The target of HEARTEN is to integrate different components, i.e. the sensor kit, the mHealth apps for the different actors, and the knowledge management system, in a validated ICT co-operative environment that will engage all the ecosystem actors with the final aim to improve the HF patients’ management. HEARTEN user needs identification, identified relevant users and stakeholders of the HF management ecosystem. A detailed analysis on their skills and needs was performed. Following this, we created different user interaction scenarios covering the following cases; (i) Patient and healthcare professional interaction, (ii) Patient and caregiver interaction, and (iii) Educational activity. Based also on state of art analysis, HEARTEN architecture covers the missing features of these projects, such as the creation of a patient-centered ecosystem that allows for the communication among users taking advantage of the available technological solutions, such as smartphones, web applications and cloud computing services in an efficient and effective manner. Special emphasis was given on defining the concept for engaging all the actors to the HF patient management focusing on different interaction scenarios between the patient and each of the identified HEARTEN actors (healthcare professional, caregiver, nutritionist, physical activity expert, and psychologist). HEARTEN platform goes beyond the monitoring of the patient’s status and lays in the creation of a supportive environment that empowers the patients in self-management towards improving the health outcomes and overall quality of life. As far as WP4 is concerned the basic idea is to develop specific biosensors that determine novel breath and saliva HF biomarkers, previously identified during a dedicated pre-pilot trial. The breath and saliva biosensor will be integrated in a sensor device that includes additional commercial sensors for monitoring the ECG, the blood pressure, the heart and respiratory frequency, the body weight and the physical activity. The innovative contribution provided to the project by the activities of the WP4 focused first on the identification of a preliminary panel of biomarkers in breath and saliva, then on the development of validated analytical methods for their determination and finally in the selection of the best suited biomarkers on the basis of the results obtained during a pre-pilot trial.
Based on the final list of selected biomarkers, the corresponding biosensors were developed as part of the WP5 activities. For breath biosensors, no devices are available for the detection of volatile organic compounds for monitoring of HF patients. The project has successfully developed acetone sensors that operate at room temperature and have a detection limit of 1 ppm. This is not adequate for general purpose HF monitoring because the median exhaled acetone concentration for healthy people is 0.65 ppm, but it is suggested that the sensors can be used for HF patients with diabetes mellitus because in this case the ranges are higher.
No biosensors are available for the detection of biomarkers relevant for HF monitoring in saliva. The project has successfully developed biosensors for TNF-α, cortisol, IL-10 and NT-proBNP with the required sensitivity and selectivity. However, for IL-10 no validation has been achieved due to the lack of results from standard analytical methods, and for NT-proBNP results are only available for artificial saliva. Quantitative measurements in real saliva samples have required the use of the standard addition method. In the project for the first time the standard addition method and impedance spectroscopy have been used for quantitative measurements of biomarkers in real saliva samples. The biosensors developed operate with a high performance demonstrated by good response, recovery, stability and repeatability in the complex saliva matrix. The KMS developed in this WP is a novel tool supporting management of HF patients, consisting of 9 distinct modules. Combining different patient data (i.e. clinical, sensor –clinical and movement-, nutrition and biomarker related data) the Hearten KMS provides a novel solution for management and self-management of HF patients: it monitors patient status in an objective way (instead of the subjective NYHA class estimation), it early identifies adverse events, estimates risk for non adherence, and it estimated treatment adherence in terms of medication, nutrition and physical activity. Additionally, it supports research and hypothesis testing. In cases were a comparison to relevant approaches in literature is feasible –even if implemented differently and using different data (i.e NYHA class detection and adverse event prediction)- KMS demonstrates in general higher performance (e.g. in terms of accuracy). Using data mining/machine learning techniques and a novel selection and combination of different types of patient data the HEARTEN KMS achieves high diagnostic and prognostic accuracy. All the above are offered through a novel cloud setting. Concerning the app, an innovative, yet easy to use system was designed and developed which allows for efficient management of a heart failure patient.
