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The objective of the study is to assess the efect of thrombolitic therapy on clinical prognostic markers in patients with intermediate-high risk pulmonary embolism compared to clasic therapy, and also the assessment of the bleeding risk associated with thrombolitic therapy. Material and methods. We included 82 consecutive patients with intermediate-high risk pulmonary embolism, which were divided in two groups: study group (thrombolysed) and control group (treated with unfractioned heparine alone). Patients with lower bleeding risk were included in control group. Patients were assessed regarding the right ventricle dysfunction markers (both imagistic and biochemical) on admision and on 48 h. The early (7th day) mortality rate and hemodinamic instability rate were evaluated in the two groups, also the major bleeding rate. Outcomes. We proved a statistical significant lower mortality rate in the study group, with no difference regarding the major bleedings. Meanwhile, the hemodinamic instability was lower in the study group compared to control group. Conclusions. The proper patient selection with intermediate-high pulmonary embolism for thrombolitic therapy can bring important benefits in terms of mortality and bleeding complications

Introduction Due to the aging of the population we are confronting ourselves with an increased number of patients with chronic heart failure which stands a prevalence of over 37,7 million cases worldwide, being a major public health issue, first by the substantial morbidity and mortality that carries and second by the economic burden it brings annually upon the health-care system. The main purpose of this study is to test a machine learning algorithm which can predict, through voice analysis the acutisation of heart failure, considering the particularities of the patient's voice. Methods The data we have used has been collected from a total of 16 patients, 9 men and 7 women, ages between 65 and 91 years old, who have agreed to take part in the study. The selective criteria of inclusion has been the cause of hospitalization, selecting only the patients presented with cardiogenic acute pulmonary edema, regardless the precipitation cause or other known cardiovascular comorbidities. With the same electronic device we have recorded each patient's voice, twice a day, starting from the day one of hospitalization, when their general status was critical, until the day of discharge, when they were clinically stable. Each voice recording containing specific keywords has been associated to the most used classification system for heart failure, the New York Heart Association Functional Classification and introduced into the machine learning algorithm. Results After integrating the information from 15 patients, the algorithm has classified correctly the 16th patient into the third NYHA stage, based only on his voice recording. Conclusion Voice is a cheap and easy way to monitor a patient's health status. The algorithm we have used for analyzing the voice provides high accuracy results, but for a larger dataset it might not be the best choice as it is computationally expensive. We are looking to obtain larger datasets and to compute more complex voice analyzer algorithms. Funding Acknowledgement Type of funding sources: None.

Background: Advanced disease prevention solutions using telemedicine are vital for the future in order to decrease the burden produced by the cardio-vascular diseases. This solutions need to ensure more accessibility, high performance and good data privacy. Material and Method: Study groups consist of 48 acute myocardial infarction patients: 24 home monitored using telemedicine and 24 control group performing unmonitored rehabilitation. The process will have two phases: the inpatient period will be common (5-7 days) and the outpatient period (7 weeks). The monitored group will receive a set of Bluetooth capable monitoring devices at discharge, a blood pressure device, a cardio watch, a pulsoxymeter, an ECG chest strap and a Smartphone with the application installed. The application that will be built from scratch will contain the nutritional, medical and physical activity plan and also has monitoring and treatment adjustment functions by the medical personnel Results: Before hospital discharge and at the end of the program all patients will perform an ECG treadmill test, which summed with other medical investigations, will quantify the cardiac fitness level and their improvement. Also using a MAST personalized model there will be assessed a set of performance indicators. Conclusions: The Online Cardiac rehabilitation solution will be a light and secure software, built using Specific Enablers, technology created as generic enablers in the FI-WARE project. This is hoped to offer more patient independence, improve their cardiac fitness and quality of life and reduces the high cost that cardiovascular disease brings to the medical system.