Marta Kaminska

Acute short-term noninvasive ventilation (NIV) for hypercapnic respiratory failure in chronic obstructive pulmonary disease (COPD) has well-established benefits; however, the role of long-term home NIV remains controversial. In the past decade, studies utilizing aggressive NIV settings to maximally reduce carbon dioxide levels (PaCO2) have resulted in several positive clinical trials and led to updated guidelines on home NIV for stable hypercapnic COPD patients. This clinical respiratory review discusses the high-intensity NIV approach, summarizes recent key trials and guidelines pertaining to home NIV in COPD, and considers key clinical questions for future research and application in the Canadian context. With recent evidence and Canadian Thoracic Society (CTS) guidelines supporting the use of NIV in carefully selected COPD patients with persistent daytime hypercapnia, we believe it is time to reconsider our approach.

Summary Obstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea–hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow‐wave activity, sleep spindles, K‐complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high‐density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.