Computational analysis of the effect of the type of LVAD flow on coronary perfusion and ventricular afterload.

We developed a computational model to investigate the hemodynamic effects of a pulsatile left ventricular assist device (LVAD) on the cardiovascular system. The model consisted of 16 compartments for the cardiovascular system, including coronary circulation and LVAD, and autonomic nervous system control. A failed heart was modeled by decreasing the end-systolic elastance of the ventricle and blocking the mechanism controlling heart contractility. We assessed the physiological effect of the LVAD on the cardiovascular system for three types of LVAD flow: co-pulsation, counter-pulsation, and continuous flow modes. The results indicated that the pulsatile LVAD with counter-pulsation mode gave the most physiological coronary blood perfusion. In addition, the counter-pulsation mode resulted in a lower peak pressure of the left ventricle than the other modes, aiding cardiac recovery by reducing the ventricular afterload. In conclusion, these results indicate that, from the perspective of cardiovascular physiology, a pulsatile LVAD with counter-pulsation operation is a plausible alternative to the existing LVAD with continuous flow mode.

Numerical simulation of the effect of sodium profile on cardiovascular response to hemodialysis.

PURPOSE: We developed a numerical model that predicts cardiovascular system response to hemodialysis, focusing on the effect of sodium profile during treatment. MATERIALS AND METHODS: The model consists of a 2-compartment solute kinetics model, 3-compartment body fluid model, and 12-lumped-parameter representation of the cardiovascular circulation model connected to set-point models of the arterial baroreflexes. The solute kinetics model includes the dynamics of solutes in the intracellular and extracellular pools and a fluid balance model for the intracellular, interstitial, and plasma volumes. Perturbation due to hemodialysis treatment induces a pressure change in the blood vessels and the arterial baroreceptors then trigger control mechanisms (autoregulation system). These in turn alter heart rate, systemic arterial resistance, and cardiac contractility. The model parameters are based largely on the reported values. RESULTS: We present the results obtained by numerical simulations of cardiovascular response during hemodialysis with 3 different dialysate sodium concentration profiles. In each case, dialysate sodium concentration profile was first calculated using an inverse algorithm according to plasma sodium concentration profiles, and then the percentage changes in each compartment pressure, heart rate, and systolic ventricular compliance and systemic arterial resistance during hemodialysis were determined. A plasma concentration with an upward convex curve profile produced a cardiovascular response more stable than linear or downward convex curves. CONCLUSION: By conducting numerical tests of dialysis/cardiovascular models for various treatment profiles and creating a database from the results, it should be possible to estimate an optimal sodium profile for each patient.

Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience.

The purpose of this study was to develop an automated scheme to facilitate detection of localized ground-glass opacity (GGO) in the lung at computed tomography (CT). Institutional review board approval and informed consent were not required. Two radiologists reviewed CT images from 14 patients (five men, nine women) who had lung cancer or metastasis and whose malignancy was classified as GGO. The lung region was sampled and completely covered with contiguous, 50% overlapping regions of interest (ROIs) measuring 30 x 30 pixels in size. The lung area within each ROI was analyzed to compute texture features and gaussian curve fitting features. Performance of the artificial neural networks (ANNs) measured by using the area under the receiver operating characteristic curve was 0.92. With a threshold of 0.9, the sensitivity of the ANN for detecting GGO ROIs was 94.3% (280 of 297 ROIs), and the positive predictive value was 29.1% (280 of 963 ROIs). A computerized scheme may hold promise in facilitating detection of localized GGO at CT.