Systems and methods are provided for optimizing hemodynamics within a patient. Specifically, the system incorporates invasive sensor data (e.g., pressure measurements) combined with mechanisms to dynamically change the loading conditions of the heart and/or heart rate, in order to understand hemodynamic parameters. Computational analyzes on dynamic sensor data are used to understand and guide heart rate, filling pressures, and/or volume resuscitation in critically ill patients. By pacing the heart or inducing tricuspid regurgitation, the system may cause dynamic changes in sensor data to understand optimal loading conditions and heart rates. While determining optimal hemodynamic parameters, the system may then automatically optimize the heart rate and/or filling pressures in critically ill patients.
