The goals of this research program center on creating a legacy resource to facilitate collaborative sleep research with opportunities to intersect other neuroscience and medical disciplines and to serve as a rich resource for innovation. Immediate existing avenues of research to be pursued include sleep apnea and sleep-related hypoventilation associations with health outcomes. We will leverage a large sample size by performing subject characteristic effect modification analyses addressing existing knowledge gaps (e. g., gender-specific differences), which can inform risk stratification and treatment strategies.
Data generated will establish a basis for identifying sleep physiologic signal forecasters (e.g., cardiac electrophysiology measures) of incident new cardiac arrhythmia and rises in blood pressure over time, provide a platform to develop and apply novel electrophysiologic analyses, and inform priority outcomes in clinical trials. Therefore, development of this resource harnessing clinic-based data carries a very high level of broad, far-reaching significance to 1) elucidate sleep disorder hypotheses of interest with effective power, 2) contribute to our understanding of clinic-based normative standards for sleep physiologic measures, and 3) allow sufficient power to examine age- and gender-specific differences and patient reported outcomes as they pertain to sleep disorders and health. These efforts position the sleep program on the forefront of innovations in sleep science.
2018-2020 - Neuroscience Research Development Program Multimodal Neurocardiorespiratory Physiologic Sleep Signal Repository: Transformative Resource Facilitating Transdisciplinary Research Opportunities
Prevalence and Predictors of Obesity Hypoventilation Syndrome and Sleep Related Hypoventilation in Bariatric Surgery patients. Tran K, Wang L, Gharaibeh S, Kempke N, Kashyap S, Cetin D, Aboussouan L, Mehra R., SLEEP 2019.
Members & Collaborations
- Loutfi Aboussouan, MD
- Nancy Foldvary Schaefer,DO,MS
- Alex Milinovich
- James Bena
- Lu Wang