HEARTEN KMS - A knowledge management system targeting the management of patients with heart failure.

The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multi-parametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78% to 95% depending on the module/functionality.

The Evolution of mHealth Solutions for Heart Failure Management.

In the last decade, the uptake of information and communication technologies and the advent of mobile internet resulted in improved connectivity and penetrated different fields of application. In particular, the adoption of the mobile devices is expected to reform the provision and delivery of healthcare, overcoming geographical, temporal, and other organizational limitations. mHealth solutions are able to provide meaningful clinical information allowing effective and efficient management of chronic diseases, such as heart failure. A variety of data can be collected, such as lifestyle, sensor/biosensor, and health-related information. The analysis of these data empowers patients and the involved ecosystem actors, improves the healthcare delivery, and facilitates the transformation of existing health services. The aim of this study is to provide an overview of (i) the current practice in the management of heart failure, (ii) the available mHealth solutions, either in the form of the commercial applications, research projects, or related studies, and (iii) the several challenges related to the patient and healthcare professionals' acceptance, the payer and provider perspective, and the regulatory constraints.

Cleverballoon: An integrated approach for developing a drug-coated balloon with everolimus.

Coronary artery disease (CAD) is a chronic disease associated with high mortality and morbidity. Although treatment with drug-eluting stents is the most frequent interventional approach for coronary artery disease, drug-coated balloons (DCBs) constitute an innovative alternative, especially in the presence of certain anatomical conditions in the local coronary vasculature. DCBs allow the fast and homogenous transfer of drugs into the arterial wall, during the balloon inflation. Their use has been established for treating in-stent restenosis caused by stent implantation, while recent clinical trials have shown a satisfactory efficacy in de novo small-vessel disease. Several factors affect DCBs performance including the catheter design, the drug dose and formulation. Cleverballoon focuses on the design and development of an innovative DCB with everolimus. For the realization of the development of this new DCB, an integrated approach, including in- vivo, in-vitro studies and in-silico modelling towards the DCB optimization, is presented.Clinical Relevance-The proposed study introduces the integration of in- vivo, in-vitro and in silico approaches in the design and development process of a new DCB, following the principles of 3R's for the replacement, reduction, and refinement of animal and clinical studies.

Classification of Inflammation of Unknown Origin patients based on RNA-seq and SomaScan data.

A preliminary analysis was conducted on data acquired from RNA sequencing and SomaScan platforms, for the classification of patients with Inflammation of Unknown Origin. To this end, a multimodal data integration approach was designed, by combining the two platforms, in order to assess the potentiality of learning estimators, using the differentially expressed features from the independent profiling experiments of both platforms. The classification framing was the differentiation of Inflammation of Unknown Origin patients against a multitude of Systemic Autoinflammatory disease patients. Separate false discovery rate analyses were performed on each dataset to extract statistically significant features between the two designated sample groups. Genomic analysis managed higher overall classification metrics compared to proteomic analysis, averaging an ~19% increase overall metrics and classifiers, with a ~0.07% increase in standard error. The multimodal data integration approach achieved similar results to the individual platforms' analyses. More specifically, it managed the same classification accuracy, sensitivity, and specificity scores as the best individual analysis, with the simple Logistic Regression estimator.Clinical Relevance- This study highlights the advantage of exploiting RNA sequencing data to identify potential Inflammation of Unknown Origin disease specific biomarkers, even against other Systemic Autoinflammatory diseases. These findings are further emphasized given the non-apparent clinical discrepancy between Inflammation of Unknown Origin and other Systemic Autoinflammatory diseases.

LETHE: A Digital Intervention for Cognitive Decline.

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

Investigation of crimping effects on the stent deployment through in silico modeling.

Atherosclerosis is one of the most mortal diseases that affects the arterial vessels, due to accumulation of plaque, altering the hemodynamic environment of the artery by preventing the sufficient delivery of blood to other organs. Stents are expandable tubular wires, used as a treatment option. In silico studies have been extensively exploited towards examining the performance of such devices by employing Finite Element Modeling. This study models the crimping stage during stent implantation to examine the effect of inclusion of pre-stress state of the stent. The results show that modeling of the crimping stress state of the stent prior to the deployment results in under-expansion of the stent, due to the indirect inclusion of strain-induced hardening effects. As a result, it is evident that the compressive stent stress configuration is important to be considered in the computational modeling approaches of stent deployment.

A proof-of-concept study for the simulation of blood flow in a post arterial segment for different blood rheology models.

Cardiovascular disease (CVD) and especially atherosclerosis are chronic inflammatory diseases which cause the atherosclerotic plaque growth in the arterial vessels and the blood flow reduction. Stents have revolutionized the treatment of this disease to a great extent by restoring the blood flow in the vessel. The present study investigates the performance of the blood flow after stent implantation in patient-specific coronary artery and demonstrates the effect of using Newtonian vs. non-Newtonian blood fluid models in the distribution of endothelial shear stress. In particular, the Navier-Stokes and continuity equations were employed, and three non-Newtonian fluid models were investigated (Carreau, Carreau-Yasuda and the Casson model). Computational finite elements models were used for the simulation of blood flow. The comparison of the results demonstrates that the Newtonian fluid model underestimates the calculation of Endothelial Shear Stress, while the three non-Newtonian fluids present similar distribution of shear stress. Keywords: Blood flow dynamics, stented artery, non-Newtonian fluid. Clinical Relevance- This work demonstrates that when blood flow modeling is performed at stented arteries and predictive models are developed, the non-Newtonian nature of blood must be considered.

An in silico trials platform for the evaluation of stent design effect in post-implantation outcomes.

Bioresorbable Vascular Scaffolds (BVS), developed to allow drug deliver and mechanical support, followed by complete resorption, have revolutionized atherosclerosis treatment. InSilc is a Cloud platform for in silico clinical trials (ISCT) used in the design, development and evaluation pipeline of stents. The platform integrates beyond the state-of-the-art multi-disciplinary and multiscale models, which predict the scaffold's performance in the short/acute and medium/long term. In this study, a use case scenario of two Bioabsorbable Vascular Stents (BVSs) implanted in the same arterial anatomy is presented, allowing the whole InSilc in silico pipeline to be applied and predict how the different aspects of this intervention affect the success of stenting process.

Investigation of the drug release time from the biodegrading coating of an everolimus eluting stent.

This case-study examines the release time of the everolimus drug from an experimental biodegrading coating of a Rontis corp. drug eluting stent (DES). The controlled drug release is achieved by the degradation of the coating, which consists of a mixture of polylactic co-glycolic acid (PLGA) and everolimus (55:45). In our analysis, we used the outcome of another study, which contains the geometry of an in-silico deployed Rontis corp. stent in a 3D reconstructed coney arterial segment. Using this geometry as input, the everolimus release was simulated using a computational model that includes: i) modeling of the blood flow dynamics, ii) modeling of PLGA degradation, and iii) modeling of the everolimus advection and diffusion towards both the lumen and the arterial wall. The results show the rapid release of everolimus. This is justified due to the high porosity of the coating, which is caused by the initial high concentration of everolimus in the coating.Clinical Relevance - The methodology presented in this work is an additional step towards predicting accurately drug release from DES. Also, the results of our work prove that high drug concentration in the coating causes its rapid release, which could be used as input in the design of new DES.

Investigation of Drug Eluting Stents performance in human atherosclerotic artery through in silico modeling.

Atherosclerosis is a chronic inflammatory disease associated with heart attack and stroke. It causes the growth of atherosclerotic plaques inside the arterial vessels, which in turn results to the reduction of the blood flow to the different organs. Drug-Eluting Stents (DES) are mesh-like wires, carrying pharmaceutical coating, designed to dilate and support the arterial vessel, restore blood flow and through the controlled local drug delivery inhibit neo-intimal thickening. In silico modeling is an efficient method of accurately predicting and assessing the performance of the stenting procedure. The present in silico study investigates the performance of two different stents (Bare Metal Stent, Drug-Eluting Stent) in a patient-specific coronary artery and assesses the effect of stent coating, considering that the same procedural approach is followed by the interventional cardiologist. The results demonstrate that even if small differences are obtained in the two models, the incorporation of the stent coatings (in DES) does not significantly affect the outcomes of the stent deployment, the stresses and strains in the scaffold and the arterial tissue. Nevertheless, it is suggested that regarding the DES expansion, higher pressure should be applied at the inner surface of the stent.

An in silico trials platform for the evaluation of effect of the arterial anatomy configuration on stent implantation.

The introduction of Bioresorbable Vascular Scaffolds (BVS) has revolutionized the treatment of atherosclerosis. InSilc is an in silico clinical trial (ISCT) platform in a Cloud-based environment used for the design, development and evaluation of BVS. Advanced multi-disciplinary and multiscale models are integrated in the platform towards predicting the short/acute and medium/long term scaffold performance. In this study, InSilc platform is employed in a use case scenario and demonstrates how the whole in silico pipeline allows the interpretation of the effect of the arterial anatomy configuration on stent implantation.

A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for In Silico Clinical Trials of Stent Design.

Goal: To develop a cardiovascular virtual population using statistical modeling and computational biomechanics. Methods: A clinical data augmentation algorithm is implemented to efficiently generate virtual clinical data using a real clinical dataset. An atherosclerotic plaque growth model is employed to 3D reconstructed coronary arterial segments to generate virtual coronary arterial geometries (geometrical data). Last, the combination of the virtual clinical and geometrical data is achieved using a methodology that allows for the generation of a realistic virtual population which can be used in in silico clinical trials. Results: The results show good agreement between real and virtual clinical data presenting a mean gof 0.1 ± 0.08. 400 virtual coronary arteries were generated, while the final virtual population includes 10,000 patients. Conclusions: The virtual arterial geometries are efficiently matched to the generated clinical data, both increasing and complementing the variability of the virtual population.

Validation study of a novel method for the 3D reconstruction of coronary bifurcations.

Quantitative Coronary Angiography (QCA) is an important tool in the study of coronary artery disease. Validation of this technique is crucial for their ongoing development and refinement although it is difficult due to several factors such as potential sources of error. The present work aims to a further validation of a new semi-automated method for three-dimensional (3D) reconstruction of coronary bifurcations arteries based on X-Ray Coronary Angiographies (CA). In a dataset of 40 patients (79 angiographic views), we used the aforementioned method to reconstruct them in 3D space. The validation was based on the comparison of these 3D models with the true silhouette of 2D models annotated by an expert using specific metrics. The obtained results indicate a good accuracy for the most parameters (≥ 90 %). Comparison with similar works shows that our new method is a promising tool for the 3D reconstruction of coronary bifurcations and for application in everyday clinical use.

Design and implementation of in silico clinical trial for Bioresorbable Vascular Scaffolds.

In the recent years, Bioresorbable Vascular Scaffolds (BVS) for the treatment of atherosclerosis have been introduced. InSilc is a cloud based in silico clinical trial (ISCT) platform for drug-eluting BVS. The platform integrates multidisciplinary and multiscale models predicting the BVS performance. In this study, we present a use case scenario and demonstrate the functioning of the individual modules and of the whole pipeline and the ability to predict BVS short, medium, long-term outcomes.

InSilc: 3D Reconstruction and plaque characterization tool.

Coronary artery disease (CAD) is the leading cause of mortality in Europe and worldwide. Atherosclerosis is the most common pathologic process that is highly related with CAD, while the implantation of drug-eluting Bioresorbable Vascular Scaffolds (BVS) is the most promising procedure for treating patients with CAD. InSilc is an textbfin silico clinical trial (ISCT) platform for the development and assessment of drugeluting BVS. The InSilc platform provides insight in the performance of drug-eluting BVS in their short term and medium/long term through the Mechanical Modelling Module, the Deployment Module, the Fluid Dynamics Module, the Myocardial Perfusion Module, the Drug-delivery Module and the Degradation Module. In order for the aforementioned modules to be developed, the utilization of the reconstructed patient specific arterial segment and the BVS design are required, which is achieved through the 3D reconstruction and plaque characterization tool.In this study, the overall architecture of the InSilc platform is presented with special emphasis on the 3D reconstruction and plaque characterization tool. The tool will be able to implement different medical image processing workflows. The workflows will require minimum user intervention in order to be used in large scale clinical trials.

A Clinical Decision Support Platform for the Risk Stratification, Diagnosis, and Prediction of Coronary Artery Disease Evolution.

SMARTool aims to perform accurate risk stratification of coronary artery disease patients as well as to provide early diagnosis and prediction of disease progression. This is achieved by the acquisition of data from about 263 patients including computed tomography angiographic images, clinical, molecular, biohumoral, exposome, inflammatory and omics data. Data are collected in two time points with a followup period of approximately 5 years. In the first step, data mining techniques are implemented for the estimation of risk stratification. In the next step, patients, who are classified as medium to high risk are considered for coronary imaging and computational modelling of blood flow, plaque growth and stenosis severity assessment. Additionally, patients with increased stenosis are selected for stent deployment. All the above modules are integrated in a cloud-based platform for the clinical decision support (CDSS) of patients with coronary artery disease. The work presents preliminary results employing the SMARTool dataset as well as the concept and architecture of the under development platform.

KardiaTool: An Integrated POC Solution for Non-invasive Diagnosis and Therapy Monitoring of Heart Failure Patients.

The aim of this work is to present KardiaTool platform, an integrated Point of Care (POC) solution for noninvasive diagnosis and therapy monitoring of Heart Failure (HF) patients. The KardiaTool platform consists of two components, KardiaPOC and KardiaSoft. KardiaPOC is an easy to use portable device with a disposable Lab-on-Chip (LOC) for the rapid, accurate, non-invasive and simultaneous quantitative assessment of four HF related biomarkers, from saliva samples. KardiaSoft is a decision support software based on predictive modeling techniques that analyzes the POC data and other patient's data, and delivers information related to HF diagnosis and therapy monitoring. It is expected that identifying a source comparable to blood, for biomarker information extraction, such as saliva, that is cost-effective, less invasive, more convenient and acceptable for both patients and healthcare professionals would be beneficial for the healthcare community. In this work the architecture and the functionalities of the KardiaTool platform are presented.

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

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.

In silico assessment of the effects of material on stent deployment.

Coronary stents are expandable scaffolds that are used to widen occluded diseased arteries and restore blood flow. Because of the strain they are exposed to and forces they must resist as well as the importance of surface interactions, material properties are dominant. Indeed, a common differentiating factors amongst commercially available stents is their material. Several performance requirements relate to stent materials including radial strength for adequate arterial support post-deployment. This study investigated the effect of the stent material in three finite element models using different stents made of: (i) Cobalt-Chromium (CoCr), (ii) Stainless Steel (SS316L), and (iii) Platinum Chromium (PtCr). Deployment was investigated in a patient specific arterial geometry, created based on a fusion of angiographic data and intravascular ultrasound images. In silico results show that: (i) the maximum von Mises stress occurs for the CoCr, however the curved areas of the stent links present higher stresses compared to the straight stent segments for all stents, (ii) more areas of high inner arterial stress exist in the case of the CoCr stent deployment, (iii) there is no significant difference in the percentage of arterial stress volume distribution among all models.