Risk factors associated with pulmonary hypertension in obesity hypoventilation syndrome
2021; American Academy of Sleep Medicine; Volume: 18; Issue: 4 Linguagem: Inglês
10.5664/jcsm.9760
ISSN1550-9397
AutoresJuan F. Masa, Iván D. Benítez, Shahrokh Javaheri, María V. Mogollón, Maria Á. Sánchez-Quiroga, F.J. Gómez de Terreros, Jaime Corral, R. Gallego, Odile Romero, Candela Caballero‐Eraso, Estrella Ordax-Carbajo, María F. Troncoso, Mónica González, Soledad López-Martín, José M. Marı́n, Sergi Martí, Trinidad Díaz-Cambriles, Eusebi Chiner, Carlos Egea, Javier Barca, Ferrán Barbé, Babak Mokhlesi, Isabel Utrabo, Nicolás González‐Mangado, Maria A. Martinez-Martinez, Elena Ojeda-Castillejo, Daniel López‐Padilla, Santiago Carrizo, Begoña Carazo Gallego, Mercedes Pallero, Odile Romero, María Antonia Ramón, Eva Arias, Jesús Muñoz-Méndez, Cristina Senent, José N. Sancho-Chust, Nieves B. Navarro-Soriano, Emilia Barrot, José María Benítez, Jesús Sánchez-Gómez, Rafael Golpe, M.A. Gómez Mendieta, Sílvia Gómez, Mónica Bengoa,
Tópico(s)Cardiovascular Function and Risk Factors
ResumoFree AccessScientific InvestigationsRisk factors associated with pulmonary hypertension in obesity hypoventilation syndrome Juan F. Masa, MD, PhD, Iván D. Benítez, MStat, Shahrokh Javaheri, MD, Maria Victoria Mogollon, MD, Maria Á. Sánchez-Quiroga, MD, Francisco J. Gomez de Terreros, MD, PhD, Jaime Corral, MD, Rocio Gallego, MD, Auxiliadora Romero, MD, Candela Caballero-Eraso, MD, PhD, Estrella Ordax-Carbajo, MD, PhD, María F. Troncoso, MD, PhD, Mónica González, MD, PhD, Soledad López-Martín, MD, José M. Marin, MD, PhD, Sergi Martí, MD, PhD, Trinidad Díaz-Cambriles, MD, Eusebi Chiner, MD, PhD, Carlos Egea, MD, PhD, Javier Barca, MD, Ferrán Barbé, MD, PhD, Babak Mokhlesi, MD, MSc, on behalf of the Spanish Sleep Network, Isabel Utrabo, MD, Nicolás González-Mangado, MD, PhD, Maria A. Martinez-Martinez, MD, Elena Ojeda-Castillejo, MD, Daniel López-Padilla, MD, Santiago J. Carrizo, MD, PhD, Prof, Begoña Gallego, MD, PhD, Mercedes Pallero, MD, Odile Romero, MD, Maria A. Ramón, PT, MSc, Eva Arias, MD, Jesús Muñoz-Méndez, MD, PhD, Cristina Senent, MD, PhD, Jose N. Sancho-Chust, MD, PhD, Nieves B. Navarro-Soriano, MD, Emilia Barrot, MD, PhD, José M. Benítez, MD, Jesús Sanchez-Gómez, MD, Rafael Golpe, MD, PhD, María A. Gómez-Mendieta, MD, PhD, Silvia Gomez, MD, Mónica Bengoa, MD Juan F. Masa, MD, PhD Address correspondence to: Juan F. Masa, MD, PhD, C/Rafael Alberti 12, 10005 Cáceres, Spain; Tel: 0034927256296; Fax: 0034927256297; Email: E-mail Address: [email protected] Respiratory Department, San Pedro de Alcántara Hospital, Cáceres, Spain CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE) , Iván D. Benítez, MStat CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Institut de Recerca Biomédica de Lleida (IRBLLEIDA), Lleida, Spain , Shahrokh Javaheri, MD Division of Pulmonary and Sleep Medicine, Bethesda North Hospital, Cincinnati, Ohio , Maria Victoria Mogollon, MD Cardiology Department, San Pedro de Alcántara Hospital, Cáceres, Spain , Maria Á. Sánchez-Quiroga, MD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE) Respiratory Department, Virgen del Puerto Hospital, Plasencia, Cáceres, Spain , Francisco J. Gomez de Terreros, MD, PhD Respiratory Department, San Pedro de Alcántara Hospital, Cáceres, Spain CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE) , Jaime Corral, MD Respiratory Department, San Pedro de Alcántara Hospital, Cáceres, Spain CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE) , Rocio Gallego, MD Respiratory Department, San Pedro de Alcántara Hospital, Cáceres, Spain CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE) , Auxiliadora Romero, MD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Unidad Médico-Quirúrgica de Enfermedades Respiratorias, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, Sevilla, Spain , Candela Caballero-Eraso, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Unidad Médico-Quirúrgica de Enfermedades Respiratorias, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, Sevilla, Spain , Estrella Ordax-Carbajo, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Respiratory Department, University Hospital, Burgos, Spain , María F. Troncoso, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Respiratory Department, IIS Fundación Jiménez Díaz, Madrid, Spain , Mónica González, MD, PhD Respiratory Department, Valdecilla Hospital, Santander, Spain , Soledad López-Martín, MD Respiratory Department, Gregorio Marañón Hospital, Madrid, Spain , José M. Marin, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Respiratory Department, Miguel Servet Hospital, Zaragoza, Spain , Sergi Martí, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Respiratory Department, Vall d'Hebron Hospital, Barcelona, Spain , Trinidad Díaz-Cambriles, MD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Respiratory Department, Doce de Octubre Hospital, Madrid, Spain , Eusebi Chiner, MD, PhD Respiratory Department, San Juan Hospital, Alicante, Spain , Carlos Egea, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Respiratory Department, Alava University Hospital IRB, Vitoria, Spain , Javier Barca, MD Instituto Universitario de Investigación Biosanitaria de Extremadura (INUBE) Nursing Department, Extremadura University, Cáceres, Spain , Ferrán Barbé, MD, PhD CIBER de Enfermedades Respiratorias (CIBERES), Madrid, Spain Institut de Recerca Biomédica de Lleida (IRBLLEIDA), Lleida, Spain , Babak Mokhlesi, MD, MSc Medicine/Pulmonary and Critical Care, University of Chicago, Illinois , on behalf of the Spanish Sleep Network , Isabel Utrabo, MD , Nicolás González-Mangado, MD, PhD , Maria A. Martinez-Martinez, MD , Elena Ojeda-Castillejo, MD , Daniel López-Padilla, MD , Santiago J. Carrizo, MD, PhD, Prof , Begoña Gallego, MD, PhD , Mercedes Pallero, MD , Odile Romero, MD , Maria A. Ramón, PT, MSc , Eva Arias, MD , Jesús Muñoz-Méndez, MD, PhD , Cristina Senent, MD, PhD , Jose N. Sancho-Chust, MD, PhD , Nieves B. Navarro-Soriano, MD , Emilia Barrot, MD, PhD , José M. Benítez, MD , Jesús Sanchez-Gómez, MD , Rafael Golpe, MD, PhD , María A. Gómez-Mendieta, MD, PhD , Silvia Gomez, MD , Mónica Bengoa, MD Published Online:April 1, 2022https://doi.org/10.5664/jcsm.9760Cited by:2SectionsAbstractEpubPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:Pulmonary hypertension (PH) is prevalent in obesity hypoventilation syndrome (OHS). However, there is a paucity of data assessing pathogenic factors associated with PH. Our objective is to assess risk factors that may be involved in the pathogenesis of PH in untreated OHS.Methods:In a post hoc analysis of the Pickwick trial, we performed a bivariate analysis of baseline characteristics between patients with and without PH. Variables with a P value ≤ .10 were defined as potential risk factors and were grouped by theoretical pathogenic mechanisms in several adjusted models. Similar analysis was carried out for the 2 OHS phenotypes, with and without severe concomitant obstructive sleep apnea.Results:Of 246 patients with OHS, 122 (50%) had echocardiographic evidence of PH defined as systolic pulmonary artery pressure ≥ 40 mm Hg. Lower levels of awake PaO2 and higher body mass index were independent risk factors in the multivariate model, with a negative and positive adjusted linear association, respectively (adjusted odds ratio 0.96; 95% confidence interval 0.93 to 0.98; P = .003 for PaO2, and 1.07; 95% confidence interval 1.03 to 1.12; P = .001 for body mass index). In separate analyses, body mass index and PaO2 were independent risk factors in the severe obstructive sleep apnea phenotype, whereas body mass index and peak in-flow velocity in early/late diastole ratio were independent risk factors in the nonsevere obstructive sleep apnea phenotype.Conclusions:This study identifies obesity per se as a major independent risk factor for PH, regardless of OHS phenotype. Therapeutic interventions targeting weight loss may play a critical role in improving PH in this patient population.Clinical Trial Registration:Registry: Clinicaltrial.gov; Name: Alternative of Treatment in Obesity Hypoventilation Syndrome; URL: https://clinicaltrials.gov/ct2/show/NCT01405976; Identifier: NCT01405976.Citation:Masa JF, Benítez ID, Javaheri S, et al. Risk factors associated with pulmonary hypertension in obesity hypoventilation syndrome. J Clin Sleep Med. 2022;18(4):983–992.BRIEF SUMMARYCurrent Knowledge/Study Rationale: What are the risk factors associated with pulmonary hypertension (PH) in untreated obesity hypoventilation syndrome (OHS), and do these risk factors vary based on OHS phenotype? There are no systematic large-scale studies examining mechanistic risk factors associated with PH in untreated OHS.Study Impact: This is the first cross-sectional study evaluating potential pathophysiological risk factors for PH in a large sample of patients with untreated OHS. Obesity is a shared risk factor for both OHS phenotypes, with or without severe obstructive sleep apnea, suggesting weight loss may play a critical role in improving PH. The mechanisms implicating obesity with PH are discussed.INTRODUCTIONObesity hypoventilation syndrome (OHS) is defined by obesity, daytime hypercapnia, and presence of sleep-disordered breathing, after excluding other causes of hypoventilation.1 Concomitant obstructive sleep apnea (OSA) is common (90%),2 and 73% of patients with OHS also have severe OSA.3 Compared with patients with eucapnic OSA4,5 and eucapnia with obesity, patients with OHS have a higher prevalence of cardiovascular and respiratory morbidity, hospitalization, health care resource utilization, and overall mortality.6–17Pulmonary hypertension (PH) is frequently under recognized in patients with OHS. In the largest randomized clinical trial to date (Pickwick study), approximately 8% of patients had been diagnosed with PH upon study enrollment. However, baseline assessment revealed that nearly 50% of the patients had echocardiographic evidence of PH defined as pulmonary artery systolic pressure ≥ 40 mm Hg.18 Other observational studies have also reported a high prevalence of PH in patients with OHS (from 52% to 88%).2,19–22From a mechanistic standpoint, there are several pathogenic conditions that could be involved in the increased risk of PH in patients with OHS. These include presence of OSA with associated nocturnal hypoxemia and hypercapnia promoting pulmonary arteriolar vasoconstriction,23 awake arterial blood hypoxemia and hypercapnia, and left ventricular diastolic dysfunction elevating left ventricular end-diastolic pressure.24 Furthermore, obesity per se is an inflammatory condition and its associated comorbidities (ie, hypertension and insulin resistance) may further contribute to PH.25Despite the overwhelming pathophysiological mechanisms linking OHS to PH, to our knowledge, there are no systematic large-scale studies examining risk factors associated with PH in untreated OHS. We hypothesized that the presence of PH is associated with the severity of hypoxemia and hypercapnia, severity of OSA, degree of obesity, and presence of diastolic dysfunction. To test our hypothesis, we performed a cross-sectional analysis of the baseline data of patients with untreated OHS, with and without concomitant severe OSA phenotypes, enrolled in the Pickwick randomized controlled trials.3,17,18,26–31METHODSTrial designWe carried out a cross-sectional study of a multicenter, open-label, randomized clinical trial with 2 parallel groups conducted at 16 clinical sites in Spain.ParticipantsFrom May 2009 to March 2013, we successively screened patients between 15 and 80 years of age who were referred for pulmonary consultations due to suspected OHS or OSA at 16 tertiary hospitals in Spain (see supplemental material). OHS was defined as obesity, with a body mass index (BMI) ≥ 30 kg/m2, stable hypercapnic respiratory failure (Partial pressure of carbon dioxide in the arterial blood (PaCO2) ≥ 45 mm Hg, pH ≥ 7.35, and no clinical exacerbation during the previous 2 months), no noteworthy spirometric evidence of chronic obstructive pulmonary disease (forced expiratory volume in the first second had to be above 70% of predicted in cases where forced expiratory volume in the first second/forced vital capacity was below 70), and no evidence of neuromuscular, chest wall, or metabolic disease to explain hypercapnia. Other inclusion criteria were absence of narcolepsy or restless legs syndrome and a correctly executed 30-minute continuous positive airway pressure/noninvasive ventilation treatment test (see supplemental material). The exclusion criteria were as follows: (1) a psycho-physical inability to complete questionnaires, (2) severe chronic debilitating illness, (3) severe chronic nasal obstruction, and (4) a lack of informed consent.The Pickwick project comprised 2 parallel randomized clinical trials based on the presence of severe OSA [apnea-hypopnea index (AHI) ≥ 30 events/h] or nonsevere OSA (AHI < 30 events/h). Both randomized clinical trials were conducted in 2 phases29 (see figure S1 in supplemental material). In the present cross-sectional analysis, we used data obtained during the baseline evaluation of patients enrolled in both randomized controlled trials who had adequate measure of systolic pulmonary artery pressure obtained from baseline conventional transthoracic echocardiogram (Figure 1). The study was approved by the ethics committees of all 16 centers, and written informed consent was obtained from all patients.Figure 1: Study flowchart.Flow chart of the study protocol. Of 375 selected patients, 56 were excluded and 319 were randomized: 221 in the severe OSA trial and 98 in the nonsevere OSA trial. Of this 319, 73 were excluded because of inadequate echocardiogram. Of the remaining 246, 122 had and 124 did not have PH. Of the 122 with PH, 87 had severe OSA and 35 had nonsevere OSA who were included in the subgroup analysis, severe OSA and nonsevere OSA, respectively. Of the 124 without PH, 80 had severe OSA and 44 had nonsevere OSA who were included in the subgroup analysis, severe OSA and nonsevere OSA, respectively. Therefore, the severe OSA subgroup had 167 patients and nonsevere OSA had 79 patients. ECHO = transthoracic echocardiogram, OSA = obstructive sleep apnea, PH = pulmonary hypertension, RCT = randomized controlled trial, sPAP = systolic pulmonary artery pressure.Download FigureOutcomesAt baseline visit we assessed anthropometric data; smoking habits and alcohol consumption; arterial blood gases during wakefulness while resting and breathing ambient air to assess PaCO2, partial pressure of oxygen in arterial blood (PaO2), pH, and calculated bicarbonate (see page 7 in supplemental material); sphygmomanometric blood pressure32 (see supplemental material); spirometry,33 6-minute walk distance test;34 sleepiness based on the Epworth Sleepiness Scale; comorbidities (hypertension, diabetes, dyslipidemia, ischemic cardiomyopathy, chronic heart failure, stroke, pulmonary hypertension, cardiac arrhythmia, and leg arteriopathy) obtained from the electronic medical records and during face-to-face interview with patients; health-related quality of life tests using the Functional Outcomes of Sleep Questionnaire, the Medical Outcome Survey Short Form 36, the visual analog well-being scale,35,36 oxygen therapy, conventional polysomnography (see pages 8 and 9 in the supplemental material), and conventional transthoracic echocardiogram.EchocardiographyAll 2-dimensional (2D) and Doppler echocardiograms were recorded using available echocardiographic equipment in each center (see supplemental material). Left ventricle size and wall thickness were measured according to international guidelines.37 All these parameters were derived from 2D-guided M-mode imaging or from linear measurements obtained from 2D images. Left ventricle ejection fraction was calculated from end diastolic and end systolic volume estimates, using volumetric measurements. Left ventricle mass was estimated by linear method with Cube's formula (0.8 × 1.04 × [(IVS + LVDD + LVPW)3 − LVDD3] + 0.6 g), where LVDD is the diastolic diameter of left ventricle (internal diameter), LVPW is the left ventricle posterior wall thickness, and IVS is the interventricular septum thickness, all measured at end-diastole in the long axis parasternal view using either 2D-guided M-mode or linear measurements from 2D echocardiographic images. Left ventricle mass index was obtained dividing the left ventricle mass by the body surface area according to Dubois' formula. To assess left ventricle diastolic function, we obtained peak early (E), peak atrial (A) mitral valve in-flow velocities, as well as their ratio (E/A), deceleration time and the antero-posterior diameter of the left atrium measured in the parasternal long-axis view. Systolic pulmonary artery pressure was assessed from the maximum velocity of tricuspid regurgitation signal (continuous wave Doppler) by the addition of right atrial pressure, estimated from inferior cava vein and its collapsibility. Right ventricular systolic function was evaluated using the right ventricle index of myocardial performance index.Statistical analysisBaseline bivariate analysis between the presence of PH and anthropometric, clinical, polysomnographic, and echocardiographic characteristics was carried out. We considered a systolic pulmonary artery pressure ≥ 40 mm Hg as evidence of PH.30We performed an adjusted logistic regression model with presence of PH as the dependent variable, and the predictors included in the model were variables that showed a P value < .10 in bivariate analysis between patients with and without PH. Recognizing that certain independent variables of interest can be highly correlated with each other, we used data from the literature, as well as biologic plausibility, to identify the variables best suited for the models. Although age was not statistically different in bivariate analysis, it was included in the models as a confounding risk factor.38 The 6-minute-walk distance test as well as the right ventricular systolic function based in the right ventricle index of myocardial performance index were not included in the model because they are more likely a consequence of PH. The oxygen desaturation index (ODI), AHI, and arousal index were log-transformed to normalize their distribution. A generalized additive model with penalized cubic regression spline was used to evaluate the type of association between each potential risk factor and the presence of PH. The mean oxygen saturation during sleep and PaCO2 were categorized by tertiles after observing a nonlinear relationship with the risk of PH.We created models including variables according to a pathogenic perspective as follows: (1) obesity, ie, BMI and spirometric parameters; (2) persistent hypoxemia, ie, PaO2 and oxygenation parameters during sleep; (3) OSA, ie, polysomnographic OSA-related parameters such as arousal index, AHI, and ODI; (4) hypoventilation, ie, PaCO2; and (5) postcapillary mechanism, ie, diastolic and systolic dysfunction parameters based on echocardiography. To assess all pathogenic groups together, a process of selection of potential risk factors using a relaxed Least Absolute Shrinkage and Selection Operator (LASSO) model was carried out.39 A 5-fold cross validation was carried out to select the lambda parameter of the LASSO model. Lambda was selected as the largest at which the mean square error was within 1 standard error of the minimal mean square error. A Spearman correlation test between independent risk factors based on the LASSO analysis with the rest of the variables was performed. To perform LASSO analysis, missing values were replaced by the means of the nonmissing values. A similar analysis was repeated for the 2 recognized OHS phenotypes (subgroup analysis), OHS with severe OSA and OHS without severe OSA.Data management and statistical analyses were performed using R software (R Core Team 2017, Version 3.4.2, Vienna, Austria) and SPSS software (IBM-SPSS Statistics, Version 22.0. IBM Corp., Armonk, NY).RESULTSStudy participantsOf the 375 patients who met inclusion criteria, 56 were excluded (Figure 1). Of the 319 remaining patients, 73 (23%) were excluded due to lack of appropriate echocardiograms. Of the 246 available for analysis, 122 (49.6%) had evidence of PH. The median of systolic pulmonary artery pressure in the group without PH was 34.0 mm Hg (28.0–36.4) vs 46.0 mmHg (43.0–55.8) in the PH group. Table 1 summarizes the bivariate analysis of baseline characteristics and polysomnographic and echocardiographic parameters based on PH status. Patients with PH were more obese, had higher AHI, lower PaO2, slightly higher PaCO2, and worse exercise tolerance and right ventricular systolic function.Table 1 Baseline characteristics of patients with and without PH.No PH (n = 124)PH (n = 122)Unadjusted Odds Ratio (95% CI)PAge, years66.0 [55.0;73.0]66.0 [56.0;72.0]1.00 [0.98;1.02].825Sex, female83 (66.9%)79 (64.8%)0.91 [0.53;1.54].721Smokers28 (22.6%)21 (17.2%)0.71 [0.38;1.34].298Alcohol drinkers‡20 (16.3%)21 (17.2%)1.07 [0.54;2.11].844BMI, kg/m239.1 [35.7;45.1]43.6 [38.1;49.2]1.08 [1.04;1.12]< .001ESS9.00 [6.00;13.0]10.0 [7.00;15.0]1.03 [0.98;1.08].273FOSQ76.0 [58.8;90.2]73.0 [57.2;88.0]1.00 [0.98;1.01].610SF 36-Physical38.2 [29.9;45.0]34.3 [27.5;43.5]0.98 [0.96;1.01].191SF 36-Mental43.3 [33.6;53.5]44.9 [31.9;52.5]1.00 [0.98;1.02].898VAWS50.0 [37.9;66.0]48.5 [39.0;58.3]0.99 [0.98;1.01].409Hypertension85 (69.1%)87 (71.3%)1.11 [0.64;1.93].709Systolic BP, mm Hg138 [130;142]140 [130;148]1.00 [0.99;1.02].598Diastolic BP, mm Hg80.0 [70.0;85.2]80.0 [70.0;90.0]1.00 [0.98;1.02].826Diabetes44 (35.5%)50 (41.0%)1.26 [0.75;2.12].379Dyslipidemia57 (46.0%)53 (43.8%)0.92 [0.55;1.52].735Stroke7 (5.65%)9 (7.38%)1.32 [0.47;3.89].595Ischemic heart disease10 (8.06%)9 (7.44%)0.92 [0.35;2.39].860Arrhythmia8 (6.45%)12 (9.92%)1.58 [0.62;4.24].335Chronic heart failure20 (16.1%)24 (19.8%)1.28 [0.67;2.50].456Leg arteriopathy12 (9.76%)7 (5.79%)0.57 [0.20;1.50].260Pulmonary hypertension diagnosis7 (5.65%)18 (14.9%)2.87 [1.19;7.75].018pH7.40 [7.38;7.42]7.40 [7.38;7.42]0.42 [0.00;730].822PaO2, mm Hg64.0 [57.7;71.0]59.0 [55.0;64.0]0.95 [0.93;0.98].001PaCO2, mm Hg49.0 [47.0;51.0]50.0 [48.0;53.1]1.09 [1.02;1.17].008Bicarbonate, mmol/L29.6 [27.7;31.4]29.1 [28.0;31.0]1.03 [0.95;1.11].537FEV1, % of predicted80.0 [67.8;91.2]75.0 [63.0;84.0]0.99 [0.98;1.00].079FVC, % of predicted81.2 (19.8)77.8 (21.1)0.99 [0.98;1.00].1836-MWD, meters377 (104)334 (132)1.00 [0.99;1.00].010C-reactive protein, mg/L0.94 [0.59;1.72]1.00 [0.70;2.00]1.04 [0.84;1.28].725Polysomnographic parameters† TST, hours5.44 (1.41)5.24 (1.22)0.89 [0.74;1.08].248 Sleep efficiency, %74.1 [61.8;86.1]72.3 [60.7;81.3]1.00 [0.98;1.01].739 Non-REM 1 and 2, %78.0 [65.1;88.4]84.1 [70.3;91.6]1.02 [1.00;1.03].084 On-REM 3, %10.2 [3.00;21.3]6.15 [0.23;16.9]0.98 [0.96;1.00].118 REM sleep, %9.60 [4.22;14.7]8.60 [2.86;14.7]0.99 [0.96;1.01].317 Arousal index36.0 [17.8;62.2]49.8 [26.0;75.4]1.01 [1.00;1.02].014 AHI43.2 [17.6;75.5]51.8 [23.8;88.8]1.01 [1.00;1.01].052 Severe OSA, %‡80 (64.5%)87 (71.3%)1.36 [0.80;2.35].258 ODI49.8 [21.4;78.2]57.8 [28.9;91.8]1.01 [1.00;1.02].029 Mean SpO287.0 [82.8;90.0]85.0 [82.0;89.0]0.96 [0.92;1.01].097 TST with SpO2 < 90%, %75.0 [44.5;94.8]77.0 [49.9;95.6]1.00 [1.00;1.01].374Oxygen therapy¶27 (21.8%)37 (30.3%)1.56 [0.88;2.80].130Echocardiogram parameters TEI0.36 [0.26;0.49]0.39 [0.28;0.53]4.43 [1.03;19.1].046 E/A ratio0.85 [0.71;1.04]0.88 [0.72;1.09]1.01 [0.44;2.33].975 Deceleration time, ms232 [196;261]222 [190;265]1.00 [1.00;1.00].623 Left atrial diameter, mm41.0 [36.0;46.0]43.0 [39.0;47.0]1.02 [0.99;1.04].245 LVTDD, mm48.9 [43.1;52.0]48.6 [44.0;53.0]1.02 [0.98;1.06].255 LVTSD, mm30.4 [27.0;34.0]31.0 [27.0;35.0]1.02 [0.98;1.05].405 LVEF, %64.0 [60.0;70.0]64.0 [59.0;69.0]0.98 [0.96;1.01].240 Left ventricular mass index, g/m3109 [85.8;123]109 [93.7;127]1.00 [1.00;1.01].293Data are presented as n (%), median (25;75 IQR), or mean (SD). †Polysomnography was performed in baseline conditions without CPAP/NIV or oxygen therapy in-place. ‡Severe OSA means an AHI ≥ 30 events/h. ¶Oxygen therapy was prescribed during the baseline visit. AHI = apnea-hypopnea index, BMI = body mass index, BP = blood pressure, CI = confidence interval, CPAP = continuous positive airway pressure, E/A= E and A waves, ESS = Epworth Sleepiness Scale, FEV1 = forced expiratory volume in the first second, FVC = forced vital capacity, IQR = interquartile range, LVEF = left ventricular ejection fraction, LVTDD = left ventricular telediastolic diameter, LVTSD = left ventricular telesystolic diameter, NIV = noninvasive ventilation, ODI = 3% oxygen desaturation index, OSA = obstructive sleep apnea, PaCO2 = Partial pressure of carbon dioxide in the arterial blood, PaO2 = pressure of oxygen in arterial blood, PH = pulmonary hypertension (systolic pulmonary artery pressure ≥ 40 mm Hg), REM = rapid eye movement, SD = standard deviation, SpO2 = oxygen saturation by pulse oximetry, TEI = right ventricle index of myocardial performance, TST = total sleep time, VAWS = visual analog well-being scale; 6-MWD = 6-minute walk distance.Table 2 shows the potentially relevant risk factors from pathogenic age-adjusted and multivariate model.Table 2 Association between pathogenic mechanisms and pulmonary hypertension.Age-Adjusted ModelMultivariate Model*OR (95% CI)POR (95% CI)PObesity BMI, kg/m21.08 (1.04;1.13)<.0011.07 (1.03;1.12).001 FEV1, % of predicted0.98 (0.97;0.99).013Sustained hypoxemia PaO2, mm Hg0.95 (0.92;0.98).0010.96 (0.93;0.98).003 Mean SpO2 during sleep First tertileReference Second tertile0.67 (0.35;1.26).214 Third tertile0.7 (0.38;1.31).269OSA Arousal index†1.18 (1.03;1.36)‡.017 Oxygen desaturation index†1.13 (1.01;1.27)‡.049 Apnea-hypopnea index†1.09 (0.98;1.21)‡.1 Non-REM 1 and 2, %1.02 (1.00;1.03).079Hypoventilation PaCO2, mm Hg First tertileReference Second tertile1.23 (0.66;2.30).512 Third tertile2.2 (1.18;4.12).013*Selection variables using Least Absolute Shrinkage and Selection Operator (LASSO). †Log-transformed variable was used. ‡OR for a 50% increase in variable. Mean SpO2 during sleep and PaCO2 were categorized by tertiles due to their nonlinear relationship with the presence of pulmonary hypertension (see Figure S3). BMI = body mass index, CI = confidence interval, FEV1 = forced expiratory volume in the first second, OR = odd ratio, OSA = obstructive sleep apnea, PaCO2 = Partial pressure of carbon dioxide in the arterial blood, PaO2 = pressure of oxygen in arterial blood, REM = rapid eye movement, SpO2 = oxygen saturation by pulse oximetry.Obesity modelBMI showed a significant positive linear association and forced expiratory volume in the first second showed a significant negative linear association with the presence of PH after adjusting for age (Table 2 and Figure S2 in the supplemental material).Arterial blood hypoxemia modelPaO2 showed a significant negative linear association with the presence of PH after adjusting for age (Table 2 and Figure S2).OSA modelArousal index and ODI showed a significant positive linear association with the presence of PH after adjusting for age (Table 2 and Figure S2).Hypoventilation modelPaCO2 showed a negative nonlinear relationship with the presence of PH (Figure S2). After categorizing PaCO2 by tertiles, the third tertile showed a significantly higher relationship with PH than the first tertile (Table 2).Multivariate modelVariable selection process, based on LASSO regression, selected BMI with an adjusted odds ratio of 1.07 (95% confidence interval [CI] 1.03–1.12; P = .001) and PaO2 with an adjusted odds ratio of 0.96 (95% CI 0.93–0.98; P = .003) as independent risk factors for PH (Table 2 and Figure S3 in the supplemental material).Subgroups analysis for OHS with and without severe OSA phenotypesOf the 246 patients available for the analysis, 167 (67.9%) had severe OSA (Figure 1). Of the 167 with severe OSA, 87 (52.1%) had PH. Of the 79 with nonsevere OSA, 35 (44.3%) had PH. In the severe OSA subgroup, the median pulmonary artery systolic pressure was 32.0 mm Hg (interquartile range = 26.0–35.1) in the group without PH and 47.0 mm Hg (43.6–57.0) in the PH group. In the nonsevere OSA subgroup, the pulmonary artery systolic pressure was 35.5 mm Hg (31.8–37.0) in the group without PH and 43.4 mm Hg (41.1–51.5) in the PH group.Table S1 and Table S2 in the supplemental material summarize the baseline characteristics, polysomnography, and echocardiographic parameters for each OSA phenotype with bivariate analysis between those with and without PH. In the severe OSA subgroup, patients with PH were more obese, with worse daytime oxygenation, hypoventilation, exercise tolerance, OSA severity, and better left ventricular diastolic function. In the nonsevere OSA subgroup, patients with PH were more obese, with a higher prevalence of diabetes mellitus, with worse quality of life, exercise tolerance, and worse left ventricular diastolic function.Table S3 and Table S4 in the supplemental material summarize the potential risk factors and the adjusted models in the severe and nonsevere OSA subgroups. BMI, PaCO2, and PaO2 were potential independent risk factors for PH in the severe OSA subgroup in the age-adjusted model. However, in the multivariate model, only BMI and PaO2 were independent risk factors with an adjusted odds ratio of 1.07 (95% CI 1.02–1.12; P = .008) for BMI and 0.95 (95% CI 0.92–0.99; P = .024) for PaO2 (Table S3 and Figure S4 in the supplemental material). E/A ratio, presence of diabetes mellitus, and BMI were potential independent risk factors for PH in the nonsevere OSA subgroup in the age-adjusted model. However, in the multivariate model, only BMI and E/A ratio were independent risk factors with an adjusted odds ratio of 1.09 (95% CI 1.01–1.19; P = .008) for BMI and 0.05 (95% CI 0.00–0.59; P = .017) for E/A ratio (Table S4 and Figure S5 in the supplemental material).DISCUSSIONThis is the first cross-sectional study evaluating potentia
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