Nonalcoholic Fatty Liver Disease Is Associated With Higher 1-year All-Cause Rehospitalization Rates in Patients Admitted for Acute Heart Failure

Repeat hospitalization due to acute heart failure (HF) is a global public health problem that markedly impacts on health resource use. Identifying novel predictors of rehospitalization would help physicians to determine the optimal postdischarge plan for preventing HF rehospitalization. Nonalcoholic fatty liver disease (NAFLD) is an emerging risk factor for many heart diseases, including HF. We assessed whether NAFLD at hospital admission predicts 1-year all-cause rehospitalization in patients with acute HF.We enrolled all patients consecutively admitted for acute HF to our General Medicine Division, from January 2013 to April 2014, after excluding patients with acute myocardial infarction, severe heart valve diseases, malignancy, known liver diseases, and those with volume overload related to extracardiac causes. NAFLD was diagnosed by ultrasonography and exclusion of competing etiologies. The primary outcome of the study was the 1-year all-cause rehospitalization rate.Among the 107 patients enrolled in the study, the cumulative rehospitalization rate was 12.1% at 1 month, 25.2% at 3 months, 29.9% at 6 months, and 38.3% at 1 year. Patients with NAFLD had markedly higher 1-year rehospitalization rates than those without NAFLD (58% vs 21% at 1 y; P\u200a<\u200a0.001 by the log-rank test). Cox regression analysis revealed that NAFLD was associated with a 5.5-fold increased risk of rehospitalization (adjusted hazard ratio 5.56, 95% confidence interval 2.46-12.1, P\u200a<\u200a0.001) after adjustment for multiple HF risk factors and potential confounders.In conclusion, NAFLD was independently associated with higher 1-year rehospitalization in patients hospitalized for acute HF

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data

OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.</p

Nonalcoholic Fatty Liver Disease Is Associated With Ventricular Arrhythmias in Patients With Type 2 Diabetes Referred for Clinically Indicated 24-Hour Holter Monitoring

Recent studies have suggested that nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of heart rate-corrected QT interval prolongation and atrial fibrillation in patients with type 2 diabetes. Currently, no data exist regarding the relationship between NAFLD and ventricular arrhythmias in this patient population

AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

Early impairment in left ventricular longitudinal systolic function is associated with an increased risk of incident atrial fibrillation in patients with type 2 diabetes

It is known that type 2 diabetic patients are at high risk of atrial fibrillation (AF). However, the early echocardiographic determinants of AF vulnerability in this patient population remain poorly known

Data and methodologies for a resource-efficient planning of primary and secondary aggregates in South East Europe (SEE) countries

Recent, complete and reliable statistics on sources and quantities of primary and secondary aggregates, actual recycling rates, and the over- all aggregates requirements for the construction industry are core to resource-efficient planning. In the absence of such data, the objectives of the plan may be difficult or impossible to achieve, or plans created may be unrealistic or even counter-productive. It is therefore necessary to determine the degree to which these data are available, under whose jurisdiction their collec- tion, storage, and reporting falls, and whether they are currently used in planning. This is also the case in most South East Europe (SEE) coun- tries where, however, secondary aggregates are often not considered in planning for aggregates supply. The contribution of this paper is mostly focused on the definition of aggregates and the strategy to estimate the contribution of uncon- ventional aggregates, for which data gaps and uncertainties are clearly a bottlenec

Detection of the “Crossed Aorta Sign” during Echocardiography before Angiography

We report the case of an anomalous circumflex (Cx) origin from the right sinus of Valsalva with retroaortic course observed in a modified apical four-chamber view during transthoracic study (TTE). This finding is known as the “crossed aorta sign.” Usually, the diagnosis of this congenital anomaly of coronary circulation is established during coronary angiography. In this case, the diagnosis was performed by echocardiography before angiography. We believe that recent improvements in echocardiography increase the potential of this imaging technology also in the diagnosis of coronary artery anomalies

Nonalcoholic fatty liver disease is associated with aortic valve sclerosis in patients with type 2 diabetes mellitus.

BACKGROUND:Recent epidemiological data suggest that non-alcoholic fatty liver disease (NAFLD) is closely associated with aortic valve sclerosis (AVS), an emerging risk factor for adverse cardiovascular outcomes, in nondiabetic and type 2 diabetic individuals. To date, nobody has investigated the association between NAFLD and AVS in people with type 2 diabetes, a group of individuals in which the prevalence of these two diseases is high.METHODS AND RESULTS:We recruited 180 consecutive type 2 diabetic patients without ischemic heart disease, valvular heart disease, hepatic diseases or excessive alcohol consumption. NAFLD was diagnosed by liver ultrasonography whereas AVS was determined by conventional echocardiography in all participants. In the whole sample, 120 (66.7%) patients had NAFLD and 53 (29.4%) had AVS. No patients had aortic stenosis. NAFLD was strongly associated with an increased risk of prevalent AVS (odds ratio [OR] 2.79, 95% CI 1.3-6.1, p<0.01). Adjustments for age, sex, duration of diabetes, diabetes treatment, body mass index, smoking, alcohol consumption, hypertension, dyslipidemia, hemoglobin A1c and estimated glomerular filtration rate did not attenuate the strong association between NAFLD and risk of prevalent AVS (adjusted-OR 3.04, 95% CI 1.3-7.3, p\u200a=\u200a0.01).CONCLUSIONS:Our results provide the first demonstration of a positive and independent association between NAFLD and AVS in patients with type 2 diabetes mellitus