1Division of Gastroenterology and Hepatology, NewYork-Presbyterian/Weill Cornell Medical Center, New York City, New York, USA.
2Department of Internal Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York City, New York, USA.
3Department of Medicine, Division of Sleep Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York City, New York, USA
Background and aims: Covert HE (CHE) is a common early stage of HE associated with poor outcomes. Available neuropsychiatric diagnostic testing is underutilized and has significant clinical limitations. Sleep deterioration is consistently associated with CHE and HE; however, objective data is sparse and it has not been studied longitudinally. We longitudinally study and describe an association of sleep metrics with CHE as detected by a commercial wearable technology.
Methods: We monitored sleep for 6 months using a commercial fitness tracker in 25 participants with cirrhosis, hypothesizing that CHE as diagnosed by psychometric testing would be associated with significant reductions in sleep quality, especially restorative sleep (deep sleep + rapid eye movement). Mixed-effects modeling was performed to evaluate sleep factors associated with CHE and developed and internally validated a score based on these sleep metrics for associated CHE.
Results: Across 2862 nights with 66.3% study adherence, we found that those with CHE had consistently worse sleep, including an average of 1 hour less of nightly restorative sleep, driven primarily by reductions in rapid eye movement. A model including albumin, bilirubin, rapid eye movement, sleep disturbances, and sleep consistency showed good discrimination (area under the receiver operating curve=0.79) for CHE status with a sensitivity of 76% and specificity of 69%.
Conclusions: Our large longitudinal study of sleep in cirrhosis suggests that sleep derangements in CHE can be detected using wearable technology. Given the known importance of sleep to overall health and CHE/HE to prognosis in cirrhosis, the ability to associate dynamic sleep metrics with CHE may in the future help with the detection and passive monitoring as factors that precipitate decompensation of cirrhosis become better understood and mobile health data validation and integration improves.