Jonathan Waxman Funded Projects
Prediction of Physiological Events in People with Sleep Disordered
Funding Source: National Center on Sleep Disorders Research, Pre-Doctoral Fellowship
Faculty Sponsors: David Carley
Dates: 9/01/09– 8/31/12
Abstract: Sleep-disordered breathing (SDB) refers to a spectrum of disorders characterized by abnormal respiratory patterns or levels of ventilation during sleep. The most common is obstructive sleep apnea (OSA). People with OSA experience repetitive apnea (cessation of breathing) and hypopnea (marked decrease in tidal volume) during sleep in association with airway compromise and excessive daytime sleepiness (EDS). An arousal, or brief, often unnoticed, disruption of sleep is commonly associated with apnea. People with OSA also exhibit cognitive dysfunction, including impairment to memory, attention, and executive function. OSA- associated EDS and cognitive dysfunction are thought to significantly contribute to automobile accidents and workplace injuries. The first aim of this research is to predict the onset of nocturnal apnea, hypopnea, and arousal. Our proposal to accomplish this aim represents an entirely new approach to improving the effectiveness and tolerability of SBD therapy. The most common therapy is continuous positive airway pressure (CPAP), which is difficult for many patients to tolerate. Existing auto-adjusting PAP may be more tolerable but relies on detection of disordered breathing events and does not appear to improve quality of life compared with conventional CPAP. Predicting these events could lead to more effective titration of PAP levels and improved outcomes. Other therapies, such as the electrical stimulation of various cranial nerves or pharyngeal muscles, could also be improved by predicting disordered breathing. The second aim is to predict the onset of unintended daytime sleep while subjects undergo maintenance of wakefulness tests, which assess one's ability to resist sleep in a soporific condition. The third aim is to predict performance lapses during driving simulations. Accomplishing aims two and three could lead to the development of warning devices for at-risk individuals. Our novel prediction algorithms track the interactions between several physiological systems and reveal the most important predictors. The fourth aim is to contrast the key predictors between OSA, acutely sleep-deprived, and control subjects, and between men and women. By doing so, we expect to gain insight into the underlying pathophysiology of SDB and EDS, and will investigate sex differences in OSA and sleep deprivation. OSA is a major public health problem whose effects on society are comparable to those of smoking. The capability to predict its adverse consequences will be an invaluable tool to improve the quality of life of people with SDB, reduce the associated costs, and improve public health.