were found in any pulmonary function test parameters or the Epworth Sleepiness Scale (Table 2). 7. Discussion This large, single-center study demonstrates that risk factors for AF and stroke are highly prevalent among patients referred for the assessment of SA, particularly those with evidence of severe SA at the time of polysomnography. Nearly 50% of patients with severe SA had a CHADS2
Table 1 Recognized risk factors for the development of Stroke and AF, distribution of medication intake distribution in the sample by AHI score. Statistical significance were shown if p b 0.001 between: group AHI b 5 and group 5-AHI-30: a; group AHI b 5 and group AHI N 30: b; and group 5-AHI-30 and group AHI N 30: c. Variables
AHI b 5 (n = 107)
AHI 5–30 (n = 306)
AHI N 30 (n = 797)
Age (years) Mean ± SD Sex (females) n (%) BMI (Kg/m2) Mean ± SD Smoking history: Never Current Ex-smoker Alcohol (daily consumers) n (%) Percent SaO2 b 90% Mean ± SD HTN n (%) COPD n (%) Diabetes n (%) Prior stroke or TIA n (%) Heart failure n (%) CHADS2 score Mean ± SD CHADS2 score 0 n (%) CHADS2 score 1 n (%) CHADS2 Score ≥ 2 n (%) Medications Digoxin, % ACE inhibitors, % Diuretics, % B-blockers, % Amiodarone, % ASA, % Anticoagulant, % Benzodiazepines, %
47.3 ± 11.4
49.3 ± 12.3
52.74 ± 12.4
97 (59.5)
77 (33.3)
170 (21)
b0.001b,c
30.7 ± 7.3
30.8 ± 4.7
33.83 ± 10.1
b0.001b,c
77 (43.9) 51 (36.4) 35 (19.6) 46 (43.0)
113 (48.9) 61 (26.9) 56 (24.3) 154 (50.3)
262 (32.5) 289 (35.7) 258 (32.0) 430 (54.0)
3.7 ± 11.3
7.7 ± 12.8
23.2 ± 25.1
b0.001b,c
19 (17.8)
83 (27.1)
308 (38.6)
b0.001
3 (2.8)
11 (3.6)
38 (4.8)
0.502
4 (3.7)
24 (7.8)
78 (9.8)
0.093
3 (2.8)
7 (2.3)
18 (2.3)
0.939
1 (0.9)
6 (2)
18 (3.4)
0.206
0.28 ± 0.64
0.43 ± 0.8
0.59 ± 0.8
82 (79.6)
207 (70.2)
423 (56.3)
16 (15.5)
64 (21.7)
248 (33)
5 (4.9)
24 (8.1)
81 (10.8)
1.7 16.9 10.2 10.2 0.5 8.5 13.6 1.7
1.9 12.3 5.7 6.6 5.7 5.7 12.3 6.6
2.5 10.9 14.3 10.9 3.4 5 11.8 9.2
p-Value 0.001b
b0.001 0.081
b0.001b
b0.001
0.918 0.517 0.103 0.509 0.166 0.651 0.943 0.165
AHI: Apnea/hypopnea Index; SD: Standard deviation; BMI: Body mass index; SaO2: Oxygen saturation; HTN: Hypertension; COPD: Chronic obstructive pulmonary disease; TIA: Transient ischemic attack; ACE: Angiotensin converting enzyme; ASA: Acetylsalicylic acid.
M.C. Valenza et al. / International Journal of Cardiology 174 (2014) 73–76
75
Table 2 Sample distribution according to the polysomnography results, related to their clinical characteristics. Chi-squared test and ANOVA analysis were applied to relate the clinical variables to the AHI score group. Statistical significance were shown if p b 0.001 between: group AHI b 5 and group 5-AHI-30: a; group AHI b 5 and group AHI N 30: b; and group 5-AHI-30 and group AHI N 30: c. Variables
AHI b 5 (n = 107)
AHI 5–30 (n = 306)
AHI N 30 (n = 797)
Heart rate medium mean ± SD Minimal heart rate mean ± SD Maximal heart rate mean ± SD Systolic pressure mean ± SD Diastolic Pressure mean ± SD FEV1 (% predicted values) mean ± SD FVC (% predicted values) Mean ± SD Epworth Score mean ± SD Berlin Questionnaire score HIGH risk, % Snorers, %
68.7 ± 12
70.6 ± 12.8
68.33 ± 11.4
0.406
52 ± 7.6
52.4 ± 8.2
52 ± 9.9
0.947
94.5 ± 10.5
96.7 ± 12.5
99.7 ± 20.2
0.186
133.9 ± 19
132.7 ± 18.4
139.2 ± 15.25
0.035c
83.3 ± 11
83.6 ± 10
85.8 ± 12.2
0.318
113.6 ± 29.3
110.7 ± 20
109.3 ± 17.3
0.693
121 ± 37.5
111.8 ± 19.6
113.3 ± 17.7
0.222
11.6 ± 5.4
11.34 ± 5
12 ± 5.22
0.226
86.9
82.6
91.3
98
97.9
99
p-Value
b0.001 0.339
SD: Standard deviation; FEV1: Forced expiratory volume in 1 s; FVC: Forced vital capacity.
score ≥1, which would typically trigger the use of oral anticoagulation if AF was identified [25–27]. Hypertension was the most common cardiovascular condition found in patients with SA, and both the prevalence of hypertension and the absolute level of systolic blood pressure were higher among patients with severe SA. SA is associated with increased risk of stroke in middle-aged and older adults, particularly men [1,2]. The Sleep Heart Health Study (SHHS) [2] reported that men with moderate to severe sleep apnea were nearly three times more likely to have a stroke than men without sleep apnea or with mild sleep apnea. In women, however, the increased risk of stroke was significant only with severe levels of sleep apnea [2]. Cardiac arrhythmias also seem to be more frequent in people with SA and are most commonly observed in patients with a greater number of apneic episodes and more severe hypoxemia [28–30]. The Sleep Heart Health Study has shown that patients with severe SA (AHI N 30 events/h sleep) have a four-fold increased risk of occurrence of AF compared to matched controls without SA [31]. Although SA increases the risk of both stroke and AF, it remains to be clarified what proportion of this increased stroke risk is mediated by AF compared to other stroke risk factors such as hypertension. Our study is consistent with earlier work showing that patients with SA have a high prevalence of conditions that are associated with both AF and stroke, including obesity and hypertension. Data obtained by Framingham [32] and the Danish Diet, Cancer and Health Study [34] examining independent predictors of AF have highlighted obesity as an important marker. The risk of AF raises by about 8% for each body mass index (BMI) unit increase [33,34]. The population assessed in our study showed a very high prevalence of obesity, which was higher among patients with severe SA. However, it appears that obesity predicts AF independent of SA [30,31], suggesting that other mechanisms are also important. The causal relationship between SA and AF is strengthened by studies which demonstrate a reduction in AF with continuous positive airway pressure CPAP treatment for SA [29]. Treatment of SA with tracheostomy has also been shown to reduce nocturnal AF in patients with severe SA and nocturnal paroxysms of AF [35]. Recently, CPAP treatment for severe SA during 4–6 weeks induced reversion of electrical atrial remodeling as measured by signal P-wave analysis. This may imply an improvement of the AF electrical substrate [36]. Kanalaga et al. [18] have shown that treating SA with CPAP reduces the risk of AF recurrence after electrical cardioversion.
Finally, a decrease in arrhythmias was noted after bariatric surgery in morbidly obese patients with SA [16]. Although the evidence that SA treatment can prevent AF is encouraging, it is based on a modest number of studies, few of which were randomized. Moreover, there are no corresponding data indicating that sleep apnea treatment reduces stroke, which would require much larger clinical trials. 8. Limitations We only used the CHADS2 score, possibly leading to an underestimation of low-risk patients. All patients were consecutively enrolled from a Sleep Disorder clinic, maybe leading to a sub-selection of patients at higher risk of presenting risk factors for AF and stroke. 9. Conclusions Patients with SA have a significant prevalence of risk factors for AF and stroke that increases with the severity of SA. Identification of AF in patients with SA is important, as it would lead to increasing use of anticoagulants in this population. Acknowledgment We adhere to the STROBE statement [37]. References [1] Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002;165:1217–39. [2] Nieto FJ, Young TB, Lind BK, et al. Association of sleep disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. JAMA 2000;283(14):1829–36. [3] Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000;342:1378–84. [4] Lavie Pk, Here P, Hoffstein V. Obstructive sleep apnea syndrome as a risk factor for hypertension: population study. BMJ 2000;320:479–82. [5] Katzmarzyk PT, Mason C. Prevalence of class I, II, and III obesity in Canada. CMAJ 2006;174:156–7. [6] Geiss LS, Pan L, Cadwell B, Gregg EW, Benjamin SM, Engelgau MM. Changes in incidence of diabetes in US adults, 1997–2003. Am J Prev Med 2006;30:371–7. [7] Banno K, Kryger MH. Sleep apnea: clinical investigations in humans. Sleep Med 2007;8:400–26. [8] Tarasiuk A, Greenberg-Dotan S, Simon-Tuval T, Oksenberg A, Reuveni H. The effect of obstructive sleep apnea on morbidity and health care utilization of middle-aged and older adults. J Am Geriatr Soc 2008;56:247–54. [9] Baranchuk A, Simpson CS, Redfearn DP, Fitzpatrick M. It's time to wake up! Sleep apnea & cardiac arrhythmias. Europace 2008;10(6):666–7.
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[10] Baranchuk A, Simpson CS, Redfearn DP, Michael KA, Fitzpatrick M. Understanding the association between sleep apnea & cardiac arrhythmias. REA 2008;1:5–6. [11] Arzt M, Young T, Finn L, Skatrud JB, Bradley TD. Association of sleep disordered breathing and the occurrence of stroke. Am J Respir Crit Care Med 2005;172:1447–51. [12] Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea–hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 2005;365:1046–53. [13] Roche F, Xuong AN, Court-Fortune, et al. Relationship among the severity of sleep apnea syndrome, cardiac arrhythmias, and autonomic imbalance. Pacing Clin Electrophysiol 2003;26:669–77. [14] Niroumand M, Kuperstein R, Sasson Z, Hanly PJ. Impact of obstructive sleep apnea on left ventricular mass and diastolic function. Am J Respir Crit Care Med 2001;163:1632–6. [15] Healey JS, Connolly SJ, Gold MR, et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med 2012;366(2):120–9. [16] Yazdan-Ashoori P, Baranchuk A. Obstructive sleep apnea may increase the risk of stroke in AF patients: refining the CHADS2 score. Int J Cardiol 2011;146:131–3. [17] Gami AS, Pressman G, Caples SM, et al. Association of atrial fibrillation and obstructive sleep apnea. Circulation 2004;110:364–7. [18] Kanagala R, Murali NS, Friedman PA, et al. Obstructive sleep apnea and the recurrence of atrial fibrillation. Circulation 2003;107:2589–94. [19] Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP. Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med 1999;131:485–91. [20] Johns MW. Daytime sleepiness, snoring, and obstructive sleep apnea: the Epworth Sleepiness Scale. Chest 1993;103:30–6. [21] Mohsenin V, Valor R. Sleep apnea in patients with hemispheric stroke. Arch Phys Med Rehabil 1995;76:71–6. [22] Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research: the Report of an American Academy of Sleep Medicine Task Force. Sleep 1999;22:667–89. [23] Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Los Angeles: UCLA Brain Information Service/Brain Research Institute; 1968. [24] Meoli AL, Casey KR, Clark RW, et al. Hypopnea in sleep-disordered breathing in adults. Sleep 2001;24:469–70.
[25] Camm AJ, Kirchhof P, Lip GYH, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010;31:2369–429. [26] Wann LS, Curtis AB, January CT, et al. ACCF/AHA/HRS focused update on the management of patients with atrial fibrillation (updating the 2006 guideline). Heart Rhythm 2011;8:157–76. [27] Incidence of atrial fibrillation in patients with severe obstructive sleep apnea: The Reveal XT-SA study. http://clinicaltrials.gov/ct2/show/NCT01058551. [Accessed on 2/2/2012]. [28] Koehler U, Schafer H. Is obstructive sleep apnea (OSA) a risk factor for myocardial infarction and cardiac arrhythmias in patients with coronary heart disease (CHD)? Sleep 1996;19:283–6. [29] Liston R, Deegan PC, McCreery C, McNicholas WT. Role of respiratory sleep disorders in the pathogenesis of nocturnal angina and arrhythmias. Postgrad Med J 1994;70:275–80. [30] Ryan CM, Usui K, Floras JS, Bradley TD. Effect of continuous positive airway pressure on ventricular ectopy in heart failure patients with obstructive sleep apnoea. Thorax 2005;60:781–5. [31] Mehra R, Benjamin EJ, Shahar E, et al. Association of nocturnal arrhythmias with sleep-disordered breathing: The Sleep Heart Health Study. Am J Respir Crit Care Med 2006;173:910–6. [32] Wang TJ, Parise H, Levy D, et al. Obesity and the risk of new-onset atrial fibrillation. JAMA 2004;292:2471–7. [33] Frost L, Hune LJ, Vestergaard P. Overweight and obesity as risk factors for atrial fibrillation or flutter: the Danish Diet, Cancer, and Health. Study Am J Med 2005;118:489–95. [34] Dublin S, French B, Glazer NL, et al. Risk of new-onset atrial fibrillation in relation to body mass index. Arch Intern Med 2006;166:2322–8. [35] Guilleminault C, Conolly SJ, Winkle RA. Cardiac arrhythmia and conduction disturbances during sleep in 400 patients with sleep apnea syndrome. Am J Cardiol 1983;52:490–4. [36] Baranchuk A, Pang H, Seaborn GEJ, et al. Reverse atrial electrical remodelling induced by continuous positive airway pressure in patients with severe obstructive sleep apnea. J Interv Card Electrophysiol 2013;36:247–53. [37] von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 2008;61(4):344–9.
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Prevalence of risk factors for atrial fibrillation and stroke among 1210 patients with sleep disordered breathing☆,☆☆ Marie Carmen Valenza a,⁎, Adrian Baranchuk b, Gerald Valenza-Demet a, Tomás Muñoz-Casaubon c, Jose Antonio Martin-Navajas d, Jeff Healey e a
Department of Physical Therapy, Universidad de Granada, Granada, Spain Division of Cardiology (Arrhythmia Service), Kingston General Hospital, Queen's University, Kingston, Ontario, Canada Sleep Laboratory, Department of Pulmonology, San Cecilio Hospital, Granada, Spain d Division of Cardiology, San Cecilio Hospital, Granada, Spain e Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada b c
a r t i c l e
i n f o
Article history: Received 18 December 2013 Received in revised form 13 February 2014 Accepted 22 March 2014 Available online 29 March 2014 Keywords: Sleep apnea Atrial fibrillation Stroke
a b s t r a c t Aims: This study sought to identify the prevalence of risk factors for atrial fibrillation and stroke in a sleep apnea population. Methods: Study participants included 1210 consecutive adults who were referred with suspicion of sleep apnea. Statistical analysis was used to determine the relationship between sleep apnea syndrome and risk factors for atrial fibrillation and stroke. Results: Among 1210 enrolled patients, 65.8% had severe sleep apnea (Apnea/hypopnea Index — AHI N 30), 25.2% had mild to moderate sleep apnea (AHI 5 to 30), and 8.8% had no sleep apnea (AHI b 5). At baseline, the mean apnea–hypopnea index in patients with sleep apnea syndrome was 35. Compared to patients with an AHI b5, those with an AHI N 30 were older (47.3 ± 11.4 vs. 52.74 ± 12.4, p b 0.001) and had a higher body mass index (BMI) (30.7 ± 7.3 vs. 33.83 ± 10.1, p b 0.001), a higher prevalence of hypertension (38 vs. 16%, p b 0.001), and a higher CHADS2 (congestive heart failure, hypertension, age, diabetes and prior stroke) score (0.59 ± 0.8 vs. 0.28 ± 0.64, p b 0.001). Conclusions: Patients with severe sleep apnea have a higher prevalence of risk factors for atrial fibrillation and stroke when compared with subjects without sleep apnea. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction After insomnia, sleep apnea (SA) is the most common sleep disorder, affecting approximately 5% of the adult population [1]. Sleep apnea has been independently linked to adverse cardiovascular outcomes such as hypertension [2–4], stroke [2–4], myocardial ischemia [5–8], arrhythmias [9,10], and both cardiovascular [2,3] and all-cause mortality [3, 11,12]. Patients with sleep apnea are frequently diagnosed with coexisting autonomic imbalance [13] and diastolic heart dysfunction [14]. It is considered that these two mechanisms may potentially promote the development of atrial fibrillation (AF), which is the most
common arrhythmia encountered in clinical practice and an important cause of stroke [15,16]. Earlier studies have assessed the prevalence of sleep-disordered breathing among patients with cardiovascular disease in individuals undergoing cardiac assessment [17] or cardiac interventions [18], for example. The main objective of this study was to assess the prevalence of cardiovascular risk factors for AF and stroke in an unbiased sample of patients referred for the assessment of sleep apnea and to determine whether there is a correlation between the severity of sleep apnea and the prevalence of AF and stroke.
2. Methodology ☆ Financial support: This study was partially funded by a project grant from the Granada Research of Excellence Initiative on BioHealth, Spain. ☆☆ The data of the patients was developed in Granada (Spain) and the analysis of the data and article writing was done in Canada with the collaboration of Dr. Baranchuk and Dr. Healey during an observership. ⁎ Corresponding author at: Departmento de Fisioterapia, Facultad de Ciencias de la Salud, Universidad de Granada, Av. de Madrid SN, 18071 Granada, Spain. Tel./fax: +34 958242360. E-mail address: [email protected] (M.C. Valenza).
http://dx.doi.org/10.1016/j.ijcard.2014.03.156 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
We conducted a cross-sectional study that included consecutive patients referred to the Sleep Laboratory of San Cecilio Hospital specifically for the evaluation of sleep-disordered breathing. All patients underwent a baseline clinical evaluation and diagnostic polysomnography. The presence of clinical risk factors for AF, including: age, gender, hypertension, body mass index (BMI), congestive heart failure, prior stroke and CHADS2 score; were compared between three patient groups: those
74
M.C. Valenza et al. / International Journal of Cardiology 174 (2014) 73–76
without sleep apnea (AHI b 5), those with mild to moderate sleep apnea (AHI N 5 and b 30) and those with severe sleep apnea (AHI N 30). The study was approved by the Human Investigation Committee of San Cecilio University Hospital and all subjects gave their written informed consent prior to their inclusion in the study. 3. Baseline assessment Data on demographic characteristics, sleep, medical history, medication use, and habits were obtained with the use of a standardized questionnaire administered by a trained researcher. Patients with documented history of AF were excluded from the study. The interview was conducted in the presence of participants' most closely related person, preferably their spouse. After completion of this assessment, subjects underwent overnight polysomnography. Patients' height and weight were recorded at the time of the polysomnography and used to calculate their body mass index (BMI). Sleep-history data included the Berlin questionnaire [19], the Epworth Sleepiness Scale [20], and self-reported habitual snoring, which was defined as loud snoring occurring “frequently” or “constantly.” Data regarding medication use included the daily use of beta-blockers, diuretics, other antihypertensive medications, antiplatelet therapy, and anticoagulants. Data on risk factors included a history of hypertension, diabetes mellitus and chronic obstructive pulmonary disease (COPD), either reported by the patient on the baseline medical questionnaire or noted by the referring physician. In addition, the pulmonary function test, electrocardiography, and plethysmography performed during polysomnography were considered sufficient evidence to establish the diagnosis of sleep apnea. Patients were classified according to whether they were current or former smokers or had never smoked. If applicable, data were based on the number of pack-years of smoking. 4. Polysomnography Participants attended overnight polysomnography using Grass dataacquisition systems (Astro-Med, West Warwick, RI) on the basis of a protocol described previously [21]. A single polysomnography study conducted during an entire night was used to establish the presence of sleep apnea [22]. Sleep stages were scored over 30-second intervals according to standard criteria [23]. Total cessation of airflow in the nose and mouth for at least 10 s was classified as apnea (obstructive apnea if respiratory efforts were present and central apnea if respiratory efforts were absent). Partial airway closure resulting in a reduction of more than 30% of airflow for at least 10 s and associated with 4% or more oxygen desaturation was considered hypopnea [24]. The polysomnographic variables assessed included the apnea–hypopnea index (AHI), that is, the number of apneas–hypopneas per hour of sleep. 5. Statistical analysis We used SPSS statistical software, version 16.0 for descriptive statistics and data analysis. Subjects were distributed into 3 groups according to their polysomnography results: subjects with severe SA (AHI N 30), subjects with mild to moderate SA (AHI: 5–30), and subjects with no SA (AHI b 5). All continuous data were examined for normality using the Shapiro–Wilk W statistic. An ANOVA was used to compare numerical variables among the three groups. The χ2 test was used to compare categorical variables among three groups. A p value b0.05 was considered statistically significant. 6. Results The study population consisted of 1210 consecutive subjects referred for polysomnography. Severe SA (AHI N 30) was present in 65.8% of participants, mild to moderate SA (AHI: 5–30) was found in
25.2% of them, and no SA (AHI b 5) was found in 8.8% of the sample. The age, BMI, gender, and medical history characteristics of subjects according to their AHI results are reported in Table 1. Patients with severe SA were older, more likely to be male, and had a higher BMI (Table 1). Patients with severe SA were also more likely to have a history of hypertension and a higher CHADS2 score (Table 1). Smoking history and daily alcohol consumption were frequent in this population, but no significant differences were found among patients with severe SA, mild to moderate SA, and no SA. No significant differences were found either in medication use between the SA groups. Compared to patients without SA, those with severe SA had a higher systolic blood pressure and more abnormal Berlin questionnaire results. However, no differences were found in any pulmonary function test parameters or the Epworth Sleepiness Scale (Table 2). 7. Discussion This large, single-center study demonstrates that risk factors for AF and stroke are highly prevalent among patients referred for the assessment of SA, particularly those with evidence of severe SA at the time of polysomnography. Nearly 50% of patients with severe SA had a CHADS2
Table 1 Recognized risk factors for the development of Stroke and AF, distribution of medication intake distribution in the sample by AHI score. Statistical significance were shown if p b 0.001 between: group AHI b 5 and group 5-AHI-30: a; group AHI b 5 and group AHI N 30: b; and group 5-AHI-30 and group AHI N 30: c. Variables
AHI b 5 (n = 107)
AHI 5–30 (n = 306)
AHI N 30 (n = 797)
Age (years) Mean ± SD Sex (females) n (%) BMI (Kg/m2) Mean ± SD Smoking history: Never Current Ex-smoker Alcohol (daily consumers) n (%) Percent SaO2 b 90% Mean ± SD HTN n (%) COPD n (%) Diabetes n (%) Prior stroke or TIA n (%) Heart failure n (%) CHADS2 score Mean ± SD CHADS2 score 0 n (%) CHADS2 score 1 n (%) CHADS2 Score ≥ 2 n (%) Medications Digoxin, % ACE inhibitors, % Diuretics, % B-blockers, % Amiodarone, % ASA, % Anticoagulant, % Benzodiazepines, %
47.3 ± 11.4
49.3 ± 12.3
52.74 ± 12.4
97 (59.5)
77 (33.3)
170 (21)
b0.001b,c
30.7 ± 7.3
30.8 ± 4.7
33.83 ± 10.1
b0.001b,c
77 (43.9) 51 (36.4) 35 (19.6) 46 (43.0)
113 (48.9) 61 (26.9) 56 (24.3) 154 (50.3)
262 (32.5) 289 (35.7) 258 (32.0) 430 (54.0)
3.7 ± 11.3
7.7 ± 12.8
23.2 ± 25.1
b0.001b,c
19 (17.8)
83 (27.1)
308 (38.6)
b0.001
3 (2.8)
11 (3.6)
38 (4.8)
0.502
4 (3.7)
24 (7.8)
78 (9.8)
0.093
3 (2.8)
7 (2.3)
18 (2.3)
0.939
1 (0.9)
6 (2)
18 (3.4)
0.206
0.28 ± 0.64
0.43 ± 0.8
0.59 ± 0.8
82 (79.6)
207 (70.2)
423 (56.3)
16 (15.5)
64 (21.7)
248 (33)
5 (4.9)
24 (8.1)
81 (10.8)
1.7 16.9 10.2 10.2 0.5 8.5 13.6 1.7
1.9 12.3 5.7 6.6 5.7 5.7 12.3 6.6
2.5 10.9 14.3 10.9 3.4 5 11.8 9.2
p-Value 0.001b
b0.001 0.081
b0.001b
b0.001
0.918 0.517 0.103 0.509 0.166 0.651 0.943 0.165
AHI: Apnea/hypopnea Index; SD: Standard deviation; BMI: Body mass index; SaO2: Oxygen saturation; HTN: Hypertension; COPD: Chronic obstructive pulmonary disease; TIA: Transient ischemic attack; ACE: Angiotensin converting enzyme; ASA: Acetylsalicylic acid.
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75
Table 2 Sample distribution according to the polysomnography results, related to their clinical characteristics. Chi-squared test and ANOVA analysis were applied to relate the clinical variables to the AHI score group. Statistical significance were shown if p b 0.001 between: group AHI b 5 and group 5-AHI-30: a; group AHI b 5 and group AHI N 30: b; and group 5-AHI-30 and group AHI N 30: c. Variables
AHI b 5 (n = 107)
AHI 5–30 (n = 306)
AHI N 30 (n = 797)
Heart rate medium mean ± SD Minimal heart rate mean ± SD Maximal heart rate mean ± SD Systolic pressure mean ± SD Diastolic Pressure mean ± SD FEV1 (% predicted values) mean ± SD FVC (% predicted values) Mean ± SD Epworth Score mean ± SD Berlin Questionnaire score HIGH risk, % Snorers, %
68.7 ± 12
70.6 ± 12.8
68.33 ± 11.4
0.406
52 ± 7.6
52.4 ± 8.2
52 ± 9.9
0.947
94.5 ± 10.5
96.7 ± 12.5
99.7 ± 20.2
0.186
133.9 ± 19
132.7 ± 18.4
139.2 ± 15.25
0.035c
83.3 ± 11
83.6 ± 10
85.8 ± 12.2
0.318
113.6 ± 29.3
110.7 ± 20
109.3 ± 17.3
0.693
121 ± 37.5
111.8 ± 19.6
113.3 ± 17.7
0.222
11.6 ± 5.4
11.34 ± 5
12 ± 5.22
0.226
86.9
82.6
91.3
98
97.9
99
p-Value
b0.001 0.339
SD: Standard deviation; FEV1: Forced expiratory volume in 1 s; FVC: Forced vital capacity.
score ≥1, which would typically trigger the use of oral anticoagulation if AF was identified [25–27]. Hypertension was the most common cardiovascular condition found in patients with SA, and both the prevalence of hypertension and the absolute level of systolic blood pressure were higher among patients with severe SA. SA is associated with increased risk of stroke in middle-aged and older adults, particularly men [1,2]. The Sleep Heart Health Study (SHHS) [2] reported that men with moderate to severe sleep apnea were nearly three times more likely to have a stroke than men without sleep apnea or with mild sleep apnea. In women, however, the increased risk of stroke was significant only with severe levels of sleep apnea [2]. Cardiac arrhythmias also seem to be more frequent in people with SA and are most commonly observed in patients with a greater number of apneic episodes and more severe hypoxemia [28–30]. The Sleep Heart Health Study has shown that patients with severe SA (AHI N 30 events/h sleep) have a four-fold increased risk of occurrence of AF compared to matched controls without SA [31]. Although SA increases the risk of both stroke and AF, it remains to be clarified what proportion of this increased stroke risk is mediated by AF compared to other stroke risk factors such as hypertension. Our study is consistent with earlier work showing that patients with SA have a high prevalence of conditions that are associated with both AF and stroke, including obesity and hypertension. Data obtained by Framingham [32] and the Danish Diet, Cancer and Health Study [34] examining independent predictors of AF have highlighted obesity as an important marker. The risk of AF raises by about 8% for each body mass index (BMI) unit increase [33,34]. The population assessed in our study showed a very high prevalence of obesity, which was higher among patients with severe SA. However, it appears that obesity predicts AF independent of SA [30,31], suggesting that other mechanisms are also important. The causal relationship between SA and AF is strengthened by studies which demonstrate a reduction in AF with continuous positive airway pressure CPAP treatment for SA [29]. Treatment of SA with tracheostomy has also been shown to reduce nocturnal AF in patients with severe SA and nocturnal paroxysms of AF [35]. Recently, CPAP treatment for severe SA during 4–6 weeks induced reversion of electrical atrial remodeling as measured by signal P-wave analysis. This may imply an improvement of the AF electrical substrate [36]. Kanalaga et al. [18] have shown that treating SA with CPAP reduces the risk of AF recurrence after electrical cardioversion.
Finally, a decrease in arrhythmias was noted after bariatric surgery in morbidly obese patients with SA [16]. Although the evidence that SA treatment can prevent AF is encouraging, it is based on a modest number of studies, few of which were randomized. Moreover, there are no corresponding data indicating that sleep apnea treatment reduces stroke, which would require much larger clinical trials. 8. Limitations We only used the CHADS2 score, possibly leading to an underestimation of low-risk patients. All patients were consecutively enrolled from a Sleep Disorder clinic, maybe leading to a sub-selection of patients at higher risk of presenting risk factors for AF and stroke. 9. Conclusions Patients with SA have a significant prevalence of risk factors for AF and stroke that increases with the severity of SA. Identification of AF in patients with SA is important, as it would lead to increasing use of anticoagulants in this population. Acknowledgment We adhere to the STROBE statement [37]. References [1] Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002;165:1217–39. [2] Nieto FJ, Young TB, Lind BK, et al. Association of sleep disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. JAMA 2000;283(14):1829–36. [3] Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000;342:1378–84. [4] Lavie Pk, Here P, Hoffstein V. Obstructive sleep apnea syndrome as a risk factor for hypertension: population study. BMJ 2000;320:479–82. [5] Katzmarzyk PT, Mason C. Prevalence of class I, II, and III obesity in Canada. CMAJ 2006;174:156–7. [6] Geiss LS, Pan L, Cadwell B, Gregg EW, Benjamin SM, Engelgau MM. Changes in incidence of diabetes in US adults, 1997–2003. Am J Prev Med 2006;30:371–7. [7] Banno K, Kryger MH. Sleep apnea: clinical investigations in humans. Sleep Med 2007;8:400–26. [8] Tarasiuk A, Greenberg-Dotan S, Simon-Tuval T, Oksenberg A, Reuveni H. The effect of obstructive sleep apnea on morbidity and health care utilization of middle-aged and older adults. J Am Geriatr Soc 2008;56:247–54. [9] Baranchuk A, Simpson CS, Redfearn DP, Fitzpatrick M. It's time to wake up! Sleep apnea & cardiac arrhythmias. Europace 2008;10(6):666–7.
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M.C. Valenza et al. / International Journal of Cardiology 174 (2014) 73–76
[10] Baranchuk A, Simpson CS, Redfearn DP, Michael KA, Fitzpatrick M. Understanding the association between sleep apnea & cardiac arrhythmias. REA 2008;1:5–6. [11] Arzt M, Young T, Finn L, Skatrud JB, Bradley TD. Association of sleep disordered breathing and the occurrence of stroke. Am J Respir Crit Care Med 2005;172:1447–51. [12] Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea–hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 2005;365:1046–53. [13] Roche F, Xuong AN, Court-Fortune, et al. Relationship among the severity of sleep apnea syndrome, cardiac arrhythmias, and autonomic imbalance. Pacing Clin Electrophysiol 2003;26:669–77. [14] Niroumand M, Kuperstein R, Sasson Z, Hanly PJ. Impact of obstructive sleep apnea on left ventricular mass and diastolic function. Am J Respir Crit Care Med 2001;163:1632–6. [15] Healey JS, Connolly SJ, Gold MR, et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med 2012;366(2):120–9. [16] Yazdan-Ashoori P, Baranchuk A. Obstructive sleep apnea may increase the risk of stroke in AF patients: refining the CHADS2 score. Int J Cardiol 2011;146:131–3. [17] Gami AS, Pressman G, Caples SM, et al. Association of atrial fibrillation and obstructive sleep apnea. Circulation 2004;110:364–7. [18] Kanagala R, Murali NS, Friedman PA, et al. Obstructive sleep apnea and the recurrence of atrial fibrillation. Circulation 2003;107:2589–94. [19] Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP. Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med 1999;131:485–91. [20] Johns MW. Daytime sleepiness, snoring, and obstructive sleep apnea: the Epworth Sleepiness Scale. Chest 1993;103:30–6. [21] Mohsenin V, Valor R. Sleep apnea in patients with hemispheric stroke. Arch Phys Med Rehabil 1995;76:71–6. [22] Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research: the Report of an American Academy of Sleep Medicine Task Force. Sleep 1999;22:667–89. [23] Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Los Angeles: UCLA Brain Information Service/Brain Research Institute; 1968. [24] Meoli AL, Casey KR, Clark RW, et al. Hypopnea in sleep-disordered breathing in adults. Sleep 2001;24:469–70.
[25] Camm AJ, Kirchhof P, Lip GYH, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010;31:2369–429. [26] Wann LS, Curtis AB, January CT, et al. ACCF/AHA/HRS focused update on the management of patients with atrial fibrillation (updating the 2006 guideline). Heart Rhythm 2011;8:157–76. [27] Incidence of atrial fibrillation in patients with severe obstructive sleep apnea: The Reveal XT-SA study. http://clinicaltrials.gov/ct2/show/NCT01058551. [Accessed on 2/2/2012]. [28] Koehler U, Schafer H. Is obstructive sleep apnea (OSA) a risk factor for myocardial infarction and cardiac arrhythmias in patients with coronary heart disease (CHD)? Sleep 1996;19:283–6. [29] Liston R, Deegan PC, McCreery C, McNicholas WT. Role of respiratory sleep disorders in the pathogenesis of nocturnal angina and arrhythmias. Postgrad Med J 1994;70:275–80. [30] Ryan CM, Usui K, Floras JS, Bradley TD. Effect of continuous positive airway pressure on ventricular ectopy in heart failure patients with obstructive sleep apnoea. Thorax 2005;60:781–5. [31] Mehra R, Benjamin EJ, Shahar E, et al. Association of nocturnal arrhythmias with sleep-disordered breathing: The Sleep Heart Health Study. Am J Respir Crit Care Med 2006;173:910–6. [32] Wang TJ, Parise H, Levy D, et al. Obesity and the risk of new-onset atrial fibrillation. JAMA 2004;292:2471–7. [33] Frost L, Hune LJ, Vestergaard P. Overweight and obesity as risk factors for atrial fibrillation or flutter: the Danish Diet, Cancer, and Health. Study Am J Med 2005;118:489–95. [34] Dublin S, French B, Glazer NL, et al. Risk of new-onset atrial fibrillation in relation to body mass index. Arch Intern Med 2006;166:2322–8. [35] Guilleminault C, Conolly SJ, Winkle RA. Cardiac arrhythmia and conduction disturbances during sleep in 400 patients with sleep apnea syndrome. Am J Cardiol 1983;52:490–4. [36] Baranchuk A, Pang H, Seaborn GEJ, et al. Reverse atrial electrical remodelling induced by continuous positive airway pressure in patients with severe obstructive sleep apnea. J Interv Card Electrophysiol 2013;36:247–53. [37] von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 2008;61(4):344–9.