Obstructive Sleep Apnea Hypopnea Syndrome (OSAS) in a French overseas territory: a cross-sectional study in Martinique

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In Martinique, a French overseas territory, the prevalence of continuous positive airway pressure (CPAP) therapy is the highest in France, suggesting a substantial burden of moderate-to-severe disease. The objective of this study was to assess the prevalence of OSAS and to identify factors associated with moderate-to-severe OSAS in this population. Methods We conducted an observational study including 213 consecutive adults evaluated at the Sleep Unit of the University Hospital of Martinique between July 2024 and July 2025. Moderate-to-severe OSAS was defined in symptomatic patients by an apnea–hypopnea index (AHI) ≥ 15 events/hour. Results OSAS (AHI ≥ 5 events/hour) was diagnosed in 189 patients, corresponding to a prevalence of 88.7% (95% CI 84.4–93.0%). Moderate-to-severe disease was observed in 61.3% of cases. Although women accounted for 65% of referrals, men more frequently presented with moderate-to-severe OSAS. Insomnia symptoms were reported by 62% of patients, and comorbid insomnia and sleep apnea (COMISA) was identified in 57.7% of those with confirmed OSAS. In multivariable analysis, male sex, increased neck circumference, snoring, nocturia, and witnessed apneas were independently associated with moderate-to-severe OSAS. Conclusion This study demonstrates a very high hospital-based prevalence of OSAS in Martinique and reveals a marked gender-related diagnostic gap. Simple clinical markers may facilitate earlier identification of patients at high risk for moderate-to-severe OSAS. obstructive sleep apnea insomnia French overseas territory COMISA healthcare inequity Introduction Obstructive sleep apnea syndrome (OSAS) is a prevalent but underdiagnosed chronic respiratory disorder associated with substantial cardiovascular, metabolic and neurocognitive morbidity ( 1 – 3 ). In France, OSAS affects approximately 4% of the adult population, yet a large proportion of cases remain undiagnosed, particularly in populations with high cardiometabolic risk ( 4 – 6 ). OSAS is characterized by recurrent upper airway collapse during sleep, leading to intermittent hypoxia and sleep fragmentation. Diagnosis relies on respiratory polygraphy or polysomnography, and disease severity is defined by the apnea–hypopnea index ( 7 ). Continuous positive airway pressure (CPAP) remains the first-line treatment for severe OSAS ( 6 ). In Martinique, a French overseas territory, the prevalence of CPAP treatment reaches 3.8%, the highest among all French departments ( 8 ). This unusually high rate suggests a substantial burden of moderate-to-severe OSAS, potentially driven by the high prevalence of obesity, diabetes and hypertension observed in this population ( 9 – 12 ). Despite the growing burden of OSAS in Europe, data from overseas territories remain scarce and are largely absent from the European respiratory literature. These regions combine a high prevalence of cardiometabolic risk factors with specific sociodemographic characteristics that may influence disease presentation, severity and access to diagnosis. In Martinique, the clinical profile of patients referred for sleep apnea evaluation, the determinants of disease severity, and potential diagnostic inequalities (particularly across sex) remain poorly characterized. Moreover, comorbid insomnia and sleep apnea (COMISA), a condition associated with diagnostic delay and poorer outcomes, has not been specifically explored in this population. Therefore, this study aimed to characterize patients referred for suspected OSAS in Martinique and to identify factors associated with moderate-to-severe disease. Materials and Methods Study Design and Participants The study included all adults (aged ≥ 18 years) who were referred by their physician for sleep apnea screening at the Sleep Center of the University Hospital of Martinique (CHUM) between July 1st, 2024 and July 31st, 2025. Sample Size Calculation The sample size was calculated based on an estimated prevalence of 85% for confirmed OSAS diagnoses among patients undergoing sleep apnea screening at the University Hospital of Martinique. This prevalence was derived from a pilot study conducted prior to the main investigation. Using Cochran’s formula for sample size estimation for proportions, assuming a power of 80% and an alpha risk of 5%, the minimum required sample size was determined to be 197 patients. Endpoints The primary endpoint of this study was to determine the proportion of patients with OSAS among patients referred for sleep apnea screening at the Sleep Center of the Pulmonology Department, University Hospital of Martinique. The secondary endpoints included determining the factors associated with moderate-to-severe OSAS, determining the proportion of self-reported insomnia and COMISA (Comorbid Insomnia and Sleep Apnea), and describing the prevalence of periodic limb movements among patients who underwent full polysomnography. Data Collection and Questionnaire Self-administered Medical Questionnaire Prior to the sleep study, all patients completed a standardized self-administered questionnaire comprising 120 items. The following information was collected: sociodemographic and anthropometric data (age, sex, weight, height, neck and waist circumference, occupational category), lifestyle factors (alcohol and tobacco consumption, physical activity, caffeine intake, recreational drug use, sleep habits, and daytime functioning), medication use (current treatments, sleep medications), medical history (general, Ear-Nose-Throat (ENT), respiratory, and cardiovascular comorbidities), Sleep-related symptoms (snoring, witnessed apneas, daytime sleepiness, morning headaches, nocturia, insomnia, memory impairment, decreased libido, restless legs syndrome), clinical information (reason for referral, reported symptoms, and quality of sleep) and standardized scales (Epworth Sleepiness Scale, Pichot Fatigue Scale for fatigue assessment, Pichot Depression Scale QD2A) Sleep Study Recordings: Diagnostic data were obtained from either polysomnography or respiratory polygraphy, using the NOX software system. Parameters recorded were: Apnea-Hypopnea Index (AHI), Oxygen Desaturation Index (ODI), micro-arousal index and periodic Limb movement index (PLMI) when polysomnography. Obstructive sleep apnea was defined as an apnea–hypopnea index (AHI) ≥ 5 events/hour. Severity was classified as mild (5–14.9 events/hour), moderate (15–29.9 events/hour), and severe (≥ 30 events/hour). Statistical analysis Data were extracted using Microsoft Excel and exported to R software version 4.5.2 for all statistical analyses. We first conducted descriptive analyses to characterize patients according to OSAS severity, followed by analytical statistics. Continuous variables were described using means with standard deviations (SD) and/or medians with interquartile ranges (IQR), depending on their distribution. Categorical variables were presented as frequencies and percentages. For between-group comparisons, Student’s t-test was used for continuous variables, while Chi-square or Fisher’s exact tests were applied for categorical variables. Variables associated with moderate-to-severe OSAS at a significance threshold of p < 0.20 in bivariate analysis were included in a multivariable binary logistic regression model to identify independent factors associated with OSAS severity. Statistical significance was set at p < 0.05 for all tests. Ethical considerations The study was conducted in accordance with the World Medical Association Declaration of Helsinki and was approved by the Institutional Review Board of the Centre Hospitalier Universitaire de Martinique (approval number 2025/072). Patient consent was obtained through signed consent forms during consultations and information notices displayed in the Pulmonology Department. Results In the study period, 213 consecutive patients were included in the study. Of these, 114 received polygraphy and 99 polysomnography. The study population was predominantly female (65.3% n = 139) with a mean age of 56 years ± 16.1 years The reasons for referral were mostly snoring: 61 (28,6%), and insomnia: 34 (16%) Excessive daytime sleepiness: 31 (14,6%), assessment before bariatric surgery 5 (2%), Other reasons for referral were post-stroke assessment, arrhythmia evaluation, and ophthalmologic pre-surgery assessment :15 (7%). Baseline demographic and anthropometric characteristics are summarized in Table 1 . Table 1 Baseline Characteristics of the Study Population and Factors Associated with Moderate-to-Severe OSAS Characteristic Overall N = 213 1 Mild/non-OSAS N = 97 1 Moderate/Severe OSAS N = 116 1 p-value 2 Age 56 ( 16 ) 53 ( 17 ) 58 ( 15 ) 0.021 Sex (male) 74 (35%) 23 (24%) 51 (44%) 0.002 BMI 32 ( 19 ) 29 ( 7 ) 34 (25) 0.031 Neck circumference 40.4 (7.1) 38.7 (6.3) 41.6 (7.4) 0.010 Missing data 60 33 27 Waist circumference 105 ( 15 ) 100 ( 14 ) 108 ( 15 ) 0.001 Missing data 61 34 27 Family history of OSAS 68 (39%) 31 (36%) 37 (41%) 0.6 Missing data 37 12 25 Shift work 27 (15%) 8 (9.5%) 19 (19%) 0.07 Missing data 30 13 17 Sleep duration on workdays 7.43 (1.57) 7.54 (1.37) 7.35 (1.68) 0.4 Missing data 53 25 28 Sleep duration on rest days 8.17 (4.05) 7.99 (1.37) 8.31 (5.31) 0.6 Missing data 56 27 29 Current smoker 21 (10%) 10 (11%) 11 (9.8%) 0.8 Missing data 7 3 4 Former smoker 50 (26%) 21 (24%) 29 (27%) 0.7 Missing data 18 10 8 Caffeine consumption 101 (50%) 46 (51%) 55 (50%) > 0.9 Missing data 12 6 6 Tea consumption 68 (35%) 38 (42%) 30 (29%) 0.05 Missing data 18 7 11 Alcohol consumption 60 (36%) 24 (32%) 36 (39%) 0.3 Use of hypnotics 30 (15%) 15 (17%) 15 (14%) 0.6 Missing data 17 7 10 Other treatments 72 (68%) 33 (66%) 39 (70%) 0.7 Sport 142 (71%) 68 (74%) 74 (68%) 0.4 Missing data 12 5 7 1 Mean (SD); n (%) 2 Welch Two Sample t-test; Pearson’s Chi-squared test; Fisher’s exact test The clinical symptom profile demonstrated a high prevalence of sleep-related complaints (Table 2 ). Among active drivers, 13.7% (n = 27) reported having avoided a motor vehicle accident due to sleepiness while driving. Table 2 Symptoms of the Study Population and Factors Associated with Moderate-to-Severe OSAS Characteristic Overall N = 213 1 Mild/non-OSAS N = 97 1 Moderate/Severe OSAS n = 116 1 p-value 2 Insomnia 124 (62%) 54 (61%) 70 (63%) 0.8 Missing data 14 9 5 Snoring 0.020 Never 12 (5.9%) 10 (11%) 2 (1.8%) Yes 141 (70%) 60 (67%) 81 (72%) I don’t know 49 (24%) 20 (22%) 29 (26%) Daytime sleepiness 186 (89%) 86 (91%) 100 (88%) 0.5 Missing data 4 2 2 Daytime nap 74 (39%) 29 (34%) 45 (43%) 0.2 Missing data 23 11 12 Witnessed apnea 72 (37%) 24 (26%) 48 (47%) 0.004 Missing data 19 6 13 Memory impairment 159 (78%) 75 (81%) 84 (76%) 0.4 Missing data 9 4 5 Nocturnal awakening 196 (96%) 88 (95%) 108 (96%) 0.7 Missing data 8 4 4 Nocturia 109 (52%) 42 (45%) 67 (58%) 0.070 Missing data 4 4 0 Decreased libido 84 (45%) 30 (34%) 54 (53%) 0.009 Missing data 25 10 15 Morning headaches 132 (64%) 64 (68%) 68 (61%) 0.3 Missing data 8 3 5 Night sweats 146 (71%) 64 (67%) 82 (73%) 0.4 Missing data 6 2 4 Tired on waking 146 (72%) 65 (71%) 81 (72%) 0.9 Missing data 10 6 4 Better sleep on days off 125 (65%) 55 (62%) 70 (69%) 0.3 Missing data 22 8 14 1 Mean (SD); n (%) 2 Welch Two Sample t-test; Pearson’s Chi-squared test; Fisher’s exact test Cardiovascular comorbidities were highly prevalent in the study population. Hypertension was the most common comorbidity, affecting 51%, followed by arrhythmias in 22.2% and heart failure in 13.1% (n = 23). Metabolic disorders included hypercholesterolemia in 18.8% (n = 34) and diabetes mellitus in 16.5% of cases (n = 30) (Table 3 ). Table 3 Comorbidities of the Study Population and Factors Associated with Moderate-to-Severe OSAS Characteristic Overall N = 213 1 Mild/non-OSAS N = 97 1 Moderate/Severe OSAS N = 116 1 p-value 2 Asthma 64 (34%) 34 (39%) 30 (30%) 0.2 Missing data 25 9 16 COPD 14 (8.1%) 6 (7.7%) 8 (8.5%) 0.8 Missing data 41 19 22 Chronic cough 43 (24%) 24 (30%) 19 (20%) 0.12 Missing data 35 16 19 Dyspnea 55 (31%) 24 (29%) 31 (33%) 0.6 Missing data 38 15 23 Tonsillectomy 34 (18%) 11 (13%) 23 (22%) 0.082 Missing data 21 9 12 Adenoidectomy 18 (9.8%) 7 (8.1%) 11 (11%) 0.5 Missing data 29 11 18 Soft palate surgery 1 (0.6%) 1 (1.2%) 0 (0%) 0.5 Missing data 34 13 21 Septoplasty 3 (1.7%) 2 (2.4%) 1 (1.0%) 0.6 Missing data 32 13 19 Turbinectomy 7 (3.9%) 3 (3.6%) 4 (4.2%) > 0.9 Missing data 34 13 21 Sinus surgery 11 (6.1%) 8 (9.3%) 3 (3.2%) 0.087 Missing data 33 11 22 Hypertension 100 (51%) 42 (47%) 58 (55%) 0.3 Missing data 40 14 26 Cardiac arrhythmia 39 (22%) 20 (24%) 19 (21%) 0.7 Missing data 37 12 25 Heart Failure 23 (13%) 10 (12%) 13 (14%) 0.7 Missing data 37 13 24 Aneurysm 3 (1.9%) 2 (2.7%) 1 (1.2%) 0.6 Missing data 57 23 34 Stroke 22 (12%) 10 (12%) 12 (13%) 0.8 Missing data 32 11 21 Depression 33 (18%) 17 (20%) 16 (17%) 0.7 Missing data 34 10 24 Diabetes 30 (16%) 14 (16%) 16 (17%) > 0.9 Missing data 31 11 20 Hypercholesterolemia 34 (19%) 11 (13%) 23 (24%) 0.058 Missing data 32 12 20 Thyroid dysfunction 26 (15%) 8 (9.2%) 18 (20%) 0.049 Missing data 34 10 24 GERD 30 (17%) 12 (14%) 18 (20%) 0.3 Missing data 38 11 27 Peptic ulcer 8 (4.6%) 5 (5.8%) 3 (3.4%) 0.5 Missing data 40 11 29 Orthodontic treatment 11 (6.2%) 5 (5.7%) 6 (6.6%) 0.8 Missing data 35 10 25 Jaw pain 19 (11%) 14 (16%) 5 (5.6%) 0.022 Missing data 37 11 26 Jaw dislocation 1 (0.6%) 1 (1.2%) 0 (0%) 0.5 Missing data 36 11 25 Occlusal splint 10 (5.6%) 6 (6.9%) 4 (4.3%) 0.5 Missing data 34 10 24 Neurosurgery 4 (2.3%) 2 (2.3%) 2 (2.2%) > 0.9 Missing data 36 11 25 Cranial trauma 6 (3.4%) 1 (1.2%) 5 (5.4%) 0.2 Missing data 35 11 24 Glaucoma 20 (11%) 12 (14%) 8 (8.8%) 0.3 Missing data 35 10 25 1 Mean (SD); n (%) 2 Welch Two Sample t-test; Pearson’s Chi-squared test; Fisher’s exact test Abbreviations: OSAS = obstructive sleep apnea syndrome; COPD = chronic obstructive pulmonary disease; GERD = gastroesophageal reflux Insomnia symptoms were reported by 124 patients (62%), with sleep maintenance insomnia being the most common subtype, 53.3% (n = 65). Among patients with confirmed SAS (n = 189), COMISA (comorbid insomnia and sleep apnea) was identified in 57.7% of cases (n = 109). OSAS was confirmed in 189 patients (88.7%, 95%CI 84.4–93.0%). Disease severity distribution was as follows: mild OSAS in 73 patients (38.6%, 95%CI 31.7–45.6%), moderate OSAS in 56 patients (29.6%, 95% CI 23.1–36.1%), and severe SAS in 60 patients (31.7%, 95% CI 25.1–38.4%). In bivariate analysis, patients with moderate-to-severe OSAS were significantly older compared to those with mild/non-OSAS (mean age 58 vs 53 years, p = 0.021). They presented higher BMI values (median 34 vs 29 kg/m 2 , p = 0.031), as well as higher weight 2 years ago and maximum lifetime weight. Neck circumference 41.6 cm vs 38.7 cm (p = 0.010) and waist circumference 108 cm vs 100 cm (p = 0.001) were also significantly greater in the moderate-to-severe OSAS group. Male sex was significantly associated with moderate-to-severe OSAS, representing 44% of this group compared to 24% in the mild/non-OSAS group (p = 0.002). Snoring was significantly more prevalent in the moderate-to-severe OSAS group (p = 0.020) with 72% (n = 81) compared to the mild/non-OSAS group, 67% (n = 60). Witnessed apneas were reported by 47% (n = 48) of patients with moderate-to-severe OSAS versus 26% (n = 24) in the mild/non-OSAS group (p = 0.004). Similarly, decreased libido affected 53% (n = 54) of patients with moderate-to-severe OSAS compared to 34% (n = 30) in the mild/non-OSAS group (p = 0.009). In multivariate analysis, factors independently associated with moderate-to-severe OSAS included male sex, neck circumference, snoring, nocturia and witnessed apnea (Table 4 ). Table 4 Independent Factors Associated with Moderate-to-Severe OSAS Characteristic OR 95% CI p-value Sex (male) 3.50 1.42, 9.20 0.008 Neck circumference (cm, per SD increase) 5.55 2.21, 15.7 < 0.001 Snoring 3.29 1.43, 8.29 0.007 Nocturia 2.72 1.19, 6.48 0.020 Witnessed apnea 3.66 1.55, 9.29 0.004 Abbreviations: CI = Confidence Interval; OR = Odds Ratio; cm = centimeter; SD = standard deviation; BMI = body mass index ; ESS = Epworth scale score *Only variables with p < 0.20 in bivariate analysis were included in the model Discussion The principal findings of our study were: the high prevalence of OSAS, underdiagnosis in men, high prevalence of suggestive COMISA and male sex, increased neck circumference, snoring, nocturia, and witnessed apneas as independent risk factors for moderate-to-severe OSAS. This high prevalence reflected referral bias in specialized sleep centers, where patients often present with typical clinical symptoms, or cardiometabolic comorbidities that increase the probability of OSAS. A similar prevalence (from 88 to 91% for OSAS and 52% to 74% for moderate-to-severe OSAS) were reported in a Sleep Center in Saudi Arabia ( 13 ) and Brazil ( 14 ). This substantial proportion of moderate-to-severe OSAS confirms that patients referred for sleep testing frequently have clinically significant disease requiring active management. These findings reinforce the relevance of targeted pre-hospital screening and highlight the need for improved identification of at-risk individuals in primary care, to optimize referral pathways and reduce diagnostic delays. The demographic characteristics of our cohort provide important insight into potential diagnostic inequalities. Although women accounted for 65% of referrals to the sleep centre, moderate-to-severe OSAS was significantly more prevalent among men (44% vs 24%, p = 0.002), in line with established epidemiological data ( 15 ). This discordance suggests that men, despite being at higher risk for severe OSAS, are underrepresented in diagnostic pathways and tend to be diagnosed at a more advanced stage of disease. Similar sex-related disparities in healthcare utilization have been reported previously and may reflect differences in symptom perception, health-seeking behavior, or access to care. These findings highlight a potential gender-related diagnostic delay and underline the need for targeted screening strategies aimed at improving early identification of OSAS among men. Insomnia symptoms were reported by 62% of patients, and among those with confirmed OSAS, comorbid insomnia and sleep apnea (COMISA) was identified in 57.7% (95% CI 54–68%). This prevalence is consistent with the range reported in international studies, generally between 40% and 60% ( 16 ). However, substantial variability has been described across cohorts, with lower estimates reported in regional French populations, such as the Pays de la Loire cohort (14%) ( 17 ), and higher prevalences observed in large multicenter cohorts, including the ESADA cohort (51.7%)( 18 ). In contrast, population-based data from Australia reported lower COMISA prevalence (10.2%) ( 19 ), highlighting the influence of referral pathways and study design on prevalence estimates. COMISA has been consistently associated with greater daytime impairment, increased psychiatric and cardiovascular comorbidities ( 18 – 21 ), and poorer adherence to continuous positive airway pressure therapy ( 20 ). The high prevalence observed in our cohort therefore emphasizes the clinical relevance of systematic insomnia screening in patients referred for OSAS evaluation, particularly in specialized sleep centers where diagnostic delay and disease complexity are common. Our findings have direct clinical and public health implications for the organization of OSAS screening and care pathways in European overseas territories. The identification of simple parameters of moderate-to-severe OSAS (male sex, increased neck circumference, snoring, nocturia and witnessed apneas) supports the use of pragmatic, low-cost screening approaches in primary care. These readily accessible clinical markers may help prioritize referrals for sleep testing in settings where diagnostic resources are limited and waiting times remain substantial. The marked under-representation of men in the referral population, despite a higher prevalence of severe disease, highlights a critical gender-related diagnostic gap. Targeted strategies aimed at improving early identification of OSAS in men, including opportunistic screening during cardiovascular or metabolic consultations, could contribute to reducing diagnostic delay and disease severity at presentation. The high prevalence of COMISA observed in this cohort further emphasizes the need for integrated sleep assessments. Systematic evaluation of insomnia symptoms in patients referred for suspected OSAS may improve diagnostic accuracy, inform therapeutic choice, and optimize adherence to continuous positive airway pressure therapy through early identification of patients requiring combined management strategies. At a public health level, these findings support the adaptation of national OSAS screening recommendations to the specific epidemiological and organizational context of overseas territories. Strengthening primary care training in sleep medicine, improving access to sleep investigations, and promoting decentralized screening strategies may enhance early diagnosis and reduce long-term cardiometabolic and neurocognitive complications associated with untreated OSAS. Strengths, limitations and future directions Certain limitations should be acknowledged. First, this study was conducted in a hospital-based population, with patients often symptomatic and referred to a tertiary reference center; therefore, the findings cannot be generalized to the general population. Second, the use of a self-administered questionnaire may have introduced recall bias, particularly in an older and comorbid population, where cognitive complaints were frequently reported. Despite these limitations, this study has notable strengths. To our knowledge, it provides the first detailed hospital-based epidemiological characterization of patients referred for suspected OSAS in a French overseas territory. These data offer a valuable foundation for the development of targeted screening strategies and public health policies adapted to the specific epidemiological and organisational context of Martinique. Future research should aim to validate these findings in community-based cohorts and to explore the feasibility of simplified predictive tools incorporating locally relevant risk factors. Comparative analyses across French overseas territories, including Guadeloupe and French Guiana, would further enhance the understanding of regional disparities and support the adaptation of national recommendations to overseas settings. Conclusion This study confirms a very high hospital-based prevalence of OSAS among patients referred for sleep evaluation in Martinique (88.7%), reflecting a substantial burden of clinically significant disease. It also highlights a marked gender-related diagnostic gap, as men (although representing a minority of referrals) presented with more moderate-to-severe forms, suggesting delayed diagnosis. Male sex, increased neck circumference, snoring, nocturia and witnessed apnea were independently associated with moderate-to-severe OSAS. These findings support the use of simple clinical markers to improve early identification of high-risk patients, particularly in primary care. Optimizing screening pathways through improved access to sleep investigations and targeted training of general practitioners in overseas territories may help reduce diagnostic delay and limit long-term complications of untreated OSAS. Declarations Ethics approval and consent to participate The study received approval from the Institutional Review Board of the University Hospitals of Martinique (approval number 2025/072). Written informed consent was obtained from all participants and/or their legal guardian(s). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Authors’ contributions ECM: Collected data, analyzed and interpreted the results, and drafted the manuscript. MNS: performed data analysis and interpretation. BA: Contributed to patient management. Andreu Mathilde: Contributed to patient management. IJ: Contributed to writing the manuscript Dufeal Marion : Contributed to patient management. Dramé Moustapha: Contributed to study conception and data analysis. Agossou Moustapha: Conceived the study, analyzed and interpreted the results, and drafted the manuscript. All authors read and approved the final manuscript. 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Effect of depression, anxiety, and stress symptoms on response to cognitive behavioral therapy for insomnia in patients with comorbid insomnia and sleep apnea: a randomized controlled trial. J Clin Sleep Med. 2021;17(3):545–54. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 13 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 10 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8569681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587091474,"identity":"b709af39-19cd-4438-ba54-84b3259eda3e","order_by":0,"name":"Marion Edoh Coffi","email":"","orcid":"","institution":"University of the French West Indies","correspondingAuthor":false,"prefix":"","firstName":"Marion","middleName":"Edoh","lastName":"Coffi","suffix":""},{"id":587091476,"identity":"4eeb8964-2358-4ed9-9c5e-ba20bf4febe7","order_by":1,"name":"Salvatore Metanmo Ndziha","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Salvatore","middleName":"Metanmo","lastName":"Ndziha","suffix":""},{"id":587091477,"identity":"87271d6a-89db-4b71-acea-705ad2bb2f8b","order_by":2,"name":"Berenice Awanou","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Berenice","middleName":"","lastName":"Awanou","suffix":""},{"id":587091479,"identity":"33323147-8a15-42f9-abcb-232f8e9dede1","order_by":3,"name":"Mathilde Andreu","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Andreu","suffix":""},{"id":587091480,"identity":"d93979bc-04ef-481d-aa54-e7f92321961e","order_by":4,"name":"Jocelyn Inamo","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Jocelyn","middleName":"","lastName":"Inamo","suffix":""},{"id":587091481,"identity":"5679a0a2-fa3d-46f1-9050-411955c836c8","order_by":5,"name":"Mickael Rejuadry-Lacavalerie","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Mickael","middleName":"","lastName":"Rejuadry-Lacavalerie","suffix":""},{"id":587091482,"identity":"efa0988c-9675-46ff-981e-2187e6665cd6","order_by":6,"name":"Océane l’Heveder","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Océane","middleName":"","lastName":"l’Heveder","suffix":""},{"id":587091483,"identity":"11651ffd-f9a6-4dcf-969a-2bf2c4dda624","order_by":7,"name":"Marion Duféal","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Marion","middleName":"","lastName":"Duféal","suffix":""},{"id":587091484,"identity":"3e391a1b-8ba6-44e1-8685-b4fb13604af1","order_by":8,"name":"Moustapha Dramé","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":false,"prefix":"","firstName":"Moustapha","middleName":"","lastName":"Dramé","suffix":""},{"id":587091486,"identity":"40b10aa3-a57d-4766-9cf6-d7d84e25e057","order_by":9,"name":"Moustapha Agossou","email":"data:image/png;base64,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","orcid":"","institution":"Centre Hospitalier Universitaire de Martinique","correspondingAuthor":true,"prefix":"","firstName":"Moustapha","middleName":"","lastName":"Agossou","suffix":""}],"badges":[],"createdAt":"2026-01-10 17:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8569681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8569681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102745494,"identity":"5ea34551-323e-46cf-963d-67479264aba9","added_by":"auto","created_at":"2026-02-16 08:51:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1162950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8569681/v1/a0eca71e-336e-497f-8512-86921e0c91b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Obstructive Sleep Apnea Hypopnea Syndrome (OSAS) in a French overseas territory: a cross-sectional study in Martinique","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObstructive sleep apnea syndrome (OSAS) is a prevalent but underdiagnosed chronic respiratory disorder associated with substantial cardiovascular, metabolic and neurocognitive morbidity (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In France, OSAS affects approximately 4% of the adult population, yet a large proportion of cases remain undiagnosed, particularly in populations with high cardiometabolic risk (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOSAS is characterized by recurrent upper airway collapse during sleep, leading to intermittent hypoxia and sleep fragmentation. Diagnosis relies on respiratory polygraphy or polysomnography, and disease severity is defined by the apnea\u0026ndash;hypopnea index (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Continuous positive airway pressure (CPAP) remains the first-line treatment for severe OSAS (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Martinique, a French overseas territory, the prevalence of CPAP treatment reaches 3.8%, the highest among all French departments (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This unusually high rate suggests a substantial burden of moderate-to-severe OSAS, potentially driven by the high prevalence of obesity, diabetes and hypertension observed in this population (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the growing burden of OSAS in Europe, data from overseas territories remain scarce and are largely absent from the European respiratory literature. These regions combine a high prevalence of cardiometabolic risk factors with specific sociodemographic characteristics that may influence disease presentation, severity and access to diagnosis. In Martinique, the clinical profile of patients referred for sleep apnea evaluation, the determinants of disease severity, and potential diagnostic inequalities (particularly across sex) remain poorly characterized. Moreover, comorbid insomnia and sleep apnea (COMISA), a condition associated with diagnostic delay and poorer outcomes, has not been specifically explored in this population. Therefore, this study aimed to characterize patients referred for suspected OSAS in Martinique and to identify factors associated with moderate-to-severe disease.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThe study included all adults (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years) who were referred by their physician for sleep apnea screening at the Sleep Center of the University Hospital of Martinique (CHUM) between July 1st, 2024 and July 31st, 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Size Calculation\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated based on an estimated prevalence of 85% for confirmed OSAS diagnoses among patients undergoing sleep apnea screening at the University Hospital of Martinique. This prevalence was derived from a pilot study conducted prior to the main investigation. Using Cochran\u0026rsquo;s formula for sample size estimation for proportions, assuming a power of 80% and an alpha risk of 5%, the minimum required sample size was determined to be 197 patients.\u003c/p\u003e\n\u003ch3\u003eEndpoints\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint of this study was to determine the proportion of patients with OSAS among patients referred for sleep apnea screening at the Sleep Center of the Pulmonology Department, University Hospital of Martinique. The secondary endpoints included determining the factors associated with moderate-to-severe OSAS, determining the proportion of self-reported insomnia and COMISA (Comorbid Insomnia and Sleep Apnea), and describing the prevalence of periodic limb movements among patients who underwent full polysomnography.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData Collection and Questionnaire\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSelf-administered Medical Questionnaire\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePrior to the sleep study, all patients completed a standardized self-administered questionnaire comprising 120 items. The following information was collected: sociodemographic and anthropometric data (age, sex, weight, height, neck and waist circumference, occupational category), lifestyle factors (alcohol and tobacco consumption, physical activity, caffeine intake, recreational drug use, sleep habits, and daytime functioning), medication use (current treatments, sleep medications), medical history (general, Ear-Nose-Throat (ENT), respiratory, and cardiovascular comorbidities), Sleep-related symptoms (snoring, witnessed apneas, daytime sleepiness, morning headaches, nocturia, insomnia, memory impairment, decreased libido, restless legs syndrome), clinical information (reason for referral, reported symptoms, and quality of sleep) and standardized scales (Epworth Sleepiness Scale, Pichot Fatigue Scale for fatigue assessment, Pichot Depression Scale QD2A)\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSleep Study Recordings: Diagnostic data were obtained from either polysomnography or respiratory polygraphy, using the NOX software system. Parameters recorded were: Apnea-Hypopnea Index (AHI), Oxygen Desaturation Index (ODI), micro-arousal index and periodic Limb movement index (PLMI) when polysomnography.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eObstructive sleep apnea was defined as an apnea\u0026ndash;hypopnea index (AHI)\u0026thinsp;\u0026ge;\u0026thinsp;5 events/hour. Severity was classified as mild (5\u0026ndash;14.9 events/hour), moderate (15\u0026ndash;29.9 events/hour), and severe (\u0026ge;\u0026thinsp;30 events/hour).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were extracted using Microsoft Excel and exported to R software version 4.5.2 for all statistical analyses. We first conducted descriptive analyses to characterize patients according to OSAS severity, followed by analytical statistics.\u003c/p\u003e \u003cp\u003eContinuous variables were described using means with standard deviations (SD) and/or medians with interquartile ranges (IQR), depending on their distribution.\u003c/p\u003e \u003cp\u003eCategorical variables were presented as frequencies and percentages. For between-group comparisons, Student\u0026rsquo;s t-test was used for continuous variables, while Chi-square or Fisher\u0026rsquo;s exact tests were applied for categorical variables. Variables associated with moderate-to-severe OSAS at a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in bivariate analysis were included in a multivariable binary logistic regression model to identify independent factors associated with OSAS severity. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all tests.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was conducted in accordance with the World Medical Association Declaration of Helsinki and was approved by the Institutional Review Board of the Centre Hospitalier Universitaire de Martinique (approval number 2025/072). Patient consent was obtained through signed consent forms during consultations and information notices displayed in the Pulmonology Department.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn the study period, 213 consecutive patients were included in the study. Of these, 114 received polygraphy and 99 polysomnography.\u003c/p\u003e \u003cp\u003eThe study population was predominantly female (65.3% n\u0026thinsp;=\u0026thinsp;139) with a mean age of 56 years\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1 years\u003c/p\u003e \u003cp\u003eThe reasons for referral were mostly snoring: 61 (28,6%), and insomnia: 34 (16%) Excessive daytime sleepiness: 31 (14,6%), assessment before bariatric surgery 5 (2%), Other reasons for referral were post-stroke assessment, arrhythmia evaluation, and ophthalmologic pre-surgery assessment :15 (7%).\u003c/p\u003e \u003cp\u003eBaseline demographic and anthropometric characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Population and Factors Associated with Moderate-to-Severe OSAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;213\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild/non-OSAS N\u0026thinsp;=\u0026thinsp;97\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate/Severe OSAS N\u0026thinsp;=\u0026thinsp;116\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (\u003cspan citationid=\"CR17\" 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align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e33\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of OSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShift work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration on workdays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.43 (1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.54 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.35 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration on rest days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.17 (4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.99 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.31 (5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of hypnotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther treatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eMean (SD); n (%)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eWelch Two Sample t-test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe clinical symptom profile demonstrated a high prevalence of sleep-related complaints (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among active drivers, 13.7% (n\u0026thinsp;=\u0026thinsp;27) reported having avoided a motor vehicle accident due to sleepiness while driving.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSymptoms of the Study Population and Factors Associated with Moderate-to-Severe OSAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;213\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild/non-OSAS N\u0026thinsp;=\u0026thinsp;97\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate/Severe OSAS n\u0026thinsp;=\u0026thinsp;116\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime sleepiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime nap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e23\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWitnessed apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNocturnal awakening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNocturia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased libido\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorning headaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNight sweats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTired on waking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetter sleep on days off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eMean (SD); n (%)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eWelch Two Sample t-test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCardiovascular comorbidities were highly prevalent in the study population. Hypertension was the most common comorbidity, affecting 51%, followed by arrhythmias in 22.2% and heart failure in 13.1% (n\u0026thinsp;=\u0026thinsp;23). Metabolic disorders included hypercholesterolemia in 18.8% (n\u0026thinsp;=\u0026thinsp;34) and diabetes mellitus in 16.5% of cases (n\u0026thinsp;=\u0026thinsp;30) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComorbidities of the Study Population and Factors Associated with Moderate-to-Severe OSAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;213\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild/non-OSAS N\u0026thinsp;=\u0026thinsp;97\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate/Severe OSAS N\u0026thinsp;=\u0026thinsp;116\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e41\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic cough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e38\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e23\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTonsillectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenoidectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft palate surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptoplasty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e3 (1.7%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2 (2.4%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1 (1.0%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbinectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinus surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e33\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e40\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e26\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrhythmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAneurysm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e57\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e23\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGERD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e38\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e40\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthodontic treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaw pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e26\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaw dislocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e36\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcclusal splint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurosurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e36\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCranial trauma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlaucoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eMean (SD); n (%)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eWelch Two Sample t-test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: OSAS\u0026thinsp;=\u0026thinsp;obstructive sleep apnea syndrome; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; GERD\u0026thinsp;=\u0026thinsp;gastroesophageal reflux\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInsomnia symptoms were reported by 124 patients (62%), with sleep maintenance insomnia being the most common subtype, 53.3% (n\u0026thinsp;=\u0026thinsp;65). Among patients with confirmed SAS (n\u0026thinsp;=\u0026thinsp;189), COMISA (comorbid insomnia and sleep apnea) was identified in 57.7% of cases (n\u0026thinsp;=\u0026thinsp;109).\u003c/p\u003e \u003cp\u003eOSAS was confirmed in 189 patients (88.7%, 95%CI 84.4\u0026ndash;93.0%). Disease severity distribution was as follows: mild OSAS in 73 patients (38.6%, 95%CI 31.7\u0026ndash;45.6%), moderate OSAS in 56 patients (29.6%, 95% CI 23.1\u0026ndash;36.1%), and severe SAS in 60 patients (31.7%, 95% CI 25.1\u0026ndash;38.4%).\u003c/p\u003e \u003cp\u003eIn bivariate analysis, patients with moderate-to-severe OSAS were significantly older compared to those with mild/non-OSAS (mean age 58 vs 53 years, p\u0026thinsp;=\u0026thinsp;0.021). They presented higher BMI values (median 34 vs 29 kg/m\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;0.031), as well as higher weight 2 years ago and maximum lifetime weight. Neck circumference 41.6 cm vs 38.7 cm (p\u0026thinsp;=\u0026thinsp;0.010) and waist circumference 108 cm vs 100 cm (p\u0026thinsp;=\u0026thinsp;0.001) were also significantly greater in the moderate-to-severe OSAS group. Male sex was significantly associated with moderate-to-severe OSAS, representing 44% of this group compared to 24% in the mild/non-OSAS group (p\u0026thinsp;=\u0026thinsp;0.002). Snoring was significantly more prevalent in the moderate-to-severe OSAS group (p\u0026thinsp;=\u0026thinsp;0.020) with 72% (n\u0026thinsp;=\u0026thinsp;81) compared to the mild/non-OSAS group, 67% (n\u0026thinsp;=\u0026thinsp;60). Witnessed apneas were reported by 47% (n\u0026thinsp;=\u0026thinsp;48) of patients with moderate-to-severe OSAS versus 26% (n\u0026thinsp;=\u0026thinsp;24) in the mild/non-OSAS group (p\u0026thinsp;=\u0026thinsp;0.004). Similarly, decreased libido affected 53% (n\u0026thinsp;=\u0026thinsp;54) of patients with moderate-to-severe OSAS compared to 34% (n\u0026thinsp;=\u0026thinsp;30) in the mild/non-OSAS group (p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eIn multivariate analysis, factors independently associated with moderate-to-severe OSAS included male sex, neck circumference, snoring, nocturia and witnessed apnea (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent Factors Associated with Moderate-to-Severe OSAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42, 9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeck circumference (cm, per SD increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.21, 15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43, 8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNocturia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19, 6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWitnessed apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55, 9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval; OR\u0026thinsp;=\u0026thinsp;Odds Ratio; cm\u0026thinsp;=\u0026thinsp;centimeter; SD\u0026thinsp;=\u0026thinsp;standard deviation; BMI\u0026thinsp;=\u0026thinsp;body mass index ; ESS\u0026thinsp;=\u0026thinsp;Epworth scale score\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*Only variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in bivariate analysis were included in the model\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe principal findings of our study were: the high prevalence of OSAS, underdiagnosis in men, high prevalence of suggestive COMISA and male sex, increased neck circumference, snoring, nocturia, and witnessed apneas as independent risk factors for moderate-to-severe OSAS.\u003c/p\u003e \u003cp\u003eThis high prevalence reflected referral bias in specialized sleep centers, where patients often present with typical clinical symptoms, or cardiometabolic comorbidities that increase the probability of OSAS. A similar prevalence (from 88 to 91% for OSAS and 52% to 74% for moderate-to-severe OSAS) were reported in a Sleep Center in Saudi Arabia (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and Brazil (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This substantial proportion of moderate-to-severe OSAS confirms that patients referred for sleep testing frequently have clinically significant disease requiring active management. These findings reinforce the relevance of targeted pre-hospital screening and highlight the need for improved identification of at-risk individuals in primary care, to optimize referral pathways and reduce diagnostic delays.\u003c/p\u003e \u003cp\u003eThe demographic characteristics of our cohort provide important insight into potential diagnostic inequalities. Although women accounted for 65% of referrals to the sleep centre, moderate-to-severe OSAS was significantly more prevalent among men (44% vs 24%, p\u0026thinsp;=\u0026thinsp;0.002), in line with established epidemiological data (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This discordance suggests that men, despite being at higher risk for severe OSAS, are underrepresented in diagnostic pathways and tend to be diagnosed at a more advanced stage of disease. Similar sex-related disparities in healthcare utilization have been reported previously and may reflect differences in symptom perception, health-seeking behavior, or access to care. These findings highlight a potential gender-related diagnostic delay and underline the need for targeted screening strategies aimed at improving early identification of OSAS among men.\u003c/p\u003e \u003cp\u003eInsomnia symptoms were reported by 62% of patients, and among those with confirmed OSAS, comorbid insomnia and sleep apnea (COMISA) was identified in 57.7% (95% CI 54\u0026ndash;68%). This prevalence is consistent with the range reported in international studies, generally between 40% and 60% (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, substantial variability has been described across cohorts, with lower estimates reported in regional French populations, such as the Pays de la Loire cohort (14%) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and higher prevalences observed in large multicenter cohorts, including the ESADA cohort (51.7%)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In contrast, population-based data from Australia reported lower COMISA prevalence (10.2%) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), highlighting the influence of referral pathways and study design on prevalence estimates.\u003c/p\u003e \u003cp\u003eCOMISA has been consistently associated with greater daytime impairment, increased psychiatric and cardiovascular comorbidities (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and poorer adherence to continuous positive airway pressure therapy (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The high prevalence observed in our cohort therefore emphasizes the clinical relevance of systematic insomnia screening in patients referred for OSAS evaluation, particularly in specialized sleep centers where diagnostic delay and disease complexity are common.\u003c/p\u003e \u003cp\u003eOur findings have direct clinical and public health implications for the organization of OSAS screening and care pathways in European overseas territories. The identification of simple parameters of moderate-to-severe OSAS (male sex, increased neck circumference, snoring, nocturia and witnessed apneas) supports the use of pragmatic, low-cost screening approaches in primary care. These readily accessible clinical markers may help prioritize referrals for sleep testing in settings where diagnostic resources are limited and waiting times remain substantial.\u003c/p\u003e \u003cp\u003eThe marked under-representation of men in the referral population, despite a higher prevalence of severe disease, highlights a critical gender-related diagnostic gap. Targeted strategies aimed at improving early identification of OSAS in men, including opportunistic screening during cardiovascular or metabolic consultations, could contribute to reducing diagnostic delay and disease severity at presentation.\u003c/p\u003e \u003cp\u003eThe high prevalence of COMISA observed in this cohort further emphasizes the need for integrated sleep assessments. Systematic evaluation of insomnia symptoms in patients referred for suspected OSAS may improve diagnostic accuracy, inform therapeutic choice, and optimize adherence to continuous positive airway pressure therapy through early identification of patients requiring combined management strategies.\u003c/p\u003e \u003cp\u003eAt a public health level, these findings support the adaptation of national OSAS screening recommendations to the specific epidemiological and organizational context of overseas territories. Strengthening primary care training in sleep medicine, improving access to sleep investigations, and promoting decentralized screening strategies may enhance early diagnosis and reduce long-term cardiometabolic and neurocognitive complications associated with untreated OSAS.\u003c/p\u003e \u003cp\u003eStrengths, limitations and future directions\u003c/p\u003e \u003cp\u003eCertain limitations should be acknowledged. First, this study was conducted in a hospital-based population, with patients often symptomatic and referred to a tertiary reference center; therefore, the findings cannot be generalized to the general population. Second, the use of a self-administered questionnaire may have introduced recall bias, particularly in an older and comorbid population, where cognitive complaints were frequently reported.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study has notable strengths. To our knowledge, it provides the first detailed hospital-based epidemiological characterization of patients referred for suspected OSAS in a French overseas territory.\u003c/p\u003e \u003cp\u003eThese data offer a valuable foundation for the development of targeted screening strategies and public health policies adapted to the specific epidemiological and organisational context of Martinique. Future research should aim to validate these findings in community-based cohorts and to explore the feasibility of simplified predictive tools incorporating locally relevant risk factors. Comparative analyses across French overseas territories, including Guadeloupe and French Guiana, would further enhance the understanding of regional disparities and support the adaptation of national recommendations to overseas settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms a very high hospital-based prevalence of OSAS among patients referred for sleep evaluation in Martinique (88.7%), reflecting a substantial burden of clinically significant disease. It also highlights a marked gender-related diagnostic gap, as men (although representing a minority of referrals) presented with more moderate-to-severe forms, suggesting delayed diagnosis. Male sex, increased neck circumference, snoring, nocturia and witnessed apnea were independently associated with moderate-to-severe OSAS. These findings support the use of simple clinical markers to improve early identification of high-risk patients, particularly in primary care. Optimizing screening pathways through improved access to sleep investigations and targeted training of general practitioners in overseas territories may help reduce diagnostic delay and limit long-term complications of untreated OSAS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe study received approval from the Institutional Review Board of the University Hospitals of Martinique (approval number 2025/072).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eWritten informed consent was obtained from all participants and/or their legal guardian(s).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eNot applicable.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe authors declare that they have no competing interests.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eECM: Collected data, analyzed and interpreted the results, and drafted the manuscript.\u003c/li\u003e\n \u003cli\u003eMNS: performed data analysis and interpretation.\u003c/li\u003e\n \u003cli\u003eBA: Contributed to patient management.\u003c/li\u003e\n \u003cli\u003eAndreu Mathilde: Contributed to patient management.\u003c/li\u003e\n \u003cli\u003eIJ: Contributed to writing the manuscript\u003c/li\u003e\n \u003cli\u003eDufeal Marion : Contributed to patient management.\u003c/li\u003e\n \u003cli\u003eDramé Moustapha: Contributed to study conception and data analysis.\u003c/li\u003e\n \u003cli\u003eAgossou Moustapha: Conceived the study, analyzed and interpreted the results, and drafted the manuscript.\u003c/li\u003e\n \u003cli\u003eAll authors read and approved the final manuscript.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgements\u003cbr\u003e\u0026nbsp;\u003c/u\u003e\u003c/strong\u003eWe thank our day hospital team for their invaluable contribution to this study: Christelle Fraudin, Monique Morency, Maurane Soutarson, Elodie Mondésir, Malika Crispin, and our nurse manager Emilie Geslin.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eYoshihisa A, Takeishi Y. Sleep Disordered Breathing and Cardiovascular Diseases. J Atheroscler Thromb. 2019;26(4):315\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eBorel AL, Tamisier R, B\u0026ouml;hme P, Priou P, Avignon A, Benhamou PY, et al. [Reprint of : Management of obstructive sleep apnea syndrome in people living with diabetes: context, screening, indications and treatment modalities: context, screening, indications and treatment modalities: a French position statement]. Rev Mal Respir. 2018;35(10):1067\u0026ndash;89.\u003c/li\u003e\n \u003cli\u003eFang H, Tu S, Sheng J, Shao A. Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment. J Cell Mol Med. 2019;23(4):2324\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eFuhrman C, DELMAS M, DRUET C, BOUSSAC ZAREBSKA M, FLEURY B, NGUYEN X. Le syndrome d\u0026apos;apn\u0026eacute;es du sommeil en France: un syndrome fr\u0026eacute;quent et sous-diagnostiqu\u0026eacute;. Bulletin \u0026eacute;pid\u0026eacute;miologique hebdomadaire. 2012(44-45):510\u0026ndash;4.\u003c/li\u003e\n \u003cli\u003eMeslier N, Vol S, Balkau B, Gagnadoux F, Cailleau M, Petrella A, et al. [Prevalence of symptoms of sleep apnea syndrome. Study of a French middle-aged population]. Rev Mal Respir. 2007;24(3 Pt 1):305\u0026ndash;13.\u003c/li\u003e\n \u003cli\u003ede Sant\u0026eacute; HA. \u0026Eacute;valuation clinique et \u0026eacute;conomique des dispositifs m\u0026eacute;dicaux et prestations associ\u0026eacute;es pour prise en charge du syndrome d\u0026rsquo;apn\u0026eacute;es hypopn\u0026eacute;es obstructives du sommeil (SAHOS). R\u0026eacute;vision de cat\u0026eacute;gories homog\u0026egrave;nes de dispositifs m\u0026eacute;dicaux\u0026ndash;Volet m\u0026eacute;dico-technique et \u0026eacute;valuation \u0026eacute;conomique Saint-Denis La Plaine: HAS. 2014:198.\u003c/li\u003e\n \u003cli\u003eParati G, Lombardi C, Narkiewicz K. Sleep apnea: epidemiology, pathophysiology, and relation to cardiovascular risk. Am J Physiol Regul Integr Comp Physiol. 2007;293(4):R1671\u0026ndash;83.\u003c/li\u003e\n \u003cli\u003eMandereau-Bruno L, L\u0026eacute;ger D, Delmas MC. Obstructive sleep apnea: A sharp increase in the prevalence of patients treated with nasal CPAP over the last decade in France. PLoS One. 2021;16(1):e0245392.\u003c/li\u003e\n \u003cli\u003eDaigre JL, Atallah A, Boissin JL, Jean-Baptiste G, Kangambega P, Chevalier H, et al. The prevalence of overweight and obesity, and distribution of waist circumference, in adults and children in the French Overseas Territories: the PODIUM survey. Diabetes Metab. 2012;38(5):404\u0026ndash;11.\u003c/li\u003e\n \u003cli\u003eAtallah A, Atallah V, Daigre JL, Boissin JL, Kangambega P, Larifla L, et al. [High blood pressure and obesity: disparities among four French overseas territories]. 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Insomnia symptoms combined with nocturnal hypoxia associate with cardiovascular comorbidity in the European sleep apnea cohort (ESADA). Sleep Breath. 2019;23(3):805\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eSweetman A, Melaku YA, Lack L, Reynolds A, Gill TK, Adams R, et al. Prevalence and associations of co-morbid insomnia and sleep apnoea in an Australian population-based sample. Sleep Med. 2021;82:9\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eSaaresranta T, Hedner J, Bonsignore MR, Riha RL, McNicholas WT, Penzel T, et al. Clinical Phenotypes and Comorbidity in European Sleep Apnoea Patients. PLoS One. 2016;11(10):e0163439.\u003c/li\u003e\n \u003cli\u003eSweetman A, Lack L, McEvoy RD, Catcheside PG, Antic NA, Chai-Coetzer CL, et al. Effect of depression, anxiety, and stress symptoms on response to cognitive behavioral therapy for insomnia in patients with comorbid insomnia and sleep apnea: a randomized controlled trial. J Clin Sleep Med. 2021;17(3):545\u0026ndash;54.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"obstructive sleep apnea, insomnia, French overseas territory, COMISA, healthcare inequity","lastPublishedDoi":"10.21203/rs.3.rs-8569681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8569681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eObstructive sleep apnea syndrome (OSAS) remains widely underdiagnosed and undertreated. In Martinique, a French overseas territory, the prevalence of continuous positive airway pressure (CPAP) therapy is the highest in France, suggesting a substantial burden of moderate-to-severe disease. The objective of this study was to assess the prevalence of OSAS and to identify factors associated with moderate-to-severe OSAS in this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted an observational study including 213 consecutive adults evaluated at the Sleep Unit of the University Hospital of Martinique between July 2024 and July 2025. Moderate-to-severe OSAS was defined in symptomatic patients by an apnea\u0026ndash;hypopnea index (AHI)\u0026thinsp;\u0026ge;\u0026thinsp;15 events/hour.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOSAS (AHI\u0026thinsp;\u0026ge;\u0026thinsp;5 events/hour) was diagnosed in 189 patients, corresponding to a prevalence of 88.7% (95% CI 84.4\u0026ndash;93.0%). Moderate-to-severe disease was observed in 61.3% of cases. Although women accounted for 65% of referrals, men more frequently presented with moderate-to-severe OSAS. Insomnia symptoms were reported by 62% of patients, and comorbid insomnia and sleep apnea (COMISA) was identified in 57.7% of those with confirmed OSAS. In multivariable analysis, male sex, increased neck circumference, snoring, nocturia, and witnessed apneas were independently associated with moderate-to-severe OSAS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrates a very high hospital-based prevalence of OSAS in Martinique and reveals a marked gender-related diagnostic gap. Simple clinical markers may facilitate earlier identification of patients at high risk for moderate-to-severe OSAS.\u003c/p\u003e","manuscriptTitle":"Obstructive Sleep Apnea Hypopnea Syndrome (OSAS) in a French overseas territory: a cross-sectional study in Martinique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 08:51:59","doi":"10.21203/rs.3.rs-8569681/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-09T15:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268909339844055943773815104666176604774","date":"2026-02-06T15:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T16:43:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T13:46:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T02:54:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T02:54:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-01-10T16:54:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7dd465e3-b340-4cd4-accb-fb566ca227b8","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T08:51:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 08:51:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8569681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8569681","identity":"rs-8569681","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}