System Design for Optimizing Drug Infusions Using Cardiovascular Space Mapping for Acute Heart Failure

Analytical Representation of Four-dimensional Hemodynamics for Drug Therapy Simulation in Acute Heart Failure Treatment

Acute heart failure imperils multiple organs, including the heart. Elucidating the impact of drug therapies across this multidimensional hemodynamic system remains a challenge. This paper proposes a simulator that analyzes the impact of drug therapies on four dimensions of hemodynamics: left atrial pressure, cardiac output, mean arterial pressure, and myocardial oxygen consumption. To mathematically formulate hemodynamics, the analytical solutions of four-dimensional hemodynamics and the direction of its change are derived as functions of cardiovascular parameters: systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume. Furthermore, a drug library which represents the multi-dependency effect of drug therapies on cardiovascular parameters was identified in animal experiments. In evaluating the accuracy of our derived hemodynamic direction, the average angular error of predicted versus observed direction was 18.85[deg] after four different drug infusions for acute heart failure in animal experiments. Finally, the impact of drug therapies on four-dimensional hemodynamics was analyzed in three different simulation settings. One result showed that, even when drug therapies were simulated with simple rules according to the Forrester classification, the predicted direction of hemodynamic change matched the expected direction in more than 80% in 963 different AHF patient scenarios. Our developed simulator visualizes the impact of drug therapies on four-dimensional hemodynamics so intuitively that it can support clinicians' decision-making to protect multiple organs.

Inverse ESPVR Estimation with Singularity Avoidance via Constrained EDPVR Parameter Optimization

Left ventricular end-systolic elastance Ees, as an index of cardiac contractility, can play a key role in continuous patient monitoring during cardiac treatment scenarios such as drug therapies. The clinical feasibility of Ees estimation remains challenging because most techniques have been built on left ventricular pressure and volume, which are difficult to measure or estimate in the regular ICU/CCU setting. The purpose of this paper is to propose and validate a novel approach to estimate Ees, which is independent of left ventricular pressure and volume. Our methods first derive an analytical representation of Ees as the inverse function of the gradient of the Frank-Starling Curve based on cardiac mechanics. Second, elucidating the mechanism of singularities in the inverse function, we derive multiple conditions in both end-systolic pressure-volume relationship (ESPVR) and end-diastolic pressure-volume relationship (EDPVR) parameters to avoid these singularities analytically. Third, we formulate a constrained nonlinear least squares problem to optimize both ESPVR and EDPVR parameters simultaneously to avoid singularities. The effectiveness of the proposed method in avoiding singularities was evaluated in an animal experiment. Compared to the conventional Ees estimation by linear regression, our proposed method reproduced in-vivo hemodynamics more accurately when simulating the estimated Ees variation during drug administration. Our method can be applied using the available data in the regular ICU/CCU setting. The improved clinical feasibility can support not only physicians' decision-making, including adjusting drug dosages in current clinical treatment, but also a closed-loop hemodynamic control system requiring accurate continuous Ees estimation.