Cardiovascular disease is a large problem in the intensive care unit (ICU) due to its high prevalence in modern society. In the ICU, intensive monitoring is required to help diagnose cardiac and circulatory dysfunction. However, complex interactions between the patient, disease, and treatment can hide the underlying disorder. As a result, clinical staff must often rely on their skill, intuition, and experience to choose therapy, increasing variability in care and patient outcome.

To simplify this clinical scenario, model-based methods have been created to track subject-specific disease and treatment dependent changes in patient condition, using only clinically available measurements. The approach has been tested in two pig studies on acute pulmonary embolism and septic shock and in a human study on surgical recovery from mitral valve replacement. The model-based method was able to track known pathophysiological changes in the subjects and identified key determinants of cardiovascular health such as cardiac preload, afterload, and contractility. These metrics, which can be otherwise difficult to determine clinically, can be used to help provide targets for goal-directed therapies to help provide deliver the optimal level of therapy to the patient. Hence, this model-based approach provides a feasible and potentially practical means of improving patient care in the ICU.