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.

Computational Modelling of Cardiac Electrophysiology: from Cell to Bedside

Heart diseases are the leading cause of death worldwide, both for men and women. However, the ionic mechanisms underlying many cardiac arrhythmias and genetic disorders are not completely understood, thus leading to a limited efficacy of the current available therapies and leaving many open questions for cardiac electrophysiologists. On the other hand, experimental data availability is still a great issue in this field: most of the experiments are performed in vitro and/or using animal models (e.g. rabbit, dog and mouse), even when the final aim is to better understand the electrical behaviour of in vivo human heart either in physiological or pathological conditions. Computational modelling constitutes a primary tool in cardiac electrophysiology: in silico simulations, based on the available experimental data, may help to understand the electrical properties of the heart and the ionic mechanisms underlying a specific phenomenon. Once validated, mathematical models can be used for making predictions and testing hypotheses, thus suggesting potential therapeutic targets. This PhD thesis aims to apply computational cardiac modelling of human single cell action potential (AP) to three clinical scenarios, in order to gain new insights into the ionic mechanisms involved in the electrophysiological changes observed in vitro and/or in vivo. The first context is blood electrolyte variations, which may occur in patients due to different pathologies and/or therapies. In particular, we focused on extracellular Ca2+ and its effect on the AP duration (APD). The second context is haemodialysis (HD) therapy: in addition to blood electrolyte variations, patients undergo a lot of other different changes during HD, e.g. heart rate, cell volume, pH, and sympatho-vagal balance. The third context is human hypertrophic cardiomyopathy (HCM), a genetic disorder characterised by an increased arrhythmic risk, and still lacking a specific pharmacological treatment.

Development and validation of an echocardiography score for diagnosis of cardiac masses

Background Echocardiography is the cornerstone in the evaluation of cardiac masses and provides accurate characterization. Despite, its accuracy in diagnosis of cardiac masses (CM) remains challenging and, up to date, no validated diagnostic algorithm is validated. Purpose The aim of our study was to evaluate the diagnostic accuracy of echocardiography, to identify the echocardiographic predictors of malignancy and to develop and then validate a multiparametric echocardiographic score that could be used to estimate the likelihood of the histological nature of a CM. Materials and methods The final sample consisted of 273 consecutive patients who had a 2D-echocardiographic evaluation and a histologic diagnosis. Logistic regression was performed to evaluate the ability of echocardiographic findings to discriminate benign versus malignant masses, then a scoring system was developed and validated in a separate test cohort. Results Of the 322 patients initially included in the Bologna Cardiac Masses Registry, 13 with a poor acoustic window, 27 with no histological examination patients and 9 extra-cardiac masses were excluded. In the remaining 273 patients, classical 2-D echocardiogram identified 249 masses with a diagnostic accuracy of 88%. A weighted score [Diagnostic Echocardiographic Mass (DEM) Score] ranging from 0 to 9 was obtained from 6 variables: infiltration, polylobate mass, moderate-severe pericardial effusion. The AUC for the score was 0.965 (95% CI [0.938-0.993]). In a logistic regression analysis using the DEM score as a predictor, the likelihood of malignant CM increased more than 4 times for a 1-unit increase in the score (OR=4.468; 95% CI 2.733-7.304). A score < 3 denoted a high probability of a benign diagnosis, and a score ≥ 5 points corresponded to a higher risk of malignancy. Conclusion 2D-Echocardiography provides a high diagnostic accuracy in identifying cardiac masses and our multiparametric echocardiographic score could be useful to predict the histological nature of cardiac masses.