Obstructive sleep apnea in sarcoidosis and impact of cpap treatment on fatigue

OSA and Chronic Respiratory Disease: Mechanisms and Epidemiology

Obstructive sleep apnea (OSA) is a highly prevalent disorder that has profound implications on the outcomes of patients with chronic lung disease. The hallmark of OSA is a collapse of the oropharynx resulting in a transient reduction in airflow, large intrathoracic pressure swings, and intermittent hypoxia and hypercapnia. The subsequent cytokine-mediated inflammatory cascade, coupled with tractional lung injury, damages the lungs and may worsen several conditions, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and pulmonary hypertension. Further complicating this is the sleep fragmentation and deterioration of sleep quality that occurs because of OSA, which can compound the fatigue and physical exhaustion often experienced by patients due to their chronic lung disease. For patients with many pulmonary disorders, the available evidence suggests that the prompt recognition and treatment of sleep-disordered breathing improves their quality of life and may also alter the course of their illness. However, more robust studies are needed to truly understand this relationship and the impacts of confounding comorbidities such as obesity and gastroesophageal reflux disease. Clinicians taking care of patients with chronic pulmonary disease should screen and treat patients for OSA, given the complex bidirectional relationship OSA has with chronic lung disease.

Influencing Factors of Daytime Sleepiness in Patients with Obstructive Sleep Apnea Hypopnea Syndrome and Its Correlation with Pulse Oxygen Decline Rate

Objective

To explore the influencing factors of daytime sleepiness in patients with obstructive sleep apnea hypopnea syndrome (OSAHS) and the correlation between daytime sleepiness and pulse oxygen decline rate in patients with severe OSAHS.

Methods

From January 2018 to April 2021, 246 consecutive patients with OSAHS diagnosed by polysomnography (PSG) in our hospital were selected. All patients were grouped according to the minimum nocturnal oxygen saturation and apnea hypopnea index (AHI). There were 33 cases in the no sleep hypoxia group, 34 cases in the mild hypoxia group, 119 cases in the moderate hypoxia group, and 60 cases in the severe hypoxia group. There were 30 cases in the simple snoring group, 55 cases in the mild OSAHS group, 48 cases in the moderate OSAHS group, and 113 cases in the severe OSAHS group. The Epworth Sleepiness Scale (ESS) scores of each group were compared. All patients were grouped according to ESS score. Those with score ≥9 were included in the lethargy group (n = 118), and those with score ≤10 were included in the no lethargy group (n = 128). Univariate and multivariate logistic regression analyses were used to explore the influencing factors of daytime sleepiness in OSAHS patients. Pearson correlation analysis showed the correlation between ESS score and pulse oxygen decline rate in patients with severe OSAHS.

Results

The ESS score of the severe hypoxia group > the moderate hypoxia group > the mild hypoxia group > the no sleep hypoxia group. There was significant difference among the groups (F = 19.700, P < 0.0001). There were significant differences between the severe hypoxia group and other groups and between the moderate hypoxia group and the no sleep hypoxia group and the mild hypoxia group (P < 0.05). The ESS score of the severe OSAHS group > the moderate OSAHS group > the mild OSAHS group > the simple snoring group. There was significant difference among the groups (F = 19.000, P < 0.0001). There were significant differences between the severe OSAHS group and other groups and between the moderate OSAHS group and the simple snoring group (P < 0.05). Univariate analysis showed that BMI, neck circumference, snoring degree, total apnea hypopnea time, AHI, micro arousal index (MAI), oxygen saturation (CT90%), lowest oxygen saturation (LSaO2), and mean oxygen saturation (MSaO2) were the influencing factors of daytime sleepiness in OSAHS patients (P < 0.05). Multiple logistic regression analysis showed that AHI and CT90% were independent risk factors for daytime sleepiness in OSAHS patients (P < 0.05). Pearson correlation analysis showed that there was a positive correlation between ESS score and pulse oxygen decline rate in patients with severe OSAHS (r = 0.765, P < 0.0001).

Conclusion

OSAHS patients may be accompanied by daytime sleepiness in varying degrees, which may be independently related to AHI and CT90%. The degree of daytime sleepiness in patients with severe OSAHS may be closely related to the decline rate of pulse oxygen, which should be paid great attention in clinic.