Abstract
Real-time estimation of patient cardiovascular states, including cardiac output and systemic vascular resistance, is necessary for personalized hemodynamic monitoring and management. Highly invasive measurements enable reliable estimation of these states but increase patient risk. Prior methods using minimally invasive measurements reduce patient risk but have produced unreliable estimates limited due to trade-offs in accuracy and time resolution. Our objective was to develop an approach to estimate cardiac output and systemic vascular resistance with both a high time resolution and high accuracy from minimally invasive measurements. Using the two-element Windkessel model, we formulated a state-space method to estimate a dynamic time constant - the product of systemic vascular resistance and compliance - from arterial blood pressure measurements. From this time constant, we derived proportional estimates of systemic vascular resistance and cardiac output. We then validated our method with a swine cardiovascular dataset. Our estimates produced using arterial blood pressure measurements not only closely align with those using highly invasive measurements, but also closely align when derived from three separate locations on the arterial tree. Moreover, our estimates predictably change in response to standard cardiovascular drugs. Overall, our approach produces reliable, real-time estimates of cardiovascular states crucial for monitoring and control of the cardiovascular system.