Patient‐specific assessment of cardiovascular function by combination of clinical data and computational model with applications to patients undergoing Fontan operation

Abstract

The assessment of cardiovascular function is becoming increasingly important for the care of patients with single-ventricle defects. However, most measurement methods available in the clinical setting cannot provide a separate measure of cardiac function and loading conditions. In the present study, a numerical method has been proposed to compensate for the limitations of clinical measurements. The main idea was to estimate the parameters of a cardiovascular model by fitting model simulations to patient-specific clinical data via parameter optimization. Several strategies have been taken to establish a well-posed parameter optimization problem, including clinical data-matched model development, parameter selection based on an extensive sensitivity analysis, and proper choice of parameter optimization algorithm. The numerical experiments confirmed the ability of the proposed parameter optimization method to uniquely determine the model parameters given an arbitrary set of clinical data. The method was further tested in four patients undergoing the Fontan operation. Obtained results revealed a prevalence of ventricular abnormalities in the patient cohort and at the same time demonstrated the presence of marked inter-patient differences and preoperative to postoperative changes in cardiovascular function. Because the method allows a quick assessment and makes use of clinical data available in clinical practice, its clinical application is promising.

A computational study of the Fontan circulation with fenestration or hepatic vein exclusion

“…We remark that most parameters in our models are held at their reference values. This study does not consider situations where patient–to–patient variability may be significant, which would require approaches related to efficient exploration of parameter distributions and development of patient–specific models [51]. Furthermore, variability may be augmented by the progression of the disease or the presence of other pathological conditions.…”

Hemodynamic performance of the Fontan circulation compared with a normal biventricular circulation: a computational model study

“…To predict the hemodynamic impacts of certain operations in a specific patient, the model should be tuned to the patient by optimizing model parameters based on patient-specific clinical data. This issue has been addressed elsewhere, such as in recent studies on surgical planning (40,63) and those from our group (46,82).…”

“…Accordingly, the total systemic vascular resistance, the total pulmonary vascular resistance, and the total blood volume were adjusted via a parameter optimization algorithm (herein the Nelder-Mead method employed in our previous studies; Refs. 46,82) where the values of the parameters and total blood volume are iteratively modified to minimize the root mean squared error between model simulations and measured data. The resulting parameter values (herein taken as the default parameter values) for the biventricular circulation model are summarized in Table 1.…”

An Anatomically Detailed Arterial Network Model for One-Dimensional Computational Hemodynamics

“…In fact, the 1-D model is starting to be used as baseline model to test data assimilation techniques for parameter estimation [37]. Should this combination is proved successful, it can be a powerful tool for the development of accurate patient-specific models for surgical procedures (see, for instance, [9], [10] and [38]). …”