The medical management after surgery for advanced heart failure

The aim of the Research of this Ph D Project is to improve the medical management after surgery for advanced heart failure, both after left ventricular assist devices (LVAD) implantation, and after heart transplantation in the long-term. Regarding heart transplantation (HTx), the Research Project is focused on diagnostics, classification, prevention and treatment of cardiac allograft vasculopathy (CAV), and on treatment of post-HTx cancers; the results are presented in the first part of this Thesis. In particular, the main aspect investigated are the prognostic role of information derived from coronary angiography, coronary tomography and intravascular ultrasound, and the different sensitivity of these techniques in predicting outcomes and in diagnosing CAV. Moreover, the role of mTOR inhibitors on CAV prevention or treatment is investigated, both alone and in combination with different anti-CMV prevention strategies, as well as the impact of mTOR inhibitors on clinical outcomes in the long term. Regarding LVAD, the main focus is on the role of transthoracic echocardiography in the management of patients with a continuous-flow, centrifugal, intrapericardial pump (HVAD, Heartware); this section is reported in the second part of this Thesis. The main aspects investigated are the use of echocardiography in patients with HVAD device and its interaction with the information derived from pump curves' analysis in predicting aortic valve opening status, a surrogate of the condition of support provided by the LVAD.

Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment

Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals. Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients. Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients. Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible. The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations.