Modeling and simulation of right ventricular volume measurement system during right heart catheterization

Haemodynamic monitoring is necessary for the effective management of critically ill cardiac patients. Pulmonary artery catheterization has been used for monitoring the circulation, for measurement of intracardiac pressures and to estimate preload and afterload. However, pressures may not be accurate reflection of the circulation and simultaneous measurement of volumes would improve patient treatment. However, measurement of cardiac volumes especially of the right ventricle is difficult in everyday clinical practice In this work we propose the use of pulmonary artery catheter (PAC) with ultrasonic sensors built on it, to calculate the right ventricular end-diastolic (RVEDV) and end-systolic volume (RVESV). This is achieved by using the Ultrasonic (US) beam, to measure the distances between the transducers on the catheter and the RV walls. These distances, will be used as an input to a Volume calculating algorithm, which finally provides the RVEDV and RVESV, using a Neural Network (NN). For that reason, we have used cardiac Magnetic Resonance Imaging (MRI) and have modeled the catheter and the US transducers, to get as input the distances to the surface of the cavity. With these distances, and the known cardiac volumes (calculated using MR images) we trained and validated a NN for volume calculation. The results show that the algorithm accurately calculates the RVEDV. For the RVESV, greater deviations are observed between values calculated with our algorithm and cardiac MRI. © Springer Science+Business Media, LLC 2010

Intracardiac volume calculation of right ventricular chamber - A theoretical method

In this study a novel method of right ventricular (RV) volume measurement is presented, using a distance transducer inside the right ventricular cavity. This tool could be useful during pulmonary artery catheterization (PAC), as the transducer could be mounted on the tip of the catheter which will be inserted in the cavity. Pulmonary artery catheterization has been used for monitoring hemodynamics by measuring the intracardiac pressures in critically ill patients usually in intensive care unit but also in Infarctions Unit, even in the preparation or during serious surgical operations. However, pressure measurement cannot be sufficient enough for the effective management of critical patients. The pressure-volume ratio estimation is considered the most essential for the efficient treatment of critical patients. There is no other method to our knowledge which calculates the right ventricular volume using the pulmonary artery catheter, performing measurements for a long period of time as the catheter remains within the cardiac ventricle for several hours up to a few days. This method consisted by an ultrasound transducer which is mounted on a miniature rotating structure, measuring the distance from the transducer to several points on the ventricular wall. The collected data will be processed using the appropriate mathematical model in order the volume of the cavity to be calculated. Since the implementation of such a device is not feasible at this point, we attempted a software model of the proposed technique. Given that in medical practice there is a tolerance of up to 15% approximation in volume, the results could be considered satisfactory. The error margin could be further reduced as the rotation step increases. © 2010 Elsevier Ltd. All rights reserved

KardiaSoft Architecture -A Software Supporting Diagnosis and Therapy Monitoring of Heart Failure Patients Exploiting Saliva Biomarkers

The aim of this work is to present the architecture of the KardiaSoft software, a clinical decision support tool allowing the healthcare professionals to monitor patients with heart failure by providing useful information and suggestions in terms of the estimation of the presence of heart failure (heart failure diagnosis), stratification-patient profiling, long term patient condition evaluation and therapy response monitoring. KardiaSoft is based on predictive modeling techniques that analyze data that correspond to four saliva biomarkers, measured by a point-of-care device, along with other patient's data. The KardiaSoft is designed based on the results of a user requirements elicitation process. A small clinical scale study with 135 subjects and an early clinical study with 90 subjects will take place in order to build and validate the predictive models, respectively