The intimate link between the heart, brain and sleep is central to our well-being and ability to meet the demands of life. A majority of cardiovascular (CV) deaths occur in the early waking hours from sleep. For example, sudden cardiac arrest (SCA), an extremely prevalent and devastating condition, claims more lives (>350,000/year) in the USA than all disease-related causes of death combined. A defibrillator can prevent SCA, but current clinical strategies are grossly inadequate, both in terms of identifying people at risk and importantly, monitoring and controlling the CV risk. To address this major gap, we are proposing an entirely novel approach for studying heart-brain interactions during sleep. To our knowledge, our compelling new preliminary data and innovative strategy are unprecedented. In robust preliminary studies of animals and humans, we have identified unique signatures of evolving, potentially fatal, CV disease within electrocardiogram (ECG) and electroencephalogram (EEG) waveforms that otherwise cannot be detected by current clinical methods and conventional statistics. Our powerful new tools reveal these ?hidden? signatures during sleep (i.e., as conscious activity decreases and autonomic control of the heart becomes prominent). This missing link we have identified between the heart, brain, spontaneous intrinsic arousals, and critical CV disease is independent (in multivariate analyses) of sleep disordered breathing (e.g., apnea) and established risk factors. Our novel and highly promising findings may account for the high incidence of CV deaths associated with sleep and have potential for broad application, ranging from animal models to improved reclassification of individuals currently consid- ered ?low?, ?moderate? or ?high? risk in contemporary clinical practice. This is important because asymptomatic individ- uals without advanced CV disease comprise the majority of SCA victims. They also are the ones ?missed? by current risk stratification methods and the most challenging to identify. Further, our fundamental new approach to EEG and ECG analysis will add new, clinically valuable, prognostic insight for patients with advanced CV disease (e.g., heart failure). This robust, inexpensive, personalized strategy for identifying who will and will not need lifesaving therapy will also avoid unnecessary procedures and complications, and thus, will provide substantial socioeconomic benefits. This paradigm-shifting application for a NIH Director?s New Innovator Award incorporates clinical cardiac electro- physiology, critical care and sleep medicine with engineering, mathematics, artificial intelligence, statistical dynamical systems, and molecular, cellular and clinical research to enable early diagnosis and therapy of critical CV disease. Our unique approach for gaining novel mechanistic insight into CV pathology and risk during sleep is likely to spawn new avenues in collaborative multidisciplinary research. Because our new paradigm can be seamlessly incorporated into existing technology readily available in hospitals and clinics, we expect our findings to rapidly transform contem- porary clinical practice in multiple fields. Importantly, the ability to identify and decode ?hidden? EEG and ECG signa- tures of early onset of fatal, subclinical CV illness, including SCA, the leading cause of death in the industrialized world, has extraordinary implications for human health and the potential for broad worldwide application.
The treatment of heart disease after clinical presentation has limited success. Irreversible organ system damage often initiates by the time the patient presents clinically. We seek to introduce, understand and validate a completely new approach for analyzing heart and brain signals to reveal information about evolving critical disease in an individual that otherwise would remain undetected by current clinical methods until catastrophic clinical presentation. If validated, this new paradigm will have extraordinary implications for human health and the potential to open new avenues in research and therapy.