Georgia S. Karanasiou

Stents: Biomechanics, Biomaterials, and Insights from Computational Modeling

Coronary stents have revolutionized the treatment of coronary artery disease. Improvement in clinical outcomes requires detailed evaluation of the performance of stent biomechanics and the effectiveness as well as safety of biomaterials aiming at optimization of endovascular devices. Stents need to harmonize the hemodynamic environment and promote beneficial vessel healing processes with decreased thrombogenicity. Stent design variables and expansion properties are critical for vessel scaffolding. Drug-elution from stents, can help inhibit in-stent restenosis, but adds further complexity as drug release kinetics and coating formulations can dominate tissue responses. Biodegradable and bioabsorbable stents go one step further providing complete absorption over time governed by corrosion and erosion mechanisms. The advances in computing power and computational methods have enabled the application of numerical simulations and the in silico evaluation of the performance of stent devices made up of complex alloys and bioerodible materials in a range of dimensions and designs and with the capacity to retain and elute bioactive agents. This review presents the current knowledge on stent biomechanics, stent fatigue as well as drug release and mechanisms governing biodegradability focusing on the insights from computational modeling approaches.

Modeling stent deployment in realistic arterial segment geometries: The effect of the plaque composition

Stents are medical devices used in cardiovascular intervention for unblocking the diseased arteries and restoring blood flow. During stent implantation the deformation of the arterial wall as well as the resulted stresses caused in the arterial morphology are studied. In this paper we study the effect of the composition of the atherosclerotic plaque during the stent deployment procedure, using Finite Element modeling. The stenting procedure is simulated for two different cases; in the first the presence of the plaque is ignored whereas in the second a three dimensional (3D) stiff calcified plaque is located in the stenotic area of the artery. Results indicate that in the second case the von Mises stresses in the arterial wall are higher than the stresses occurred in the first case. In addition, the distribution of the arterial von Mises stress depends on the plaque composition.

A computational approach for the estimation of heart failure patients status using saliva biomarkers

The aim of this work is to present a computational approach for the estimation of the severity of heart failure (HF) in terms of New York Heart Association (NYHA) class and the characterization of the status of the HF patients, during hospitalization, as acute, progressive or stable. The proposed method employs feature selection and classification techniques. However, it is differentiated from the methods reported in the literature since it exploits information that biomarkers fetch. The method is evaluated on a dataset of 29 patients, through a 10-fold-cross-validation approach. The accuracy is 94 and 77% for the estimation of HF severity and the status of HF patients during hospitalization, respectively.

Estimation of New York Heart Association class in heart failure patients based on machine learning techniques

The aim of this work is to present an automated method for the early identification of New York Heart Association (NYHA) class change in patients with heart failure using classification techniques. The proposed method consists of three main steps: a) data processing, b) feature selection, and c) classification. The estimation of the severity of heart failure in terms of NYHA class is addressed as two, three and, for the first time, as four class classification problem. Eleven classifiers are employed and combined with resampling techniques. The proposed method is evaluated on a dataset of 378 patients, through a 10-fold-cross-validation approach. The highest detection accuracy is 97, 87 and 67% for the two, three and the four class classification problem, respectively.

Assessing pediatrics patients’ psychological states from biomedical signals in a cloud of social robots

This paper describes an on-going research aiming to design and deploy a robotic-pet based intervention integrated to the Child Life program in a paediatric hospital. The purpose is to provide in the personalized health-care network a supplement of smart company to alleviate feelings of anxiety, loneliness and stress of long-term inpatient and their bystanders. The state of the art on companion robots for health related purposes in the long run, ethical concerns in the context of paediatric care and social and technological issues are addressed. A description of the first implementation phases, findings, lessons learned and future work are discussed under a critical multidisciplinary approach confronting perspectives from social science and technology studies, engineering, psychology and nursery. The overall research questions addressed are: can a cloud of social assistive robotic-pets join the network of well-being supply in a paediatric hospital? Under which technical and social conditions this innovation could be appropriate by the organization and -more importantly- could improve the service?

Modeling of blood flow through sutured micro-vascular anastomoses

Microanastomosis is a surgical procedure used to reconnect two blood vessels using sutures. The optimal microanastomosis may be predicted by assessing the factors that influence this invasive procedure. Blood flow and hemodynamics following microanastomosis are important factors for the successful longevity of this operation. How is the blood flow affected by the presence of sutures? Computational Fluid Dynamics (CFD) is a powerful tool that permits the estimation of specific quantities, such as fluid stresses, that are hardly measurable in vivo. In this study, we propose a methodology which evaluates the alterations in the hemodynamic status due to microanastomosis. A CFD model of a reconstructed artery has been developed, based on anatomical information provided by intravascular ultrasound and angiography, and was used to simulate blood flow after microanastomosis. The 3D reconstructed arterial segments are modeled as non-compliant 1.24 - 1.47 mm diameter ducts, with approximately 0.1 mm arterial thickness. The blood flow is considered laminar and the no-slip condition is imposed on the boundary wall, which is assumed to be rigid. In analyzing the results, the distribution of the wall shear stress (WSS) is presented in the region of interest, near the sutures. The results indicate that high values of WSS appear in the vicinity of sutures. Such regions may promote thrombus formation and subsequently anastomotic failure, therefore their meticulous study is of high importance.

A preliminary presentation of a mobile co-operative platform for Heart Failure self-management

Heart Failure (HF) is a rapidly increasing cardiovascular chronic disease that affects millions of people globally. Lack of proper management of HF patients increases the risk of frailty and other undesirable effects and contributes to loss of independence. The engagement of the HF patient and all actors related to his/her disease management is critical for empowering the patients in achieving sustainable behaviour change, regarding their adherence and compliance. To address this, the concept and the architecture of a mobile co-operative platform are described. The design and development is based on a multi-stakeholder patient centered mHealth ecosystem for HF patients that will facilitate the collaboration of multidisciplinary actors.

A Novel Concept of the Management of Coronary Artery Disease Patients Based on Machine Learning Risk Stratification and Computational Biomechanics: Preliminary Results of SMARTool Project

Coronary artery disease (CAD) is one of the most common causes of death in western societies. SMARTool project proposes a new concept for the risk stratification, diagnosis, prediction and treatment of CAD. Retrospective and prospective data (clinical, biohumoral, computed tomography coronary angiography (CTCA) imaging, omics, lipidomics, inflammatory and exposome) have been collected from ~250 patients. The proposed patient risk stratification, relying on machine learning analysis of non-imaging data, discriminates low and medium-to-high risk patients. The CAD diagnosis module is based on the 3D reconstruction and automatic blood flow dynamics of the coronary arteries, and the non-invasive estimation of smartFFR, an index correlated with invasively measured fractional flow reserve (FFR). CAD prediction is based on complex computational models of plaque growth considering the blood rheology, the lipoproteins transport and the major mechanisms of plaque growth, such as the inflammation and the foam cells formation. Finally, the treatment module is based on the simulation of virtual stent deployment. Preliminary analysis of 101 patients yielded an overall accuracy of 85.2% with the sensitivity of Class II reaching 98%. The reconstruction methodology is validated against intravascular ultrasound data and the correlation of the geometry derived metrics such as the degree of stenosis, minimal lumen area, minimal lumen diameter, plaque burden are 0.79, 0.85, 0.81 and 0.75, respectively. SmartFFR has been validated compared to invasively measured FFR with a correlation coefficient of 0.90. Plaque growth modelling demonstrates that the inclusion of variables such as the macrophages and foam cells concentrations can increase to 75% the prediction accuracy of regions prone to plaque formation.

Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques

The aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied. In the second stage, the output of the model, accompanied with data from saliva and breath biomarkers, is given as input to a classification model for determining if the patient is adherent, in terms of medication. The method is evaluated on a dataset of 29 patients and the achieved accuracy is 96%.

Available Computational Techniques to Model Atherosclerotic Plaque Progression Implementing a Multi-Level Approach

The mechanisms of atherosclerosis remain unclear and computational modeling has been used to provide insights for the understanding of the processes which lead to the initiation of plaque and its progress. In this work we review the available methodologies for vascular image processing and cardiovascular mechanics. Moreover, we present a multi-level modeling approach which can be used: (i) for the prediction of regions which are prone to plaque formation, (ii) for the medical decision support providing computational estimation of the fractional flow reserve (FFR), and (iii) for simulating the deformation of stent in coronary arteries. More specifically, in the first level three-dimensional (3D) arterial reconstruction is performed. In the second level, the 3D arteries are used for modeling of blood flow and computation of endothelial shear stress (ESS). In the third level the accumulation of lipoproteins and monocytes into the arterial wall is simulated, while in the fourth level the plaque growth process is modeled considering the lipoprotein oxidation, the macrophages differentiation, and the foam cells formation. Moreover, in the fifth level FFR is calculated implementing a novel methodology, while in the sixth level the stent deformation in stenosed arteries is modeled. Each modeling level has been validated using human data and the results show that computational modeling might assist in understanding the pathophysiology of atherosclerosis.