Sleep continuity: a new metric to quantify disrupted hypnograms in non-sedated intensive care unit patients

Introduction Sleep in intensive care unit (ICU) patients is severely altered. In a large proportion of critically ill patients, conventional sleep electroencephalogram (EEG) patterns are replaced by atypical sleep. On the other hand, some non-sedated patients can display usual sleep EEG patterns. In the latter, sleep is highly fragmented and disrupted and conventional rules may not be optimal. We sought to determine whether sleep continuity could be a useful metric to quantify the amount of sleep with recuperative function in critically ill patients with usual sleep EEG features. Methods We retrospectively reanalyzed polysomnographies recorded in non-sedated critically ill patients requiring non-invasive ventilation (NIV) for acute hypercapnic respiratory failure. Using conventional rules, we built two-state hypnograms (sleep and wake) and identified all sleep episodes. The percentage of time spent in sleep bouts (<10 minutes), short naps (>10 and <30 minutes) and long naps (>30 minutes) was used to describe sleep continuity. In a first study, we compared these measures regarding good (NIV success) or poor outcome (NIV failure). In a second study performed on a different patient group, we compared these measurements during NIV and during spontaneous breathing. Results While fragmentation indices were similar in the two groups, the percentage of total sleep time spent in short naps was higher and the percentage of sleep time spent in sleep bouts was lower in patients with successful NIV. The percentage of total sleep time spent in long naps was higher and the percentage of sleep time spent in sleep bouts was lower during NIV than during spontaneous breathing; the level of reproducibility of sleep continuity measures between scorers was high. Conclusions Sleep continuity measurements could constitute a clinically relevant and reproducible assessment of sleep disruption in non-sedated ICU patients with usual sleep EEG.