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.

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

Acute heart failure is caused by various factors and requires multiple drug therapies to remedy underlying causes. Due to the complexity of pharmacologic effects of cardiovascular agents, few studies have theoretically addressed the multidrug optimization problem. This paper proposes a drug infusion system for acute heart failure that controls cardiovascular performance metrics (cardiac output, left atrial pressure, and mean arterial pressure) within desired ranges as dictated by the cardiovascular parameters (systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume). The key to our system design is modeling and controlling cardiovascular parameters to yield the desired cardiovascular metrics. A 'tailored drug infusion' technique controls parameters by solving the optimization problem in order to conquer the complexity of multi-dependencies and the different dosage limits among multiple drugs. A 'cardiovascular space mapping' technique identifies the desired parameters from the desired metrics by deriving the analytical solutions of the metrics as functions of the parameters. To facilitate clinical discussions, parameters were set to realistic values in 5,600 simulated patients. Our results showed not only that the optimized drug combinations and dosages controlled the cardiovascular metrics to within the desired ranges, but also that they mostly corresponded to the recommended clinical use guidelines. An additional value of our system is that it proactively predicts the limitations of the tailored drug therapy, which supports the clinical decision of pivoting to alternative treatment strategies such as mechanical circulatory support.

Beside the point: motor adaptation without feedback-based error correction in task-irrelevant conditions.

Adaptation of movement may be driven by the difference between planned and actual motor performance, or the difference between expected and actual sensory consequences of movement. To identify how the nervous system differentially uses these signals, we asked: does motor adaptation occur when movement errors are irrelevant to the task goal? Participants reached on a digitizing tablet from a fixed start location to one of three targets: a point, an arc, or a ray. For the arc, reaches could be in any direction, but to a specific extent. For the ray, reaches could be to any distance, but in a targeted direction. After baseline reaching to the point, the direction or extent of continuous visual feedback was perturbed during training with either a cursor rotation or gain, respectively, while reaching to either the ray (goal = direction) or the arc (goal = extent). The perturbation, therefore, was either relevant or irrelevant to the task goal, depending on target type. During interspersed catch trials, the perturbation was removed and the target switched back to the point, identical to baseline. Although the goal of baseline and catch trials was the same, significant aftereffects in catch trials indicated behavioral adaptation in response to the perturbation. Adaptation occurred regardless of whether the perturbation was relevant to the task, and it was independent of feedback control. The presence of adaptation orthogonal to task demands supports the hypothesis that the nervous system can rely on sensory prediction to drive motor learning that can generalize across tasks.