Heart failure (HF) is the most rapidly growing cardiovascular condition with an estimated prevalence of >37.7 million individuals globally. HF is associated with increased mortality and morbidity and confers a substantial burden, in terms of cost and quality of life, for the individuals and the healthcare systems, highlighting thus the need for early and accurate diagnosis of HF. The accuracy of HF diagnosis, severity estimation and prediction of adverse events has been improved by the utilization of blood tests measuring biomarkers. The contribution of biomarkers for HF management is intensified by the fact that they can be measured in short time at the point-of-care. This is allowed by the development of portable analytical devices, commonly known as Point-of-Care Testing (POCT) devices, exploiting the advancements in the area of microfluidics and nanotechnology. The aim of this review paper is to present a review of POCT devices used for the measurement of biomarkers facilitating decision making when managing HF patients. The devices are either commercially available or in the form of prototypes under development. Both, blood and saliva samples are considered. The challenges concerning the implementation of POCT devices and the barriers for their adoption in clinical practice are discussed.

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. I. INTRODUCTION Heart Failure (HF) is a life-threatening disease, causing a significant economic burden on healthcare systems across the globe due to its high prevalence, the extended hospitalization requirements and the recurrent readmission of the patients [1]. More specifically, the European Heart Failure Association reports that 23 million people experience HF worldwide, while 3.6 million people are newly diagnosed with HF every year in

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. I. INTRODUCTION Heart Failure (HF) is a common clinical syndrome that affects approximately 2% of the adult population, ranging from less than 2% in population younger than 60 years old to 10% at ages over 75 years. Patients with HF have a poor prognosis with high short-and long-term mortality, high rehospitalization rates and severely impaired quality of life [1-3]. *Research supported by the KardiaTool project (http://www.kardiatool.eu/) that has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreement No 768686. This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains.

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