Background: The population at risk, the clinical and microbiological features of infective endocarditis (IE) have changed. Aim of our study was to evaluate the contemporary epidemiological trends, over a 17-year period in a definite region of Tuscany, Italy, to analyze the clinical outcomes and associated prognostic factors.Methods: From 1 January 1998 to 31 December 2014, all patients with a definite diagnosis of IE were prospectively entered in a data-base. The Health-Care system data-base was interrogated to capture patients who could have been missed. The final dataset derived by the merging of the two data-bases.Results: Incidence rate of IE was 4.6/100,000/y with a significant linear incidence increase. In hospitalized patients the incidence was 1.27/1,000 admissions. Over age 65 incidence rate was 11.7/100,000/y.Male/female ratio was 1.54:1. A temporal trend towards an increase in the mean population age was found (P=0.033). There was an increase in the incidence of Health-care associated IE, P=0.016. The most common microorganisms were staphylococcus aureus (25%) and coagulase-negative staphylococci (22%).In-hospital mortality was 24%. A trend towards an increase in mortality rate was found (P=0.055).Independent predictors of mortality were older age, S. aureus infection, heart failure, septic shock and persistent bacteremia. Conclusions:Our study confirms an increasing mortality trend in IE, although with a borderline significance. Elderly forms are associated with poor prognosis and higher than 1-year mortality rate even in the multivariate analysis. Ageing population, increase in healthcare-associated and staphylococcal infections, may explain the rise of IE incidence and of the mortality trend.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer’s disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with in total 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Aims:The aim of our study was to evaluate the prevalence of left atrial cavity and appendage thrombosis in patients undergoing cardioversion for non-valvular atrial tachyarrhythmias. In persistent atrial tachyarrhythmias, 90% of thromboses are reported to be located inside the left atrial appendage. This prevalence refers to old studies and meta-analysis in a mixed population of valvular and non-valvular atrial fibrillation. Left atrial cavity thrombosis in non-valvular atrial fibrillation has not been investigated recently in largescale studies.Methods and results: A total of 1,420 consecutive adult patients with paroxysmal or persistent atrial tachyarrhythmias, candidates to cardioversion, who opted for a transoesophageal echocardiography-guided strategy, were enrolled in the study. Mitral stenosis, rheumatic valve disease and mechanical prostheses were excluded. In total there were 91 thrombi in 87 patients with a prevalence of 6.13% (87/1,420). Patients with left atrial thrombosis had predisposing clinical and echo characteristics (heart failure, lower ventricular function and higher atrial volume). Except for one case in which the thrombus was located in the left atrial cavity (0.07%), and three in the right appendage, all thromboses were detected in the left atrial appendage.Conclusions: Extra-appendage thrombosis is a very rare finding in non-valvular persistent and paroxysmal atrial tachyarrhythmias and, when present, a left appendage thrombus is usually concomitant.

Longitudinal volume measurements can provide meaningful clinical insight and added value with respect to the baseline provided the analysis procedure embeds the longitudinal information.

The aim of our study was to evaluate a quantitative ultrasound technique for measuring bone tissue at the proximal phalanxes of the non-dominant hand. We correlated the mean value of the amplitude-dependent speed of sound (AD-SoS) measured at the distal metaphysis of the last four proximal phalanxes with age, months since menopause and bone mineral density (BMD) of the lumbar spine in 264 women. We further assessed the ability of the AD-SoS to discriminate between normal and osteoporotic subjects with documented vertebral fractures. We found a positive correlation between the AD-SoS and the lumbar spine BMD, whereas the AD-SoS negatively correlated with age and months since menopause. The AD-SoS showed a higher correlation with age changes and months since menopause than BMD. The AD-SoS was significantly higher in healthy females than in osteoporotic ones (p < 0.001). Multiple logistic regression analysis showed for age-adjusted values that AD-SoS decrease is significantly associated to the presence of fracture. Our results suggest that AD-SoS is valuable in assessing age and menopause related bone loss and is useful for diagnosing osteoporosis.

Background: Despite ventricular septal defects (VSDs) are the most common congenital heart

The assessment of in vivo18F images targeting amyloid deposition is currently carried on by visual rating with an optional quantification based on standardized uptake value ratio (SUVr) measurements. We target the difficulties of image reading and possible shortcomings of the SUVr methods by validating a new semi-quantitative approach named ELBA. ELBA involves a minimal image preprocessing and does not rely on small, specific regions of interest (ROIs). It evaluates the whole brain and delivers a geometrical/intensity score to be used for ranking and dichotomic assessment. The method was applied to adniimages 18F-florbetapir images from the ADNI database. Five expert readers provided visual assessment in blind and open sessions. The longitudinal trend and the comparison to SUVr measurements were also evaluated. ELBA performed with area under the roc curve (AUC) = 0.997 versus the visual assessment. The score was significantly correlated to the SUVr values (r = 0.86, p < 10-4). The longitudinal analysis estimated a test/retest error of ≃2.3%. Cohort and longitudinal analysis suggests that the ELBA method accurately ranks the brain amyloid burden. The expert readers confirmed its relevance in aiding the visual assessment in a significant number (85) of difficult cases. Despite the good performance, poor and uneven image quality constitutes the major limitation.

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access