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.

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.

Effect of the endothelial shear stress patterns on neointimal proliferation following drug-eluting bioresorbable vascular scaffold implantation: an optical coherence tomography study.

OBJECTIVES: This study sought to investigate the effect of endothelial shear stress (ESS) on neointimal formation following an Absorb bioresorbable vascular scaffold (BVS) (Abbott Vascular, Santa Clara, California) implantation. BACKGROUND: Cumulative evidence, derived from intravascular ultrasound-based studies, has demonstrated a strong association between local ESS patterns and neointimal formation in bare-metal stents, whereas in drug-eluting stents, there are contradictory data about the effect of ESS on the vessel wall healing process. The effect of ESS on neointimal development following a bioresorbable scaffold implantation remains unclear. METHODS: Twelve patients with an obstructive lesion in a relatively straight arterial segment, who were treated with an Absorb BVS and had serial optical coherence tomographic examination at baseline and 1-year follow-up, were included in the current analysis. The optical coherence tomographic data acquired at follow-up were used to reconstruct the scaffolded segment. Blood flow simulation was performed on the luminal surface at baseline defined by the Absorb BVS struts, and the computed ESS was related to the neointima thickness measured at 1-year follow-up. RESULTS: At baseline, the scaffolded segments were exposed to a predominantly low ESS environment (61% of the measured ESS was <1 Pa). At follow-up, the mean neointima thickness was 113 ± 45 µm, whereas the percentage scaffold volume obstruction was 13.1 ± 6.6%. A statistically significant inverse correlation was noted between baseline logarithmic transformed ESS and neointima thickness at 1-year follow-up in all studied segments (correlation coefficient range -0.140 to -0.662). Mixed linear regression analysis between baseline logarithmic transformed ESS and neointima thickness at follow-up yielded a slope of -31 µm/ln(Pa) and a y-intercept of 99 µm. CONCLUSIONS: The hemodynamic microenvironment appears to regulate neointimal response following an Absorb BVS implantation. These findings underline the role of the ESS patterns on vessel wall healing and should be taken into consideration in the design of bioresorbable devices.

Segmentation of microarray images using pixel classification-comparison with clustering-based methods.

OBJECTIVE: DNA microarray technology yields expression profiles for thousands of genes, in a single hybridization experiment. The quantification of the expression level is performed using image analysis. In this paper we introduce a supervised method for the segmentation of microarray images using classification techniques. The method is able to characterize the pixels of the image as signal, background and artefact. METHODS AND MATERIAL: The proposed method includes five steps: (a) an automated gridding method which provides a cell of the image for each spot. (b) Three multichannel vector filters are employed to preprocess the raw image. (c) Features are extracted from each pixel of the image. (d) The dimension of the feature set is reduced. (e) Support vector machines are used for the classification of pixels as signal, background, artefacts. The proposed method is evaluated using both real images from the Stanford microarray database and simulated images generated by a microarray data simulator. The signal and the background pixels, which are responsible for the quantification of the expression levels, are efficiently detected. RESULTS: A quality measure (qindex) and the pixel-by-pixel accuracy are used for the evaluation of the proposed method. The obtained qindex varies from 0.742 to 0.836. The obtained accuracy for the real images is about 98%, while the accuracies for the good, normal and bad quality simulated images are 96, 93 and 71%, respectively. The proposed classification method is compared to clustering-based techniques, which have been proposed for microarray image segmentation. This comparison shows that the classification-based method reports better results, improving the performance by up to 20%. CONCLUSIONS: The proposed method can be used for segmentation of microarray images with high accuracy, indicating that segmentation can be improved using classification instead of clustering. The proposed method is supervised and it can only be used when training data are available.

A new methodology for accurate 3-dimensional coronary artery reconstruction using routine intravascular ultrasound and angiographic data: implications for widespread assessment of endothelial shear stress in humans.

AIMS: To develop and validate a new methodology that allows accurate 3-dimensional (3-D) coronary artery reconstruction using standard, simple angiographic and intravascular ultrasound (IVUS) data acquired during routine catheterisation enabling reliable assessment of the endothelial shear stress (ESS) distribution. METHODS AND RESULTS: Twenty-two patients (22 arteries: 7 LAD; 7 LCx; 8 RCA) who underwent angiography and IVUS examination were included. The acquired data were used for 3-D reconstruction using a conventional method and a new methodology that utilised the luminal 3-D centreline to place the detected IVUS borders and anatomical landmarks to estimate their orientation. The local ESS distribution was assessed by computational fluid dynamics. In corresponding consecutive 3 mm segments, lumen, plaque and ESS measurements in the 3-D models derived by the centreline approach were highly correlated to those derived from the conventional method (r>0.98 for all). The centreline methodology had a 99.5% diagnostic accuracy for identifying segments exposed to low ESS and provided similar estimations to the conventional method for the association between the change in plaque burden and ESS (centreline method: slope= -1.65%/Pa, p=0.078; conventional method: slope= -1.64%/Pa, p=0.084; p =0.69 for difference between the two methodologies). CONCLUSIONS: The centreline methodology provides geometrically correct models and permits reliable ESS computation. The ability to utilise data acquired during routine coronary angiography and IVUS examination will facilitate clinical investigation of the role of local ESS patterns in the natural history of coronary atherosclerosis.

Polymerase chain reaction (PCR) and sequence specific oligonucleotide probes (SSOP) genotyping assay for detection of genes associated with rheumatoid arthritis and multiple sclerosis.

In this paper an assay for the detection of genes associated with rheumatoid arthritis (RA) and multiple sclerosis, using polymerase chain reaction (PCR) and sequence specific oligonucleotide probes (SSOP) is presented, in order to be further applied in a portable Lab-On-Chip (LOC) device. A substantial part of these reagents were based on the literature (11th International Histocompatibility Workshop, IHW), bearing the advantage of proven successful implementation in genotyping, while others were designed for this study. More precisely, our methodology discriminates HLA-DRB1 as DRB1*01, *04 and *10, which include shared epitope (SE) alleles associated with RA and additionally DRB1*15 allele, including DRB1*1501 associated with MS (broad genotyping method). To further present the basic elements of the assay for high resolution genotyping of SE DRB1 alleles, we provide as an example the case of HLA-DRB1*10 alleles (HLADRB1* 100101, *100102, *100103, *1002 and *1003). Regarding the methodology for developing a detection assay, for SNPs associated with RA or MS the basic steps are presented. DNA sequence data are obtained from IMGT/HLA and SNP database. Online software tools are used to define hybridization specificity of primers and probes towards human DNA, leading to hybridization patterns that uniquely designate a target allele and evaluate parameters influencing PCR efficiency. Respecting current technological limitations of autonomous molecular-based LOC systems the approach of broad genotyping of HLA-DRB1*01/*04/*10/*15 genes, is intended to be initially used, leaving, high resolution genotyping of SE alleles for future implementations. This method is easy to be updated and extended to detect additional associated loci with RA or MS.

Developing a genomic-based point-of-care diagnostic system for rheumatoid arthritis and multiple sclerosis.

In this paper the methodology of designing a genomic-based point-of-care diagnostic system composed of a microfluidic Lab-On-Chip, algorithms for microarray image information extraction and knowledge modeling of clinico-genomic patient data is presented. The data are processed by genome wide association studies for two complex diseases: rheumatoid arthritis and multiple sclerosis. Respecting current technological limitations of autonomous molecular-based Lab-On-Chip systems the approach proposed in this work aims to enhance the diagnostic accuracy of the miniaturized LOC system. By providing a decision support system based on the data mining technologies, a robust portable integrated point-of-care diagnostic assay will be implemented. Initially, the gene discovery process is described followed by the detection of the most informative SNPs associated with the diseases. The clinical data and the selected associated SNPs are modeled using data mining techniques to allow the knowledge modeling framework to provide the diagnosis for new patients performing the point-of-care examination. The microfluidic LOC device supplies the diagnostic component of the platform with a set of SNPs associated with the diseases and the ruled-based decision support system combines this genomic information with the clinical data of the patient to outcome the final diagnostic result.

ANGIOCARE: an automated system for fast three-dimensional coronary reconstruction by integrating angiographic and intracoronary ultrasound data.

OBJECTIVES: The development of an automated, user-friendly system (ANGIOCARE), for rapid three-dimensional (3D) coronary reconstruction, integrating angiographic and, intracoronary ultrasound (ICUS) data. METHODS: Biplane angiographic and ICUS sequence images are imported into the system where a prevalidated method is used for coronary reconstruction. This incorporates extraction of the catheter path from two end-diastolic X-ray images and detection of regions of interest (lumen, outer vessel wall) in the ICUS sequence by an automated border detection algorithm. The detected borders are placed perpendicular to the catheter path and established algorithms used to estimate their absolute orientation. The resulting 3D object is imported into an advanced visualization module with which the operator can interact, examine plaque distribution (depicted as a color coded map) and assess plaque burden by virtual endoscopy. RESULTS: Data from 19 patients (27 vessels) undergoing biplane angiography and ICUS were examined. The reconstructed vessels were 21.3-80.2 mm long. The mean difference was 0.9 +/- 2.9% between the plaque volumes measured using linear 3D ICUS analysis and the volumes, estimated by taking into account the curvature of the vessel. The time required to reconstruct a luminal narrowing of 25 mm was approximately 10 min. CONCLUSION: The ANGIOCARE system provides rapid coronary reconstruction allowing the operator accurately to estimate the length of the lesion and determine plaque distribution and volume.