Balloon Aortic Valvuloplasty as Bridge-to-Decision in High Risk Patients with Severe Aortic Valve Stenosis

Background. A sizable group of patients with symptomatic aortic stenosis (AS) can undergo neither surgical aortic valve replacement (AVR) nor transcatheter aortic valve implantation (TAVI) because of clinical contraindications. The aim of this study was to assess the potential role of balloon aortic valvuloplasty (BAV) as a “bridge-to-decision” in selected patients with severe AS and potentially reversible contraindications to definitive treatment. Methods. We retrospectively enrolled 645 patients who underwent first BAV at our Institution between July 2007 and December 2012. Of these, the 202 patients (31.2%) who underwent BAV as bridge-to-decision (BTD) requiring clinical re-evaluation represented our study population. BTD patients were further subdivided in 5 groups: low left ventricular ejection fraction; mitral regurgitation grade ≥3; frailty; hemodynamic instability; comorbidity. The main objective of the study was to evaluate how BAV influenced the final treatment strategy in the whole BTD group and in its single specific subgroups. Results. Mean logistic EuroSCORE was 23.5±15.3%, mean age was 81±7 years. Mean transaortic gradient decreased from 47±17 mmHg to 33±14 mmHg. Of the 193 patients with BTD-BAV who received a second heart team evaluation, 72.5% were finally deemed eligible for definitive treatment (25.4%for AVR; 47.2% for TAVI): respectively, 96.7% of patients with left ventricular ejection fraction recovery; 70.5% of patients with mitral regurgitation reduction; 75.7% of patients who underwent BAV in clinical hemodynamic instability; 69.2% of frail patients and 68% of patients who presented relevant comorbidities. 27.5% of the study population was deemed ineligible for definitive treatment and treated with standard therapy/repeated BAV. In-hospital mortality was 4.5%, cerebrovascular accident occurred in 1% and overall vascular complications were 4% (0.5% major; 3.5% minor). Conclusions. Balloon aortic valvuloplasty should be considered as bridge-to-decision in high-risk patients with severe aortic stenosis who cannot be immediate candidates for definitive percutaneous or surgical treatment.

Nuove acquisizioni in termini di insufficienza mitralica: complicanze tromboemboliche dopo chirurgia della valvola mitrale: incidenza, predittori e implicazioni cliniche

Objectives: to define in patients undergoing surgery for mitral regurgitation (MR) the risk of thrombo-embolic complications, particularly ischemic stroke (IS) compared to that in the general population. Background: MR is frequent, occurs mostly in the elderly and guidelines recommend surgery in asymptomatic patients but IS risks are unknown. Methods: in 1344 patients (65±12 years) consecutively operated for MR (procedures: 897 valve repair, MRep; 447 valve replacement, 231 mechanical, MVRm; 216 biological, MVRb), thrombo-embolic complications particularly IS (diagnosed by a neurologist) during follow-up were assessed early (<30 days), mid-term (30-180 days) and long-term (180 days). Results: IS occurred in 130 patients and IS or transient ischemic attack in 201. IS rates were 1.9±0.4% and 2.7±0.5%, at 30 and 180 days and 8.1±0.8% at 5 years. IS rates were lowest after MRep vs. MVRb and MVRm (6.1±0.9, 8±2.1 and 16.1±2.7% at 5 years, p<0.001). Comparison to IS expected rates in the population showed high risk within 30 days of surgery (Risk-ratio 41[26-60], p<0.001 but p>0.10 between procedures) and moderate risk after 30 days (risk-ratio 1.7 overall, p<0.001; 1.3 for MRep, p=0.07; 0.98 for MVRb, p=0.95; 4.8 for MVRm, p<0.001). Beyond 180 days, IS risk declined further and was not different from the general population for MRep (1.2, p=0.30) and for MVRb (0.9, p=0.72). Risk of IS or transient ischemic attack was higher than the general population in all groups up to 180 days. The risk of bleeding beyond 30 days was lowest in MRep vs. MVRb and MVRm (7±1, 14±4 and 16±3% at 10 years, p<0.001). Conclusion: thrombo-embolic complications after MR surgery are both reason for concern and encouragement. IS risk is notable early, irrespective of the procedure performed, but long-term is not higher than in the general population after MRep and MVRb. Preference for MRep should be emphasized and trials aimed at preventing IS should be conducted to reduce the thrombo-embolic and hemorrhagic risk in patients undergoing surgery for MR.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

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.

Profili di espressione genetica ed epigenetica ad impatto prognostico e predittivo nel carcinoma squamoso e nelle lesioni potenzialmente maligne del cavo orale

Il carcinoma squamoso orale (CSO) è spesso preceduto da lesioni definite potenzialmente maligne tra cui la leucoplachia e il lichen ma una diagnosi precoce avviene ancora oggi in meno della metà dei casi. Inoltre spesso un paziente trattato per CSO svilupperà secondi tumori. Scopo del lavoro di ricerca è stato: 1) Studiare, mediante metodica di next generation sequencing, lo stato di metilazione di un gruppo di geni a partire da prelievi brushing del cavo orale al fine di identificare CSO o lesioni ad alto rischio di trasformazione maligna. 2) Valurare la relazione esistente tra sovraespressione di p16INK4A e presenza di HPV in 35 pazienti affetti da lichen 3) Valutare la presenza di marker istopatologici predittivi di comparsa di seconde manifestazioni tumorali 4) valutare la relazione clonale tra tumore primitivo e metastasi linfonodale in 8 pazienti mediante 2 metodiche di clonalità differenti: l’analisi di mtDNA e delle mutazioni del gene TP53. I risultati hanno mostrato: 1) i geni ZAP70 e GP1BB hanno presentato un alterato stato di metilazione rispettivamente nel 100% e nel 90,9% di CSO e lesioni ad alto rischio, mentre non sono risultati metilati nei controlli sani; ipotizzando un ruolo come potenziali marcatori per la diagnosi precoce nel CSO. 2)Una sovraespressione di p16INK4A è risultata in 26/35 pazienti affetti da lichen ma HPV-DNA è stato identificato in soli 4 campioni. Nessuna relazione sembra essere tra sovraespressione di p16INK4A e virus HPV. 3)L’invasione perineurale è risultato un marker predittivo della comparsa di recidiva locale e metastasi linfonodale, mentre lo stato dei margini chirurgici si è rilevato un fattore predittivo per la comparsa di secondi tumori primitivi 4) Un totale accordo nei risultati c’è stato tra analisi di mtDNA e analisi di TP53 e le due metodiche hanno identificato la presenza di 4 metastasi linfonodali non clonalmente correlate al tumore primitivo.

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.