European Journal of Cardiovascular Medicine

Pulmonary hypertension (PH) is a chronic hemodynamic cardiopulmonary condition characterized by an elevated pulmonary artery pressure. It may be a consequence of various causes and has diverse patho- etiological mechanisms.¹ PH is defined as mean pulmonary artery pressure ≥25 mm Hg at rest during right heart catheterization.² The term pulmonary arterial hypertension (PAH) describes a subpopulation of cases with PH characterized hemodynamically by the evidence of pre-capillary PH including an end-expiratory pulmonary artery wedge pressure (PAWP) ≤15 mm Hg and a pulmonary vascular resistance >3 Wood units.³

Sleep causes a profound effect in individuals with severe pulmonary disease, by its influence on the respiratory drive, airway stability, and ventilatory mechanics.³ Sleep-related breathing disorders (SRBDs) are a group of ventilatory disorders during sleep which includes sleep apnea (SA), obstructive sleep apnea (OSA), central sleep apnea (CSA), and sleep related hypoventilation.⁴ The most frequent sleep-related breathing disorder in PAH is hypoxemia during sleep, with a prevalence ranging from 21 to 83%.⁵

Treatment of SRBDs is essential in order to improve pulmonary haemodynamics. SRBD has been treated by several different modalities over the years including Continuous Positive Airway Pressure (CPAP), surgical interventions, medical treatment, and exogenous devices.⁶ The majority of patients with SRBDs do not receive a diagnosis and are undertreated. Present study was aimed to study sleep related breathing disorders (SRBD) in patients of pulmonary arterial hypertension (PAH) in central India.

Present study was single Centre, cross-sectional study, conducted in department of Respiratory Medicine and Cardiology of a tertiary care teaching hospital situated in the Central India. Study duration was of 2 years (July 2020 to June 2022). The present study protocol was approved by the Institutional Ethics Committee (IEC).

Inclusion criteria

• Patients aged more than 18 years, newly diagnosed patients with PAH, Willing to participate in present study

Exclusion criteria

• Patients aged less than 18 years;
• Patient with prior oxygen therapy.
• Patient already diagnosed with sleep apnea.

Study was explained to participants in local language & written informed consent was taken. Upon enrolment, all the patients were subjected to detailed history , clinical examination and laboratory investigations. On the same day, 2D echocardiography was performed and diagnosis of PAH was reached. On the next day, spirometry was done to assess the pulmonary function and patients were classified as normal, obstructive, or restrictive. On the subsequent night, each patient underwent over-night polysomnography and presence or absence of SRBDs was determined. Moreover, if presence of SRBDs was observed, severity of sleep apnea was done and type SRBD (OSA or CSA) was determined.

Diagnosis of sleep apnea was based on the American Academy of Sleep Medicine (AASM) Manual for Scoring Sleep and Related Events (92). Sleep apnea was defined as a complete cessation of oronasal respiratory airflow during sleep or a decrease of more than 90% from baseline, either one lasting for more than 10 sec. Hypopnea was defined as a decrease in respiratory airflow intensity during sleep of more than 30% from baseline, accompanied by a decrease in oxygen saturation ≥3% from baseline.

If the apnea event was accompanied by the cessation of respiratory movement in the chest and abdomen, it was considered a central apnea event; otherwise, it was considered an obstructive event. If obstructive Apnea hypopnea index (AHI) totalled ≥5/hour and the obstructive events accounted for > 50% of the apneic events. In the present study, patients were divided into no SRBDs (AHI < 5 events/h of sleep), OSA (AHI ≥ 5 and ≥ 50% of the events obstructive), and CSA (AHI ≥ 5 and > 50% of the events central).

Severity of sleep apnea was assessed with the apnea-hypopnea index (AHI). AHI is the combined average number of apneas and hypopneas that occur per hour of sleep. The STOP-Bang questionnaire was used to assess the risk of developing OSA.

Data was collected and graphics were designed by Microsoft Office Excel 2016. The data was analysed with SPSS (IBM, Armonk, NY, USA) version 23.0 for windows, with the help of statistician. The categorical and continuous variables are represented as frequency (percentage) and mean (standard deviation, SD). Chi-square test was used to assess the association between various categorical variables. A two-tailed probability value of < 0.05 was considered as statistically significant.

A total of 98 patients with PAH were initially screened for the study and were explained the study procedure in their native language. Majority of the patients were aged ≥ 40 years (87.76%). The mean age of patients was 55.47 ± 13.08 years and age of the patients ranged from 24 to 85 years. Majority of the patients were males (60.20%) and the male to female ratio was 1.51. Majority of the patients had a normal BMI (18.5 – 24.9, 75.51%). Moreover, 14.29% patients were obese and remaining (10.20%) were overweight.

Table 1: General characteristics

All the patients had shortness of breath (100%). Other presenting symptoms, in decreasing order, were cough (39.79%), pedal oedema (23.47%), and chest pain (6.12%).

Table 2: Distribution of patients according to presenting symptoms

Most common comorbidities, in decreasing order, were hypertension (26.53%), ischemic heart disease (15.31%), obesity (14.29%), and type 2 diabetes mellitus (13.27%). Moreover, 16 (16.33%) patients had no comorbidities.

Table 3: Distribution of patients according to comorbidities

Only males indulged in smoking. Of 59 males, 42 (71.19%) indulged in smoking, while remaining (28.81%) did not indulge in smoking.

Table 4: Distribution of patients according to history of smoking

Majority of the patients had anaemia (64.29%). While, only 8 (8.16%) patients had elevated TLC and 4 (4.08%) patients had raised TSH levels. Moreover, all the patients had normal lipid profile, and liver and renal function test.

Table 5: Distribution of patients according to laboratory parameters

Most common type of PH, in decreasing order, were Group 3 (65.31%), Group 2 (27.55%), Group 4 (6.12%), and Group 5 (1.02%). Majority of the patients had mild PH (52.04%) followed by severe (27.55%) and moderate PH (20.41%).

Table 6: Distribution of patients according to type of pulmonary hypertension (PH)

Most of the patients with PH had SRBD (77.55%). Majority of the patients had OSA (81.57%) followed by CSA (11.84%), OSA + OHS (5.26%), and OSA + CSA (1.3%).

Table 7: Distribution of patients according to sleep apnea

Majority of the patients had mild sleep apnea (60.05%) followed by severe (23.78%) and moderate sleep apnea (15.78%).

Table 8: Distribution of patients according to severity of sleep apnea

AHI: Apnea-hypopnea index

Majority of the patients had nocturnal hypoventilation (83.67%), while 16.33% had no SRBDs.

Table 9: Distribution of patients according to nocturnal hypoventilation

Significantly greater proportion of patients aged ≥ 40 years had OSA (p-values < 0.0001) and no sleep apnea (p-values = 0.002).

Table 10: Association between age and types of SRBD

Increase in severity of PH led to increase in proportion of patients with both OSA and CSA. However, there was no significant association between severity of PH and types of sleep apnea (p-value = 0.690).

Table 11: Association between severity of PH and types of SRBD

Greater proportion of females had OSA, while greater proportion of males had CSA. However, there was no significant association between sex and types of SRBD (both p-values > 0.05).

Table 12: Association between sex and types of SRBD

Majority of the patients with both Group 2 and 3 PH had OSA and there was a significant association between them (both p-values < 0.0001). Moreover, all the patients with Group 4 and 5 PH had OSA.

Table 13: Association between types of PH and types of SRBD

Majority of the patients had low risk (43.88%) followed by intermediate risk (35.71%), and high risk (20.41%). All patients with mild sleep apnea had low risk. Significantly greater proportion of patients with mild sleep apnea had low risk than those with intermediate and high risk (p-value = 0.009). Though greater proportion of patients with moderate sleep apnea had high risk, there was no significant association (p-value = 0.267). Finally, significantly greater proportion of patients with severe sleep apnea had high risk (p-value < 0.0001).

Table 14: Association between STOP BANG grade and severity of sleep apnea

Significantly greater proportion of patients with mild sleep apnea had normal BMI than those who were overweight and obese (p-value = 0.013). Though greater proportion of patients with moderate sleep apnea were overweight and obese, there was no significant association (p-value = 0.140). Finally, significantly greater proportion of patients with severe sleep apnea were overweight and obese (p-value < 0.0001).

Table 15: Association between BMI and severity of sleep apnea

Significantly greater proportion of patients with OSA had obstruction than restriction (p-value = 0.011). Moreover, all patients with CSA had restriction. In patients with OSA + OHS, spirometry could be performed in only 1 patient and the findings were of obstructive nature. Finally, in only patient with OSA + CSA, spirometry revealed mixed finding.

Table 16: Association between spirometry findings and types of SRBD

SRBDs are conditions of abnormal and difficult respiration during sleep, including chronic snoring and sleep apnea.⁷ Some of them have limited health impact, but others can have serious consequences because of their potential effects on sleep and the balance of oxygen and carbon dioxide in the blood.⁸ OSA is one of the most common and serious SRBD. In OSA, the airway repeatedly collapses during sleep, causing lapses in breathing that both fragment sleep and affect the body‟s oxygen levels. ⁹ Snoring, gasping or choking during sleep and excessive daytime sleepiness are central symptoms of OSA. When left untreated, the condition can cause significant health problems including cardiovascular issues like high blood pressure and stroke. ^9,10

Pulmonary Hypertension (PH) causes a range of nonspecific symptoms (including breathlessness, fatigue, chest pain and weakness) and is associated with significant morbidity and mortality triggered by the debilitating progressive nature of the disease, which eventually leads to right heart failure and death.¹¹ Pulmonary Hypertension (PH) is not in itself a diagnosis, but solely a hemodynamic state characterized by resting mean pulmonary artery pressure of ≥ 20 mm Hg.⁶ SRDBs are included as one of the potential etiologies of PH. The etiology of this PH is considered to occur through mechanisms such as hypoxic vasoconstriction with subsequent vascular remodeling, systemic inflammation, and hyper-coagulable states.

In the present cross-sectional study, PH patients have a very high prevalence of SDB (77.55%), in that maximum patients had only OSA (81.57%) followed by CSA (13.51%) followed by OHS (5.26%). According to severity, most of the patients with PH had mild sleep apnea (60.05%) followed by severe (23.68%) and moderate sleep apnea (15.78%). Whereas, overall, we found that nocturnal desaturation was present in 83.67% of patients with Pulmonary hypertension.

Similar to the present study, Dyachenko et al.,¹² reported SRBDs predominantly of obstructive nature in 74.2% of the examined cases: 38.7% were mild, 22.6% were moderate, 12.9% were severe (24). Shaarawy et al.,³ further added that study population with the number of OSA patients was 5 amongst all the PAH patients and CSR/CSA patients were only 2. So the presence of CSR/CSA in these 2 patients was possibly attributed to have an effect on the cardiac condition.

Shehata et al.,¹³ reported severe OSA in maximum patients (70.3%) and non-severe OSA in remaining patients (29.7%).(103) Moreover, Fletcher et al.,¹⁴ had reported severe OSA in the absence of hypoxemic lung disease in PAH. Minic et al.,¹ in their studied cohort, observed prevalence of SRBD (OSA and CSA) in patients with PAH of nearly 71%, of whom 60% were of mild nature and 42% were of moderate.

Prisco et al.,¹⁵ evaluated 28 consecutive patients with PAH for SRBD and demonstrated a 50% prevalence of mild OSA. This is similar to the prevalence of SRBDs in the present study. Moreover, analysis of data of 2,438 patients with PAH from the Reveal database registry in the United States reported comorbid OSA in 21% of patients.¹⁰

In the present study, Increase in severity of PH lead to increase in proportion of patients with both OSA and CSA. However, we did not observe a significant association between severity of PAH and types of SRBD (p-value = 0.690). Moreover, we could establish significant association between Groups of PAH(II,III,IV,V) and OSA ( p-values < 0.0001). The association between severity of PAH and types of sleep apnea has not been evaluated adequately. A study by Shehata et al.,¹³ also supported our study findings as no association between PAH severity and sleep apnea was found. In another study, Dyachenko et al.,¹² reported contrary findings with no association of SRBD with PH functional type in their study. Thus, the findings of the present study are comparable to studies.

In the present study, we could not establish significant association between sex and types of sleep apnea (both p-values > 0.05). Female population had OSA in greater number, while male population had CSA in greater number, it was not statistically significant. Moreover, significantly greater proportion of patients with mild sleep apnea had normal BMI than those who were overweight and obese and was statistically significant (p- value = 0.013). Though greater proportion of patients with moderate sleep apnea were overweight and obese, there was no significant association established (p-value = 0.140). We could establish significant association between severe sleep apnea and overweight as well as obese patients (p- value < 0.0001).This was also in agreement with Shehata et al.,¹³ Chaouat et al.,¹⁶ and Arias et al.,¹⁷ who have reported a significant positive correlation between OSA and BMI in their respective studies (p-value < 0.05). Similarly Yan et al.,¹⁸ found that sex (p-value = 0.01) and BMI (p-value = 0.01), were associated with OSA in PAH patients .This results were slightly in consensus with our study data. Furthermore Shaarawy e al.,³ also noted that BMI did not show any significant correlation with OSA or any of the parameters of nocturnal desaturation in the studied patients.

In the present study, all patients with mild sleep apnea had low risk of STOP BANG grade. Significantly greater proportion of patients with mild sleep apnea had low risk than those with intermediate and high risk (p-value = 0.009). Though greater proportion of patients with moderate sleep apnea had high risk, there was no significant association (p-value = 0.267). Finally, significantly greater proportion of patients with severe sleep apnea had high risk (p-value < 0.0001). Amongst components of this score, Fishman et al.,¹⁹ concluded that neck circumference is the most important one and greater than 40 cm predicts OSA with sensitivity of 61% and specificity of 93%, regardless of gender.

Also, Young et al.,²⁰ reported that neck circumference was the most powerful predictor of OSA among all anthropometric variables available in STOP-Bang score, suggesting that the central obesity rather than generalized distribution of body fat is important for the development of OSA. Obesity leads to increased amount of fat in the neck which plays the greatest role in OSA. It is presumed that increased fat deposition in the neck region or adjacent to the upper airway on the pharyngeal wall can impinge on the pharyngeal lumen and predispose its collapse during sleep.

According to Chung et al.,²¹ as the STOP-Bang score increased from 0–2 to 7 and 8, the probability of having OSA, moderate/severe OSA, and severe OSA increased from 46% to 86%, 18% to 60%, and 4% to 38%, respectively in their study. The probabilities of having OSA were greater as the STOP-Bang score increased. This trend was the same across the groups of all OSA, moderate/severe OSA, and severe OSA according to the author and significant association was established supporting our study findings.

Farney et al.,²² showed that greater the cumulative score of risk factors, as reflected by the STOP-Bang model, the greater the probability of severe OSA. With any score >4, the probability of having severe OSA increased continuously. With a score of 8, the probability of severe OSA was 81.9% in their study (115).

Limitations of the study were single-centre, cross-sectional study. Effect of PH treatment on SRBDs was not evaluated. The study had small sample size. Thus, findings cannot be generalized. There was no long-term follow-up of patients. So, we could not determine the effect of OSA on the long-term survival of patients with PH. We did not investigate whether PH could be alleviated by the correction of OSA. Large, prospective cohort studies are needed to further explore these issues.

Majority of the patient with PH had mild sleep apnea (60%) fallowed by moderate and severe (40%). Severity of PH was not significantly associated with types of sleep apnea (p-value = 0.690). Both Group II and Group III PH were significantly associated with OSA (both p-values < 0.0001). Significantly greater proportion of patients with mild and severe sleep apnea had low (p-value = 0.009) and high risk (p-value < 0.0001), respectively. Significantly greater proportion of patients with OSA had obstruction than restriction (p-value = 0.011). Moreover, all patients with CSA had restriction. Thus, the findings suggest high prevalence of SRBDs (77.55%) in patients with PH. These patients should be actively evaluated for SRBDs and simultaneously treated, as presence of SRBDs further increases the severity of PH and raises both morbidity and mortality. So further, large scale studies are recommended.

Conflict of Interest: None to declare

Source of funding: Nil

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