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DESCRIPTION (provided by applicant): We shall use the clinical analysis of heart-lung interactions and contraction dyssynchrony as the vehicles for training post-doctoral candidates. This is an excellent and proven productive pathway for mentoring because it is based in clinically relevant cardiopulmonary physiology and requires the trainee to learn to use state-of- the-art monitoring and imaging techniques with broad applications to the operating room, ICU, general hospital ward and out-patient clinic. Although numerous investigators have studied heart-lung interactions, an accurate clinical application has been lacking. Ventilation and ventilatory maneuvers have profound and only recently documented understood hemodynamic effects that can be used to define cardio-respiratory failure. Our goal is to develop and validate clinically relevant and easily applicable bedside tools for the rapid and unambiguous diagnosis of the etiology of hemodynamic insufficiency and the impact of ventilatory and adjunct cardiovascular therapies upon it. We use as our research environment, the ICU, echocardiographic clinic and the cardiac surgery suite. To accomplish these goals we are testing three hypotheses: 1) Positive-pressure induced changes in LV preload and afterload are differentially affected by contractility and effective circulating blood volume. 2) Positive end-expiratory pressure improves LV performance by minimizing ventricular interdependence and LV ejection asynchrony by increasing ITP. 3) LV contractile reserve and preload responsiveness (often opposite ends of the cardiac function spectrum) and arterial tone can be readily identified by analysis of the arterial pulse pressure responses to spontaneous and positive-pressure ventilation. We shall use state-of-the- art echocardiographic imaging techniques (speckle tracking and tissue Doppler imaging) for complex analysis of LV performance and simple aortic Doppler and arterial pressure waveform analysis to assess preload- responsiveness, contractile reserve and arterial tone to derive clinically useful information.
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Autonomous diagnosis and management of the critically ill during air transport (ADMIT)
Autonomous diagnosis and management of the critically ill during air transport (ADMIT)
Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instability
Quantifying Left Ventricular Ejection Effectiveness
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