Next Article in Journal
Neoadjuvant Androgen Receptor Signaling Inhibitors before Radical Prostatectomy for Non-Metastatic Advanced Prostate Cancer: A Systematic Review
Next Article in Special Issue
Recurrent/Subsequent Stroke and Associated Outcomes in Geriatric Patients with OSA and Prior Stroke Events: A Retrospective Study Using the 2019 National Inpatient Sample
Previous Article in Journal
Efficacy and Safety of Distal Radial Access for Transcatheter Arterial Chemoembolization (TACE) of the Liver
Previous Article in Special Issue
Drug-Induced Sleep Endoscopy Findings and Hypoglossal Nerve Stimulation Therapy Outcomes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 13
Issue 4
10.3390/jpm13040642
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Usefulness of Questionnaires in Assessing the Risk of Obstructive Sleep Apnea in Patients in the Managed Care after Acute Myocardial Infarction Program—The Results of a Cross-Sectional Study

by
Danuta Łoboda
1,2,*,
Michalina Stepanik
3,
Anna Szajerska-Kurasiewicz
2,
Maciej Turski
4,
Dagmara Urbanczyk-Świć
4,
Jacek Durmała
3 and
Krzysztof S. Gołba
1,2
1
Department of Electrocardiology and Heart Failure, Medical University of Silesia in Katowice, 40-635 Katowice, Poland
2
Department of Electrocardiology, Upper-Silesian Medical Centre, 40-635 Katowice, Poland
3
Department of Rehabilitation, Medical University of Silesia in Katowice, 40-635 Katowice, Poland
4
Day Treatment Cardiac Rehabilitation Ward, Upper-Silesian Medical Centre, 40-635 Katowice, Poland
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2023, 13(4), 642; https://doi.org/10.3390/jpm13040642
Submission received: 20 March 2023 / Revised: 2 April 2023 / Accepted: 4 April 2023 / Published: 7 April 2023
(This article belongs to the Special Issue Obstructive Sleep Apnea (OSA): Pathogenesis, Symptoms and Treatments)
Review Reports Versions Notes

Abstract

:
Obstructive sleep apnea (OSA) worsens prognosis after myocardial infarction (MI) but often remains undiagnosed. The study aimed to evaluate the usefulness of questionnaires in assessing the risk of OSA in patients participating in managed care after an acute myocardial infarction program. Study group: 438 patients (349 (79.7%) men) aged 59.92 ± 10.92, hospitalized in the day treatment cardiac rehabilitation department 7–28 days after MI. OSA risk assessment: A 4-variable screening tool (4-V), STOP-BANG questionnaire, Epworth sleepiness scale (ESS), and adjusted neck circumference (ANC). The home sleep apnea testing (HSAT) was performed on 275 participants. Based on four scales, a high risk of OSA was found in 283 (64.6%) responders, including 248 (56.6%) based on STOP-BANG, 163 (37.5%) based on ANC, 115 (26.3%) based on 4-V, and 45 (10.3%) based on ESS. OSA was confirmed in 186 (68.0%) participants: mild in 85 (30.9%), moderate in 53 (19.3%), and severe in 48 (17.5%). The questionnaires’ sensitivity and specificity in predicting moderate-to-severe OSA were: for STOP-BANG—79.21% (95% confidence interval; CI 70.0–86.6) and 35.67% (95% CI 28.2–43.7); ANC—61.39% (95% CI 51.2–70.9) and 61.15% (95% CI 53.1–68.8); 4-V—45.54% (95% CI 35.6–55.8) and 68.79% (95% CI 60.9–75.9); ESS—16.83% (95% CI 10.1–25.6) and 87.90% (95% CI 81.7–92.6). OSA is common in post-MI patients. The ANC most accurately estimates the risk of OSA eligible for positive airway pressure therapy. The sensitivity of the ESS in the post-MI population is insufficient and limits this scale’s usefulness in risk assessment and qualification for treatment.

1. Introduction

Untreated obstructive sleep apnea (OSA) worsens the prognosis of patients with cardiovascular (CV) diseases by multiplying the risk of developing coronary artery disease (CAD), heart failure, ventricular and supraventricular arrhythmias, resistant hypertension, stroke, and metabolic dysfunction [1,2,3,4,5,6,7,8,9]. OSA significantly increases the risk of first and subsequent coronary events and CV death after myocardial infarction (MI), and the risk of major adverse cardiovascular events after percutaneous coronary interventions [9,10,11,12]. OSA increases pulmonary morbidity and prolongs hospitalization after coronary artery bypass grafting [13]. The presence of OSA in post-MI patients is associated with adverse left ventricular (LV) remodeling, systolic LV dysfunction, and an increased risk of ventricular arrhythmias and sudden cardiac death [5,14]. Resistant hypertension in OSA leads to LV hypertrophy, LV diastolic dysfunction, and episodes of atrial fibrillation [15], which impairs patients’ exercise capacity and increases the risk of complications associated with combined antiplatelet and antithrombotic therapy [16]. OSA, arterial hypertension, chronic kidney disease, chronic obstructive pulmonary disease, and primarily, obesity with concomitant metabolic disorders or diabetes contribute to the development of heart failure with preserved ejection fraction with coronary microvascular dysfunction, myopathy, and atrial and ventricular fibrosis [17,18]. It is known that OSA reduces exercise tolerance [19,20], which may affect the effectiveness of cardiac rehabilitation (CR) after MI. Moreover, sleep fragmentation in OSA patients is associated with many troublesome symptoms, such as daytime sleepiness, irritability, memory deterioration, and depression, thus worsening the quality of life [21] and compliance in post-MI patients.
OSA-related CV diseases’ basic mechanisms are: (1) intermittent hypoxia/reoxygenation, oxidative stress, and inflammation affecting endothelial function, and participate in plaque instability/plaque susceptibility and arterial stiffness [8,22,23]; (2) metabolic dysfunction with insulin resistance accelerating coronary microvascular dysfunction and coronary and peripheral atherosclerosis [6,18,23]; (3) negative intrathoracic pressure during respiration with closed airways with a repetitive increase in LV wall tension and subsequent septal hypertrophy/LV concentric hypertrophy, or LV remodeling with chamber dilatation [14,15,17,24,25]; (4) impaired coronary perfusion during increased LV transmural pressure, increased afterload, and OSA-related hypoxemia, resulting in nocturnal myocardial ischemia [25,26]; and (5) activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system with peripheral vasoconstriction, sodium and water retention, and resistant hypertension [3,25,26].
Overweight/obesity, high blood pressure, diabetes, hyperlipidemia, and smoking—the main risk factors for OSA and CAD, including the male sex, age over 35—are similar [27]. Despite this, many patients with MI remain undiagnosed with OSA [7,28], whose gold standard for diagnosis is polysomnography [29]. However, implementing simple OSA risk questionnaires into routine cardiac diagnostics may improve cardiologists’ cooperation with sleep or respiratory physicians in tackling a common threat [30].
The study aimed to evaluate questionnaires’ usefulness in estimating the risk of moderate-to-severe OSA in patients participating in the Polish Cardiac Society, the National Health Fund, and the Ministry of Health comprehensive managed care after acute myocardial infarction program (MC-AMI, KOS-ZAWAL).

2. Materials and Methods

2.1. Study Design and Patients

The study was cross-sectional and included consecutive patients participating in the MC-AMI program who were hospitalized in the Day Treatment Cardiac Rehabilitation Department of the Upper-Silesian Medical Centre (Katowice, Poland). The inclusion criteria for the study was a history of MI in the 7 to 28 days before admission and were consistent with the inclusion criteria for the MC-AMI program, described in detail elsewhere [31,32]. In brief, the MC-AMI program covers all patients hospitalized for acute MI (both ST-elevation MI and non-ST-elevation MI) diagnosed in line with the fourth universal definition of myocardial infarction [33]. The MC-AMI program provides treatment in four treatment modules: Module I—the treatment of the acute phase of MI (i.e., angioplasty, arterial bypass grafting, follow-up visit within 14 days after discharge); Module II—cardiac rehabilitation; Module III—electrotherapy (i.e., implantation of an implantable cardiac defibrillator in a primary prevention of sudden cardiac death, if necessary); and Module IV—specialized cardiac care during the 12 months following MI. The study exclusion criterion was the current treatment of OSA with positive airway pressure or intraoral devices.
We estimated the prevalence of OSA risk factors based on anthropometric measurements, medical history and scores on OSA risk scales in the entire study group and subgroups according to sex and the severity of OSA as determined by the home sleep apnea test (HSAT, portable level 3 sleep test). Then, we assessed the sensitivity, specificity, and positive and negative predictive values of the questionnaires in estimating the risk of moderate-to-severe OSA. Patients with HSAT-proven central or mixed sleep apnea were excluded from this analysis.

2.2. OSA Risk Assessment

The risk of OSA was assessed as a part of the routine medical history with regard to hospital admissions by standardized risk scales or questionnaires [29,34,35,36,37,38]: the STOP-BANG, the 4-variable screening tool (4-V), the Epworth sleepiness scale (ESS), and the adjusted neck circumference (ANC), as shown in Table 1. The following results indicated a high risk of OSA: (1) STOP-BANG ≥ 5 points or ≥2 points (snoring, tiredness, observed apnea, or high blood pressure) plus male sex, or body mass index > 35 kg/m2, or neck circumference > 40 cm; (2) 4-V > 13 points; (3) ESS > 10 points; and (4) ANC > 48 cm.

2.3. Polysomnography Evaluation

All participants who provided informed consent for further evaluation—irrespective of the estimated OSA risk—underwent the HSAT, using an Alice NightOne (Philips Respironics) device [29]. The HSAT recordings were analyzed following the recommendations of the American Academy of Sleep Medicine [29,39]. Sleep apnea was defined as the airflow’s complete cessation or reduction by ≥90% for ≥10 s through the respiratory tract. Apnea episodes with and without respiratory chest movement were classified as OSA and central sleep apnea, respectively. Mixed apnea was defined as episodes without respiratory effort and airflow in the first part of the event and with respiratory effort but without airflow in the last part of the event. Reduction in airflow by ≥30% for ≥10 s and leading to a ≥4% decrease in hemoglobin oxygen saturation (SpO2) was classified as hypopnea. Sleep-disordered breathing (SDB) was diagnosed if the rate of apnea or hypopnea per hour of recording (respiratory event index (REI)) exceeded four. SDB with REI of 5–14 events per hour (events/h) were classified as mild, 15–30 events/h as moderate, and >30 events/h as severe.

2.4. Statistical Analysis

The results were analyzed using MedCalc Version 20.106 (MedCalc Software Ltd., Ostend, Belgium). Depending on the normality of the distribution assessed by the Kolmogorov–Smirnov test, the quantitative parameters were presented as the arithmetic mean and standard deviation (SD) or the median and interquartile range (IQR). Qualitative data were expressed as numbers and percentages. Differences in the frequency and size of anthropometric and HSAT parameters by OSA severity and sex were calculated using the chi-square test, the student’s t-test for independent variables, or the Mann–Whitney U test, as required. Differences in the frequency and size of risk factors by OSA severity and sex were calculated using the chi-square test with the Cochran–Armitage test for trend or the Kruskal–Wallis test with the Jonckheere–Terpstra test for trend. The sensitivity, specificity, and positive and negative predictive values of the OSA questionnaires in the study group were calculated. The accuracy was calculated from the formula: the number of true positive cases (correctly identified as patient) plus the number of true negative cases (correctly identified as healthy) and divided by the number of all participants, expressed in percentages. A p-value less than 0.05 was considered statistically significant.

2.5. Ethical Considerations

The study was approved by the Bioethical Committee of the Medical University of Silesia in Katowice, Poland. Informed consent was obtained from all subjects involved in the study.

3. Results

During the study, 472 patients (Caucasians aged > 18 years) participated in CR. We collected complete medical documentation from 438 patients, including 349 (79.7%) men who ultimately constituted the study group. The mean age of participants was 59.92 ± 10.92 years. The anthropometric parameters and comorbidities of the study group are presented in Table 2.
In the study group of 438 patients, we assessed the OSA risk based on questionnaires. The high risk of OSA was calculated: in 248 (56.6%) participants (211 (48.2%) men and 37 (8.4%) women, p < 0.001) based on the STOP-BANG; in 163 (37.5%) participants (149 (34.3%) men and 14 (3.2%) women, p < 0.001) based on the ANC; in 115 (26.3%) participants (113 (25.8%) men and 2 (0.5%) women, p < 0.001) based on the 4-V; and in 45 (10.3%) participants (36 (8.2%) men and 9 (2.1%) women, p = 0.955) based on the ESS. Considering the results of all four questionnaires, a high risk of OSA was in 283 (64.6%) surveyed patients—240 (68.8%) men and 43 (48.3%) women, p < 0.001.
The technically adequate HSAT was performed on 275 patients—238 men and 37 women—who provided informed consent for further evaluation, irrespective of the estimated OSA risk. A total of 72 (26.2%) patients were not diagnosed with SDB. OSA was confirmed in 186 (68.0%) respondents; it was mild in 74 (31.1%) men and 11 (29.7%) women, moderate in 50 (21.0%) men and 3 (8.1%) women, and severe in 45 (18.9%) men and 3 (8.1%) women. In the study group, there were also 14 (5.1%) patients with central sleep apnea and 3 (1.1%) with mixed sleep apnea. We excluded these 17 patients from further analysis. Table 3 presents the characteristics of the HSAT parameters of patients with OSA according to sex.
In the group with moderate OSA, the median REI was 19.90 (IQR 17.25–24.08) events/h, while the percentage of total sleep time with oxyhemoglobin saturation below 90%, mean, and minimum SpO2 during sleep was 2.67% (0.71–8.59), 92.95 ± 1.73%, and 83.36 ± 5.54%, respectively. On the other hand, in the group with severe OSA, the median REI was 41.40 (33.90–57.10) events/h, the percentage of total sleep time with oxyhemoglobin saturation below 90% was 7.16% (1.80–23.39), the mean SpO2 was 92.11 ± 2.05%, and the minimum SpO2 was 80.87 ± 6.16%.
In turn, Table 4 shows the prevalence of risk factors and the score on the risk scales in the entire study group and in the subgroups, depending on the severity of OSA determined based on HSAT and sex.
The male-to-female prevalence ratio for OSA was 1.5:1. In men, the prevalence of risk factors such as age > 50 years, BMI > 35 kg/m2, high neck circumference, high blood pressure, snoring, and observed sleep apnea episodes increased proportionally to the severity of OSA. In women, this only concerned the prevalence of large neck circumference, BMI > 35 kg/m2, and awakenings from sleep. On the other hand, the value of the STOP-BANG and the ANC scores increased proportionally to the severity of OSA in both sexes, while the value of 4-V increased in this manner only in men. Tiredness/sleepiness, both analyzed as risk factors or counted as a score on the ESS, did not correlate with the severity of OSA. Tiredness/sleepiness were significantly more often reported by women than by men, p < 0.001.
The sensitivity, specificity, and positive and negative predictive values of the questionnaires were assessed correspondingly for both sexes due to the small size of the female subgroup. The results are presented in Table 5.

4. Discussion

In the presented analysis, the authors confirmed the high prevalence of OSA risk factors in middle-aged and older patients after MI participated in ambulatory CR. That prevalence is reflected in the risk questionnaire results (high risk of OSA in 64.6% of surveyed) and confirmed in the HSAT (36.7% of patients with moderate-to-severe OSA). The questionnaires based on a combination of symptoms and risk factors, in particular the STOP-BANG and the ANC, were more sensitive in predicting moderate-to-severe OSA. The ESS, based solely on assessing pathological daytime sleepiness, was the least sensitive. The specificity of the ANC and the 4-V exceeded 60% and were much higher than that of the STOP-BANG. The ANC had both sensitivity and specificity exceeding 60% in predicting moderate-to-severe OSA and was the only scale with a positive predictive value exceeding 50%. Thus, the ANC seems to be the most useful in post-MI patients in clinical practice.
In Poland, the prevalence of CAD is about 4.0% higher than the European Union average [40,41]. The mortality rate of patients with MI is 8.4% during hospitalization and another 9.8% in the one-year follow-up after discharge from the hospital [42]. Intensive secondary prevention focused on lifestyle changes, CR, and the reduction in all known CV risk factors reduces the risk of subsequent CV events [43,44,45]. On the other hand, the prevalence of moderate-to-severe OSA exceeds 15 to 35% among middle-aged and older adults in the general population (17.8% in Poland) [1,46] and 35% to 65% in post-MI patients [1,47,48,49]. In our group of patients from the MC-AMI program, moderate-to-severe OSA was found in 36.7% of respondents. As recommended by scientific societies [1,50], treatment of all patients with OSA and CAD should be considered. Treatment includes lifestyle changes, weight loss, and positive airway pressure therapy. Therefore, performing screening tests such as a targeted medical history, risk questionnaires, or using sleep apnea screening devices in the post-MI patient referred for CR seems particularly justified [1,50].
The OSA risk questionnaires have been validated in the general population for men and women. These scales’ sensitivity and specificity (REI ≥ 15 events/h) have been assessed as 87% and 43% for the STOP-BANG; 63% and 72% for the ANC; 24% and 93% for the 4-V; and 39% and 71% for the ESS [29,34,35,36,37,38]. These questionnaires are characterized by high accuracy in the general population, while in the cardiac population, the sensitivity and specificity of some of them are much lower. Hwang et al. [51], in a literature search of 1894 patients with CV risk factors, revealed the STOP-BANG specificity for moderate-to-severe OSA to be 22.5%, resulting in a high rate of false positives. An even lower STOP-BANG specificity of 13.0% was reported by Reuter et al. [52] and Nunes et al. [53]. On the other hand, the sensitivity of the ESS (probably the most widely known SDB risk scale in Poland) seems insufficient, which limits this scale’s usefulness in the risk assessment and qualification for OSA treatment in post-MI population. Hupin et al. [28] and Glantz et al. [49], in a revascularized CAD cohort, found the absence of typical daily OSA symptoms in 61.8–65.0% of patients. The low level of daytime sleepiness in the population with CV diseases is also reported by other authors [52,53]. In our sample, 36.7% of patients, mainly men, had moderate-to-severe OSA, and only 16.8% with such a risk scored more than ten points on the ESS. Conversely, excessive sleepiness in patients already diagnosed with OSA is considered to be a factor in increasing the risk of CV events [54]. The 4-V scale’s sensitivity, primarily validated for Japan’s population by Takegami et al. [38], is lower for Europe [35] and the United States [34], with high specificity maintained. In our sample from the Polish post-MI group, its sensitivity reached only 45.54%. There is a lack of literature on the accuracy of ANC in the post-MI patient. However, based on our study, we believe that this scale, with a sensitivity and specificity of 61%, may help identify moderate-to-severe OSA in such patients.
Another problem may be in underestimating the risk of OSA in women [55,56]. In most epidemiological reports, the prevalence of OSA is higher in men than in women, with a male-to-female ratio of approximately 1.5:1 to 3:1 [55,56]. These statistics may be an underestimate due to less specific OSA symptoms reported by women (fatigue, morning headache, and difficulty initiating sleep rather than snoring and apnea) [21,56,57,58]. Furthermore, the OSA risk calculated based on risk scales is usually higher for men, as it assumes additional points for the male sex (STOP-BANG, 4-V) [36,38] or determines the same threshold of pathological neck circumference for both sexes (STOP-BANG, ANC) [36,37], which may translate into a lower sensitivity but higher specificity of some risk scales, e.g., STOP-BANG in women [59]. Some authors report [60] that anthropometric measurements, such as the neck, waist, and hip circumferences, correlate less well with the severity of OSA in male patients than in females. In our group, OSA’s prevalence was only 1.5 times higher in men than women. The small difference between men and women may be due to the higher incidence of OSA in women post-MI than in the general population and a higher percentage of diagnoses in women without typical symptoms, the absence of which did not limit HSAT’s performance in our study. Unfortunately, the female group in the studied population was too small to assess the sensitivity and specificity of risk questionnaires separately for both sexes.
Last but not least, it is worth remembering that post-MI patients are cared for by cardiologists who rarely deal with SDB in their daily practice. The user-friendly and not time-consuming risk scales can encourage OSA diagnostics in cardiological outpatient and inpatient clinics or during early CR after MI. Therefore, it is worth considering ANC, which can be performed at the patient’s bedside by counting four simple parameters. Both the sensitivity and specificity of this scale for the studied population were high enough and totaled to 61%. Patients with CV diseases and a high risk of OSA should be referred for further diagnostics [1] at the sleep laboratory (reimbursed service in Poland) or for HSAT, provided that its availability improves (the outpatient service is not currently reimbursed by the National Health Fund, the obligatory health insurer in Poland).

Study Limitations

Risk scales and questionnaires analyzed in our study are the only auxiliary tool in SDB diagnosis [1,29]. HSAT was used to diagnose OSA instead of the gold standard, the level 1 polysomnography performed in the sleep laboratory [29]. However, HSAT is often employed in research for examining the relationship between OSA and differential CV diseases [61,62,63,64] and is recommended for screening in CR [50]. We repeated or rejected all inconclusive or technically inadequate results to ensure the credibility of the research.
Due to the lower prevalence of CAD and OSA in women, the female group in the studied population was relatively small, which could affect the statistical significance of the analysis. Similarly, this group was too small to assess the sensitivity and specificity of risk questionnaires separately for both sexes.
Patients with symptomatic heart failure and significantly reduced left ventricular ejection fraction are not eligible for outpatient CR, hence the low percentage of patients with central sleep apnea in the study sample. However, such pre-selection in the case of OSA risk assessment by HSAT is an asset rather than a limitation of the study. Likewise, patients with severe or multiple comorbidities were not referred to the day treatment rehabilitation ward but the stationary CR ward. Thus, the patients with severe chronic obstructive pulmonary disease, heart failure in NYHA III/IV class, tachyarrhythmias, etc., may have been underrepresented in the study group. According to data from the SILesian CARDiovascular (SILCARD) registry [32], in 2017–2018 in the Silesian Voivodeship, Poland, 36% of MC-AMI patients participated in ambulatory CR.
The presented results are not derived from a clinical trial, and no clinical outcomes related to the frequency of OSA diagnoses have been studied.

5. Conclusions

OSA is a common CV risk factor among post-MI patients. The ANC, with a sensitivity and specificity of 61% in predicting moderate-to-severe OSA, appears to be the most helpful risk questionnaire in clinical practice. The ESS sensitivity of only 16.83% is insufficient to assess the risk of OSA in the post-MI patient population.

Author Contributions

Conceptualization, D.Ł. and K.S.G.; methodology, D.Ł. and K.S.G.; software, D.Ł.; validation, D.Ł. and K.S.G.; formal analysis, D.Ł., M.S., A.S.-K. and K.S.G.; investigation, D.Ł., M.S., A.S.-K., M.T. and D.U.-Ś.; resources, M.T. and D.U.-Ś.; data curation, D.Ł.; writing—original draft preparation, D.Ł.; writing—review and editing, D.Ł., M.S., J.D. and K.S.G.; visualization, D.Ł.; supervision, K.S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethical Committee of the Medical University of Silesia in Katowice, Poland (Resolution No. KNW/0022/KB1/77/18 of 25 September 2018 and PCN 0022/KB1/77/I/18/20 of 13 October 2020). Informed consent was obtained from all subjects involved in the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the Department of Electrocardiology and Heart Failure, Medical University of Silesia in Katowice (Poland). The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yeghiazarians, Y.; Jneid, H.; Tietjens, J.R.; Redline, S.; Brown, D.L.; El-Sherif, N.; Mehra, R.; Bozkurt, B.; Ndumele, C.E.; Somers, V.K. Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 2021, 144, e56–e67. [Google Scholar] [CrossRef] [PubMed]
  2. Trzepizur, W.; Blanchard, M.; Ganem, T.; Balusson, F.; Feuilloy, M.; Girault, J.M.; Meslier, N.; Oger, E.; Paris, A.; Pigeanne, T.; et al. Sleep Apnea-Specific Hypoxic Burden, Symptom Subtypes, and Risk of Cardiovascular Events and All-Cause Mortality. Am. J. Respir. Crit. Care Med. 2022, 205, 108–117. [Google Scholar] [CrossRef] [PubMed]
  3. Javaheri, S.; Barbe, F.; Campos-Rodriguez, F.; Dempsey, J.A.; Khayat, R.; Javaheri, S.; Malhotra, A.; Martinez-Garcia, M.A.; Mehra, R.; Pack, A.I.; et al. Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular Consequences. J. Am. Coll. Cardiol. 2017, 69, 841–858. [Google Scholar] [CrossRef] [PubMed]
  4. Mehra, R.; Benjamin, E.J.; Shahar, E.; Gottlieb, D.J.; Nawabit, R.; Kirchner, H.L.; Sahadevan, J.; Redline, S.; Sleep Heart Health Study. Association of nocturnal arrhythmias with sleep-disordered breathing: The Sleep Heart Health Study. Am. J. Respir. Crit. Care Med. 2006, 173, 910–916. [Google Scholar] [CrossRef] [Green Version]
  5. Gami, A.S.; Olson, E.J.; Shen, W.K.; Wright, R.S.; Ballman, K.V.; Hodge, D.O.; Herges, R.M.; Howard, D.E.; Somers, V.K. Obstructive sleep apnea and the risk of sudden cardiac death: A longitudinal study of 10,701 adults. J. Am. Coll. Cardiol. 2013, 62, 610–616. [Google Scholar] [CrossRef] [Green Version]
  6. Zhao, X.; Li, X.; Xu, H.; Qian, Y.; Fang, F.; Yi, H.; Guan, J.; Yin, S.K. Relationships between cardiometabolic disorders and obstructive sleep apnea: Implications for cardiovascular disease risk. J. Clin. Hypertens. 2019, 21, 280–290. [Google Scholar] [CrossRef] [Green Version]
  7. Strausz, S.; Havulinna, A.S.; Tuomi, T.; Bachour, A.; Groop, L.; Mäkitie, A.; Koskinen, S.; Salomaa, V.; Palotie, A.; Ripatti, S.; et al. Obstructive sleep apnoea and the risk for coronary heart disease and type 2 diabetes: A longitudinal population-based study in Finland. BMJ Open 2018, 8, e022752. [Google Scholar] [CrossRef] [PubMed]
  8. Chen, L.; Zou, S.; Wang, J. Association of Obstructive Sleep Apnea Syndrome (OSA/OSAHS) with Coronary Atherosclerosis Risk: Systematic Review and Meta-Analysis. Comput. Math. Methods Med. 2022, 2022, 8905736. [Google Scholar] [CrossRef]
  9. Hla, K.M.; Young, T.; Hagen, E.W.; Stein, J.H.; Finn, L.A.; Nieto, F.J.; Peppard, P.E. Coronary heart disease incidence in sleep disordered breathing: The Wisconsin Sleep Cohort Study. Sleep 2015, 38, 677–684. [Google Scholar] [CrossRef] [Green Version]
  10. Xie, J.; Sert Kuniyoshi, F.H.; Covassin, N.; Singh, P.; Gami, A.S.; Wang, S.; Chahal, C.A.; Wei, Y.; Somers, V.K. Nocturnal Hypoxemia Due to Obstructive Sleep Apnea Is an Independent Predictor of Poor Prognosis After Myocardial Infarction. J. Am. Heart Assoc. 2016, 5, e003162. [Google Scholar] [CrossRef] [Green Version]
  11. Wang, X.; Fan, J.Y.; Zhang, Y.; Nie, S.P.; Wei, Y.X. Association of obstructive sleep apnea with cardiovascular outcomes after percutaneous coronary intervention: A systematic review and meta-analysis. Medicine 2018, 97, e0621. [Google Scholar] [CrossRef] [PubMed]
  12. Nakashima, H.; Kurobe, M.; Minami, K.; Furudono, S.; Uchida, Y.; Amenomori, K.; Nunohiro, T.; Takeshita, S.; Maemura, K. Effects of moderate-to-severe obstructive sleep apnea on the clinical manifestations of plaque vulnerability and the progression of coronary atherosclerosis in patients with acute coronary syndrome. Eur. Heart J. Acute Cardiovasc. Care 2015, 4, 75–84. [Google Scholar] [CrossRef] [PubMed]
  13. Bhama, J.K.; Spagnolo, S.; Alexander, E.P.; Greenberg, M.; Trachiotis, G.D. Coronary revascularization in patients with obstructive sleep apnea syndrome. Heart Surg. Forum 2006, 9, E813–E817. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Fisser, C.; Götz, K.; Hetzenecker, A.; Debl, K.; Zeman, F.; Hamer, O.W.; Poschenrieder, F.; Fellner, C.; Stadler, S.; Maier, L.S.; et al. Obstructive sleep apnoea but not central sleep apnoea is associated with left ventricular remodelling after acute myocardial infarction. Clin. Res. Cardiol. 2021, 110, 971–982. [Google Scholar] [CrossRef]
  15. Saeed, S.; Romarheim, A.; Solheim, E.; Bjorvatn, B.; Lehmann, S. Cardiovascular remodeling in obstructive sleep apnea: Focus on arterial stiffness, left ventricular geometry and atrial fibrillation. Expert Rev. Cardiovasc. Ther. 2022, 20, 455–464. [Google Scholar] [CrossRef]
  16. Wang, W.; Huang, Q.; Pan, D.; Zheng, W.; Zheng, S. The optimal duration of triple antithrombotic therapy in patients with atrial fibrillation and acute coronary syndrome or undergoing percutaneous coronary intervention: A network meta-analysis of randomized clinical trials. Int. J. Cardiol. 2022, 357, 33–38. [Google Scholar] [CrossRef]
  17. Sanderson, J.E.; Fang, F.; Lu, M.; Ma, C.Y.; Wei, Y.X. Obstructive sleep apnoea, intermittent hypoxia and heart failure with a preserved ejection fraction. Heart 2021, 107, 190–194. [Google Scholar] [CrossRef]
  18. Lin, Y.; Fu, S.; Yao, Y.; Li, Y.; Zhao, Y.; Luo, L. Heart failure with preserved ejection fraction based on aging and comorbidities. J. Transl. Med. 2021, 19, 291. [Google Scholar] [CrossRef]
  19. Mendelson, M.; Marillier, M.; Bailly, S.; Flore, P.; Borel, J.C.; Vivodtzev, I.; Doutreleau, S.; Tamisier, R.; Pépin, J.L.; Verges, S. Maximal exercise capacity in patients with obstructive sleep apnoea syndrome: A systematic review and meta-analysis. Eur. Respir. J. 2018, 51, 1702697. [Google Scholar] [CrossRef] [Green Version]
  20. Berger, M.; Kline, C.E.; Cepeda, F.X.; Rizzi, C.F.; Chapelle, C.; Laporte, S.; Hupin, D.; Raffin, J.; Costes, F.; Hargens, T.A.; et al. Does obstructive sleep apnea affect exercise capacity and the hemodynamic response to exercise? An individual patient data and aggregate meta-analysis. Sleep Med. Rev. 2019, 45, 42–53. [Google Scholar] [CrossRef]
  21. Lee, J.J.; Sundar, K.M. Evaluation and Management of Adults with Obstructive Sleep Apnea Syndrome. Lung 2021, 199, 87–101. [Google Scholar] [CrossRef] [PubMed]
  22. Kheirandish-Gozal, L.; Gozal, D. Obstructive Sleep Apnea and Inflammation: Proof of Concept Based on Two Illustrative Cytokines. Int. J. Mol. Sci. 2019, 20, 459. [Google Scholar] [CrossRef] [Green Version]
  23. Achim, A.; Stanek, A.; Homorodean, C.; Spinu, M.; Onea, H.L.; Lazăr, L.; Marc, M.; Ruzsa, Z.; Olinic, D.M. Approaches to Peripheral Artery Disease in Diabetes: Are There Any Differences? Int. J. Environ. Res. Public Health 2022, 19, 9801. [Google Scholar] [CrossRef] [PubMed]
  24. Alonderis, A.; Raskauskiene, N.; Gelziniene, V.; Mickuviene, N.; Brozaitiene, J. The association of sleep disordered breathing with left ventricular remodeling in CAD patients: A cross-sectional study. BMC Cardiovasc. Disord. 2017, 17, 250. [Google Scholar] [CrossRef] [Green Version]
  25. Kasai, T.; Floras, J.S.; Bradley, T.D. Sleep apnea and cardiovascular disease: A bidirectional relationship. Circulation 2012, 126, 1495–1510. [Google Scholar] [CrossRef] [Green Version]
  26. Parati, G.; Lombardi, C.; Castagna, F.; Mattaliano, P.; Filardi, P.P.; Agostoni, P.; Italian Society of Cardiology (SIC) Working Group on Heart Failure members. Heart failure and sleep disorders. Nat. Rev. Cardiol. 2016, 13, 389–403. [Google Scholar] [CrossRef] [PubMed]
  27. Mitra, A.K.; Bhuiyan, A.R.; Jones, E.A. Association and Risk Factors for Obstructive Sleep Apnea and Cardiovascular Diseases: A Systematic Review. Diseases 2021, 9, 88. [Google Scholar] [CrossRef]
  28. Hupin, D.; Pichot, V.; Berger, M.; Sforza, E.; Raffin, J.; Lietar, C.; Poyraz, E.; Maudoux, D.; Barthelemy, J.C.; Roche, F. Obstructive Sleep Apnea in Cardiac Rehabilitation Patients. J. Clin. Sleep Med. 2018, 15, 1119–1126. [Google Scholar] [CrossRef]
  29. Kapur, V.K.; Auckley, D.H.; Chowdhuri, S.; Kuhlmann, D.C.; Mehra, R.; Ramar, K.; Harrod, C.G. Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: An American Academy of Sleep Medicine clinical practice guideline. J. Clin. Sleep Med. 2017, 13, 479–504. [Google Scholar] [CrossRef]
  30. Tietjens, J.R.; Claman, D.; Kezirian, E.J.; De Marco, T.; Mirzayan, A.; Sadroonri, B.; Goldberg, A.N.; Long, C.; Gerstenfeld, E.P.; Yeghiazarians, Y. Obstructive Sleep Apnea in Cardiovascular Disease: A Review of the Literature and Proposed Multidisciplinary Clinical Management Strategy. J. Am. Heart Assoc. 2019, 8, e010440. [Google Scholar] [CrossRef] [Green Version]
  31. Ordinance of the President of the National Health Fund No. 38/2017/DSOZ on Defining the Conditions for Concluding and Implementing Contracts Related to Hospital Treatment within Comprehensive Care after Myocardial Infarction (KOS-ZAWAL), as Amended. Available online: https://www.nfz.gov.pl/zarzadzenia-prezesa/zarzadzenia-prezesa-nfz/zarzadzenie-nr-382017dsoz,6578.html (accessed on 23 January 2023). (In Polish)
  32. Wita, K.; Kułach, A.; Sikora, J.; Fluder, J.; Nowalany-Kozielska, E.; Milewski, K.; Pączek, P.; Sobocik, H.; Olender, J.; Szela, L.; et al. Managed Care after Acute Myocardial Infarction (MC-AMI) Reduces Total Mortality in 12-Month Follow-Up-Results from a Poland’s National Health Fund Program of Comprehensive Post-MI Care-A Population-Wide Analysis. J. Clin. Med. 2020, 9, 3178. [Google Scholar] [CrossRef] [PubMed]
  33. Thygesen, K.; Alpert, J.S.; Jaffe, A.S.; Chaitman, B.R.; Bax, J.J.; Morrow, D.A.; White, H.D.; Executive Group on behalf of the Joint European Society of Cardiology (ESC)/American College of Cardiology (ACC)/American Heart Association (AHA)/World Heart Federation (WHF) Task Force for the Universal Definition of Myocardial Infarction. Fourth Universal Definition of Myocardial Infarction. J. Am. Coll. Cardiol. 2018, 72, 2231–2264. [Google Scholar] [CrossRef] [PubMed]
  34. Silva, G.E.; Vana, K.D.; Goodwin, J.L.; Sherrill, D.L.; Quan, S.F. Identification of patients with sleep disordered breathing: Comparing the four-variable screening tool, STOP, STOP-Bang, and Epworth Sleepiness Scales. J. Clin. Sleep Med. 2011, 7, 467–472. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Pataka, A.; Daskalopoulou, E.; Kalamaras, G.; Fekete Passa, K.; Argyropoulou, P. Evaluation of five different questionnaires for assessing sleep apnea syndrome in a sleep clinic. Sleep Med. 2014, 15, 776–781. [Google Scholar] [CrossRef]
  36. Nicholl, D.D.; Ahmed, S.B.; Loewen, A.H.; Hemmelgarn, B.R.; Sola, D.Y.; Beecroft, J.M.; Turin, T.C.; Hanly, P.J. Diagnostic value of screening instruments for identifying obstructive sleep apnea in kidney failure. J. Clin. Sleep Med. 2013, 9, 31–38. [Google Scholar] [CrossRef] [PubMed]
  37. Pivetta, B.; Chen, L.; Nagappa, M.; Saripella, A.; Waseem, R.; Englesakis, M.; Chung, F. Use and Performance of the STOP-Bang Questionnaire for Obstructive Sleep Apnea Screening Across Geographic Regions: A Systematic Review and Meta-Analysis. JAMA Netw. Open 2021, 4, e211009. [Google Scholar] [CrossRef]
  38. Takegami, M.; Hayashino, Y.; Chin, K.; Sokejima, S.; Kadotani, H.; Akashiba, T.; Kimura, H.; Ohi, M.; Fukuhara, S. Simple four-variable screening tool for identification of patients with sleep-disordered breathing. Sleep 2009, 32, 939–948. [Google Scholar]
  39. American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification. Version 2.6. 2020. Available online: https://aasm.org/clinical-resources/scoring-manual/ (accessed on 23 January 2023).
  40. Wojtyniak, B.; Gorynski, P. Health Status of Polish Population and Its Determinants 2020; National Institute of Public Health—National Institute of Hygien: Warsaw, Poland, 2020; pp. 1–588. Available online: https://www.pzh.gov.pl/sytuacja-zdrowotna-ludnosci-polski-i-jej-uwarunkowania-raport-za-2020-rok/ (accessed on 23 January 2023).
  41. Townsend, N.; Wilson, L.; Bhatnagar, P.; Wickramasinghe, K.; Rayner, M.; Nichols, M. Cardiovascular disease in Europe: Epidemiological update 2016. Eur. Heart J. 2016, 37, 3232–3245. [Google Scholar] [CrossRef]
  42. Jankowski, P.; Topór-Mądry, R.; Gąsior, M.; Cegłowska, U.; Gierlotka, M.; Kubica, J.; Kalarus, Z.; Lesiak, M.; Wojakowski, W.; Legutko, J.; et al. Management and predictors of clinical events in 75 686 patients with acute myocardial infarction. Kardiol. Pol. 2022, 80, 468–475. [Google Scholar] [CrossRef]
  43. Kotseva, K.; De Backer, G.; De Bacquer, D.; Rydén, L.; Hoes, A.; Grobbee, D.; Maggioni, A.; Marques-Vidal, P.; Jennings, C.; EUROASPIRE Investigators; et al. Lifestyle and impact on cardiovascular risk factor control in coronary patients across 27 countries: Results from the European Society of Cardiology ESC-EORP EUROASPIRE V registry. Eur. J. Prev. Cardiol. 2019, 26, 824–835. [Google Scholar] [CrossRef]
  44. Kubielas, G.; Diakowska, D.; Uchmanowicz, I. Survival analysis of patients with acute coronary syndrome receiving comprehensive coordinated care after myocardial infarction (KOS-Zawał). Kardiol. Pol. 2022, 80, 415–421. [Google Scholar] [CrossRef] [PubMed]
  45. Jankowski, P.; Topór-Mądry, R.; Gąsior, M.; Cegłowska, U.; Eysymontt, Z.; Gierlotka, M.; Wita, K.; Legutko, J.; Dudek, D.; Sierpiński, R.; et al. Innovative Managed Care May Be Related to Improved Prognosis for Acute Myocardial Infarction Survivors. Circ. Cardiovasc. Qual. Outcomes 2021, 14, e007800. [Google Scholar] [CrossRef]
  46. Benjafield, A.V.; Ayas, N.T.; Eastwood, P.R.; Heinzer, R.; Ip, M.S.M.; Morrell, M.J.; Nunez, C.M.; Patel, S.R.; Penzel, T.; Pépin, J.L.; et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: A literature-based analysis. Lancet Respir. Med. 2019, 7, 687–698. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Le Grande, M.R.; Beauchamp, A.; Driscoll, A.; Jackson, A.C. Prevalence of obstructive sleep apnoea in acute coronary syndrome patients: Systematic review and meta-analysis. BMC Cardiovasc. Disord. 2020, 20, 147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Khokhrina, A.; Andreeva, E.; Degryse, J.M. A systematic review on the association of sleep-disordered breathing with cardiovascular pathology in adults. NPJ Prim. Care Respir. Med. 2022, 32, 41. [Google Scholar] [CrossRef]
  49. Glantz, H.; Thunström, E.; Herlitz, J.; Cederin, B.; Nasic, S.; Ejdebäck, J.; Peker, Y. Occurrence and predictors of obstructive sleep apnea in a revascularized coronary artery disease cohort. Ann. Am. Thorac. Soc. 2013, 10, 350–356. [Google Scholar] [CrossRef]
  50. Le Grande, M.R.; Neubeck, L.; Murphy, B.M.; McIvor, D.; Lynch, D.; McLean, H.; Jackson, A.C. Screening for obstructive sleep apnoea in cardiac rehabilitation: A position statement from the Australian Centre for Heart Health and the Australian cardiovascular health and rehabilitation association. Eur. J. Prev. Cardiol. 2016, 23, 1466–1475. [Google Scholar] [CrossRef]
  51. Hwang, M.; Zhang, K.; Nagappa, M.; Saripella, A.; Englesakis, M.; Chung, F. Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnoea in patients with cardiovascular risk factors: A systematic review and meta-analysis. BMJ Open Respir. Res. 2021, 8, e000848. [Google Scholar] [CrossRef]
  52. Reuter, H.; Herkenrath, S.; Treml, M.; Halbach, M.; Steven, D.; Frank, K.; Castrogiovanni, A.; Kietzmann, I.; Baldus, S.; Randerath, W.J. Sleep-disordered breathing in patients with cardiovascular diseases cannot be detected by ESS, STOP-BANG, and Berlin questionnaires. Clin. Res. Cardiol. 2018, 107, 1071–1078. [Google Scholar] [CrossRef]
  53. Nunes, F.S.; Danzi-Soares, N.J.; Genta, P.R.; Drager, L.F.; Cesar, L.A.; Lorenzi-Filho, G. Critical evaluation of screening questionnaires for obstructive sleep apnea in patients undergoing coronary artery bypass grafting and abdominal surgery. Sleep Breath 2015, 19, 115–122. [Google Scholar] [CrossRef]
  54. Mazzotti, D.R.; Keenan, B.T.; Lim, D.C.; Gottlieb, D.J.; Kim, J.; Pack, A.I. Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes. Am. J. Respir. Crit. Care Med. 2019, 200, 493–506. [Google Scholar] [CrossRef] [PubMed]
  55. Theorell-Haglöw, J.; Miller, C.B.; Bartlett, D.J.; Yee, B.J.; Openshaw, H.D.; Grunstein, R.R. Gender differences in obstructive sleep apnoea, insomnia and restless legs syndrome in adults—What do we know? A clinical update. Sleep Med. Rev. 2018, 38, 28–38. [Google Scholar] [CrossRef] [PubMed]
  56. Jordan, A.S.; McEvoy, R.D. Gender differences in sleep apnea: Epidemiology, clinical presentation and pathogenic mechanisms. Sleep Med. Rev. 2003, 7, 377–389. [Google Scholar] [CrossRef] [PubMed]
  57. Westreich, R.; Gozlan-Talmor, A.; Geva-Robinson, S.; Schlaeffer-Yosef, T.; Slutsky, T.; Chen-Hendel, E.; Braiman, D.; Sherf, Y.; Arotsker, N.; Abu-Fraiha, Y.; et al. The Presence of Snoring as Well as its Intensity Is Underreported by Women. J. Clin. Sleep Med. 2019, 15, 471–476. [Google Scholar] [CrossRef] [Green Version]
  58. Nigro, C.A.; Dibur, E.; Borsini, E.; Malnis, S.; Ernst, G.; Bledel, I.; González, S.; Arce, A.; Nogueira, F. The influence of gender on symptoms associated with obstructive sleep apnea. Sleep Breath 2018, 22, 683–693. [Google Scholar] [CrossRef]
  59. Sun, X.; Zheng, Z.; Chen, R.; Huang, H.; Lei, W.; Peng, M.; Li, X.; Zhang, N.; Cheng, J. The influence of sex on anthropometric methods and four scales for screening obstructive sleep apnea. Auris Nasus Larynx 2022, 49, 634–643. [Google Scholar] [CrossRef]
  60. Lim, Y.H.; Choi, J.; Kim, K.R.; Shin, J.; Hwang, K.G.; Ryu, S.; Cho, S.H. Sex-specific characteristics of anthropometry in patients with obstructive sleep apnea: Neck circumference and waist-hip ratio. Ann. Otol. Rhinol. Laryngol. 2014, 123, 517–523. [Google Scholar] [CrossRef]
  61. Ludka, O.; Stepanova, R.; Vyskocilova, M.; Galkova, L.; Mikolaskova, M.; Belehrad, M.; Kostalova, J.; Mihalova, Z.; Drozdova, A.; Hlasensky, J.; et al. Sleep apnea prevalence in acute myocardial infarction—The Sleep Apnea in Post-acute Myocardial Infarction Patients (SAPAMI) Study. Int. J. Cardiol. 2014, 176, 13–19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Takagi, T.; Nakamura, K.; Hashimoto, H.; Asami, M.; Ishii, R.; Enomoto, Y.; Moroi, M.; Noro, M.; Sugi, K.; Nakamura, M. The impact of sleep apnea on right atrial structural remodeling with atrial fibrillation. J. Cardiol. 2020, 75, 665–672. [Google Scholar] [CrossRef] [PubMed]
  63. Wang, T.; Yu, F.C.; Wei, Q.; Chen, L.; Xu, X.; Ding, N.; Tong, J.Y. Prevalence and clinical characteristics of sleep-disordered breathing in patients with heart failure of different left ventricular ejection fractions. Sleep Breath 2023, 27, 245–253. [Google Scholar] [CrossRef]
  64. Mañas, E.; Barbero, E.; Chiluiza, D.; García, A.; Morillo, R.; Barrios, D.; Martínez-García, M.Á.; Albalat, A.; Cano, I.; Jiménez, D.; et al. Impact of obstructive sleep apnea on cardiovascular outcomes in patients with acute symptomatic pulmonary embolism: Rationale and methodology for the POPE study. Clin. Cardiol. 2017, 40, 1182–1188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Parameters taken into account in the risk assessment of obstructive sleep apnea on individual scales. ANC: the adjusted neck circumference; ESS: the Epworth sleepiness scale; STOP-BANG: the STOP-BANG questionnaire; 4-V: the 4-variable screening tool.
Table 1. Parameters taken into account in the risk assessment of obstructive sleep apnea on individual scales. ANC: the adjusted neck circumference; ESS: the Epworth sleepiness scale; STOP-BANG: the STOP-BANG questionnaire; 4-V: the 4-variable screening tool.
Symptoms/Risk FactorsRisk Questionnaires
STOP-BANG4-VESSANC
Age older than 50 years+
Male sex++
Increased body mass index++
Neck circumference+ +
Lond and frequent snoring++ +
Observed daytime sleepiness+ +
Excessive daytime sleepiness+ +
High blood pressure/hypertension++ +
Table
Table 2. Characteristics of anthropometric parameters and comorbidities in the surveyed men and women.
Table 2. Characteristics of anthropometric parameters and comorbidities in the surveyed men and women.
All SurveyedMaleFemalep Value
n = 349 (79.7%)n = 89 (20.3%)
Anthropometric parameters
Age (years), mean ± SD59.76 ± 11.0860.55 ± 10.310.545
Neck circumference (cm), median (IQR)43.0 (41.0–44.5)38.0 (36.0–39.0)<0.001
Body weight (kg), mean ± SD88.85 ± 14.7774.84 ± 14.48<0.001
BMI (kg/m2), mean ± SD29.05 ± 4.4228.55 ± 5.030.354
Comorbidities
CAD with a history of MI, n (%)349 (100.0%)89 (100.0%)1.000
Hypertension, n (%)282 (80.8%)77 (86.5%)0.211
Atrial fibrillation, n (%)26 (7.4%)1 (1.1%)0.027
History of ischemic stroke, n (%)7 (2.0%)1 (1.1%)0.580
HFmrEF, n (%)73 (21.3%)18 (20.7%)0.894
HFrEF, n (%)7 (2.0%)2 (2.2%)0.886
LVEF (%), mean ± SD52.98 ± 5.7653.64 ± 6.100.341
Diabetes, n (%)78 (22.3%)22 (24.7%)0.635
COPD, n (%)16 (4.6%)2 (2.2%)0.322
Smoking status (within the last 5 years), n (%)138 (39.5%)45 (50.6%)0.060
BMI: body mass index; CAD: coronary artery disease, cm: centimeter, COPD: chronic obstructive pulmonary disease, HFmrEF: heart failure with mildly reduced ejection fraction, HFrEF: heart failure with reduced ejection fraction, IQR: interquartile range, LVEF: left ventricular ejection fraction, MI: myocardial infarction, SD: standard deviation.
Table
Table 3. Characteristics of polysomnographic parameters in men and women with obstructive sleep apnea.
Table 3. Characteristics of polysomnographic parameters in men and women with obstructive sleep apnea.
Polysomnographic ParametersMaleFemalep Value
n = 238n = 37
Total sleep time (min), mean ± SD463.64 ± 112.85481.89 ± 136.080.546
REI (events/h), median (IQR)12.75 (5.80–25.30)6.50 (3.43–13.23)0.007
Average episode duration (s), mean ± SD22.93 ± 6.3720.47± 5.960.029
Max episode duration (s), mean ± SD63.14 ± 28.7347.89 ± 27.820.003
Average SpO2 (%), mean ± SD92.79 ± 1.8592.92 ± 2.600.771
Minimal SpO2 (%), mean ± SD83.46 ± 6.3285.95 ± 4.270.003
TST 90 (%), median (IQR)1.80 (0.27–7.58)1.40 (0.00–10.30)0.550
Mean HR at night, mean ± SD59.35 ± 7.7959.79 ± 7.440.754
Minimal HR at night, mean ± SD46.78 ± 7.2749.47 ± 7.030.044
HR: heart rate measured by pulse oximetry; IQR: interquartile range; OSA: obstructive sleep apnea; REI: respiratory event index; SpO2: arterial oxygen saturation estimated by pulse oximetry; SD: standard deviation; TST 90: the percentage of total sleep time with oxyhemoglobin saturation below 90%.
Table
Table 4. The prevalence of risk factors and score on the risk scales in the entire study group and subgroups, depending on the severity of obstructive sleep apnea and sex.
Table 4. The prevalence of risk factors and score on the risk scales in the entire study group and subgroups, depending on the severity of obstructive sleep apnea and sex.
SexAll Surveyed
n = 438		None SDB
n = 72		Mild OSA
n = 85		Moderate OSA
n = 53		Severe OSA
n = 48		p Value for Trend
Age >50 years, n (%)M276 (79.1%)37 (66.1%)57 (77.0%)39 (78.0%)40 (88.9%)0.010
F73 (82.0%)13 (81.2%)10 (90.9%)3 (100.0%)3 (100.0%)0.225
BMI > 35 kg/m2, n (%)M34 (9.7%)5 (8.9%)5 (6.8%)9 (18.0%)9 (20.0%)0.028
F8 (9.0%)0 (0.0%)5 (45.5%)0 (0.0%)2 (66.7%)0.014
Neck circumference > 40 cm, n (%)M272 (77.9%)38 (67.9%)62 (83.8%)42 (84.0%)42 (93.3%)0.002
F14 (15.7%)0 (0.0%)5 (45.5%)2 (66.7%)1 (33.3%)0.014
BP ≥ 140/90 mmHg/hypertension, n (%)M282 (80.8%)42 (75.0%)60 (81.1%)43 (86.0%)41 (91.1%)0.025
F77 (86.5%)14 (87.5%)11 (100.0%)2 (66.7%)3 (100.0%)0.816
Loud and frequent snoring, n (%)M160 (45.8%)18 (32.1%)44 (59.5%)30 (60.0%)34 (75.6%)<0.001
F42 (47.2%)8 (50.0%)5 (45.5%)1 (33.3%)3 (100.0%)0.337
Observed stop breathing/choking/gasping, n (%)M74 (21.3%)10 (17.9%)21 (28.4%)19 (38.0%)15 (33.3%)0.041
F14 (15.7%)2 (12.5%)3 (27.3%)0 (0.0%)3 (100.0%)0.014
Daytime sleepiness/tiredness, n (%)M133 (38.1%)27 (48.2%)28 (37.8%)19 (38.0%)26 (57.8%)0.401
F60 (67.4%)13 (81.2%)9 (81.8%)1 (33.3%)3 (100.0%)0.827
Sleepiness on ESS (pts),
median (IQR)		M4.04.05.05.06.00.111
(2.0–8.0)(3.0–7.5)(2.0–8.0)(4.0–9.0)(3.0–8.5)
F4.05.07.04.03.00.467
(2.0–8.0)(2.0–7.5)(3.8–10.0)(3.3–7.8)(1.5–14.3)
STOP-BANG (pts),
median (IQR)		M5.04.05.05.06.0<0.001
(4.0–5.0)(3.0–5.0)(4.0–6.0)(4.0–6.0)(5.0–6.0)
F3.03.04.03.06.00.003
(2.0–4.0)(3.0–3.5)(3.3–5.0)(1.5–4.5)(6.0–6.0)
4-V (pts),
median (IQR)		M11.010.013.013.014.0<0.001
(9.0–14.0)(9.5–13.5)(10.0–14.0)(10.0–15.0)(11.0–15.0)
F7.07.09.07.011.00.050
(6.0–10.0)(6.0–9.5)(7.0–11.0)(6.3–10.8)(11.0–11.0)
ANC (cm),
median (IQR)		M47.546.048.049.050.0<0.001
(44.5–50.5)(43.3–50.0)(45.0–50.5)(47.0–53.0)(47.0–54.0)
F43.041.046.045.050.0<0.001
(40.0–46.0)(37.8–42.5)(44.3–49.8)(42.8–48.0)(49.3–50.8)
ANC: the adjusted neck circumference, BMI: body mass index, BP: blood pressure, cm: centimeter, ESS: the Epworth sleepiness scale, F: female, IQR: interquartile range, M: male; No: number, SDB: sleep-disordered breathing, STOP-BANG: the STOP-BANG questionnaire, 4-V: the 4-variable screening tool.
Table
Table 5. The sensitivity, specificity, and positive and negative predictive values of the questionnaires in the prediction of moderate-to-severe obstructive sleep apnea in the study population.
Table 5. The sensitivity, specificity, and positive and negative predictive values of the questionnaires in the prediction of moderate-to-severe obstructive sleep apnea in the study population.
QuestionnaireSensitivity (%) *Specificity (%) *PPV (%) *NPV (%) *Accuracy (%)
STOP-BANG79.21 (70.0–86.6)35.67 (28.2–43.7)44.2 (40.5–48.0)72.7 (63.3–80.5)52.71
ANC61.39 (51.2–70.9)61.15 (53.1–68.8)50.4 (44.2–56.6)71.1 (65.1–76.4)61.24
4-V45.54 (35.6–55.8)68.79 (60.9–75.9)48.4 (40.6–56.3)66.3 (61.5–70.7)60.01
ESS16.83 (10.1–25.6)87.90 (81.7–92.6)47.2 (32.8–62.1)62.2 (59.7–64.6)59.30
* Data presented as average (95% confidence interval); ANC: the adjusted neck circumference; ESS: the Epworth sleepiness scale; NPV: negative predictive value; PPV: positive predictive value; STOP-BANG: the STOP-BANG questionnaire; 4-V: the 4-variable screening tool.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Łoboda, D.; Stepanik, M.; Szajerska-Kurasiewicz, A.; Turski, M.; Urbanczyk-Świć, D.; Durmała, J.; Gołba, K.S. The Usefulness of Questionnaires in Assessing the Risk of Obstructive Sleep Apnea in Patients in the Managed Care after Acute Myocardial Infarction Program—The Results of a Cross-Sectional Study. J. Pers. Med. 2023, 13, 642. https://doi.org/10.3390/jpm13040642

AMA Style

Łoboda D, Stepanik M, Szajerska-Kurasiewicz A, Turski M, Urbanczyk-Świć D, Durmała J, Gołba KS. The Usefulness of Questionnaires in Assessing the Risk of Obstructive Sleep Apnea in Patients in the Managed Care after Acute Myocardial Infarction Program—The Results of a Cross-Sectional Study. Journal of Personalized Medicine. 2023; 13(4):642. https://doi.org/10.3390/jpm13040642

Chicago/Turabian Style

Łoboda, Danuta, Michalina Stepanik, Anna Szajerska-Kurasiewicz, Maciej Turski, Dagmara Urbanczyk-Świć, Jacek Durmała, and Krzysztof S. Gołba. 2023. "The Usefulness of Questionnaires in Assessing the Risk of Obstructive Sleep Apnea in Patients in the Managed Care after Acute Myocardial Infarction Program—The Results of a Cross-Sectional Study" Journal of Personalized Medicine 13, no. 4: 642. https://doi.org/10.3390/jpm13040642

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop