Obesity and cardiovascular risk. Systematic intervention is the key for prevention

Obesity is a serious public health issue and associated with an increased risk of cardiovascular disease events and mortality. The risk of cardiovascular complications is directly related to excess body fat mass and ectopic fat deposition, but also other obesity-related complications such as pre-type 2 diabetes, obstructive sleep apnoea, and non-alcoholic fatty liver diseases. Body mass index and waist circumference are used to classify a patient as overweight or obese and to stratify cardiovascular risk. Physical activity and diet, despite being key points in preventing adverse events and reducing cardiovascular risk, are not always successful strategies. Pharmacological treatments for weight reduction are promising strategies, but are restricted by possible safety issues and cost. Nonetheless, these treatments are associated with improvements in cardiovascular risk factors, and studies are ongoing to better evaluate cardiovascular outcomes. Bariatric surgery is effective in reducing the incidence of death and cardiovascular events such as myocardial infarction and stroke. Cardiac rehabilitation programs in obese patients improve cardiovascular disease risk factors, quality of life, and exercise capacity. The aim of this review was to critically analyze the current role and future aspects of lifestyle changes, medical and surgical treatments, and cardiac rehabilitation in obese patients, to reduce cardiovascular disease risk and mortality, and to highlight the need for a multidisciplinary approach to improving cardiovascular outcomes

Multimodality Imaging in Sarcomeric Hypertrophic Cardiomyopathy: Get It Right…on Time

Hypertrophic cardiomyopathy (HCM) follows highly variable paradigms and disease-specific patterns of progression towards heart failure, arrhythmias and sudden cardiac death. Therefore, a generalized standard approach, shared with other cardiomyopathies, can be misleading in this setting. A multimodality imaging approach facilitates differential diagnosis of phenocopies and improves clinical and therapeutic management of the disease. However, only a profound knowledge of the progression patterns, including clinical features and imaging data, enables an appropriate use of all these resources in clinical practice. Combinations of various imaging tools and novel techniques of artificial intelligence have a potentially relevant role in diagnosis, clinical management and definition of prognosis. Nonetheless, several barriers persist such as unclear appropriate timing of imaging or universal standardization of measures and normal reference limits. This review provides an overview of the current knowledge on multimodality imaging and potentialities of novel tools, including artificial intelligence, in the management of patients with sarcomeric HCM, highlighting the importance of specific "red alerts" to understand the phenotype-genotype linkage

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naive Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO(2) ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naive Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naive Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naive Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia

How athletes coped with COVID-19 restrictions: differences between Switzerland and Italy

The ongoing COVID-19 pandemic is a global crisis of unprecedented scale in modern times. The initial outbreak of COVID-19 in Wuhan spread rapidly, affecting other parts of China and soon other countries becoming a global threat. On 11 March 2020, the WHO has declared the ‘Pandemic state’ calling the governments to take ‘urgent and aggressive action’ to delay and mitigate the peak of infection. To respond to COVID-19 public health experts and government officials are taking several measures, including social distancing, self-isolation, or quarantine; strengthening health facilities to control the disease; and asking people to work at home. To safeguard the health of athletes and others involved all forms of organized sport have been either cancelled or postponed. These range from mass participation events such as marathon races to football league and even to the Olympics and Paralympics that for the first time in the history of the modern games, have been postponed, and will be held in 2021. All sport in Italy had been suspended from early March and from April the lockdown measures had been extended to the training session for professional and non-professional athletes within all sport facilities. Unlike Italy, the Swiss government has not imposed a general curfew so athletes continued to train outdoor although training in a group was forbidden. Some athletes in this situation will be able to build on existing coping resources while others athletes may experience psychological symptoms including fear of being infected, anxiety of physical recovery if infected, disturbed sleep, eating disorders, obsessive-compulsive disorder, and family conflicts

COVID-19: Relative Risk of Non-Vaccinated to Vaccinated Individuals

Following the outbreak of the COVID-19 pandemic, Italy has implemented an extensive vaccination campaign involving individuals above the age of 12, both sexes. The public opinion and the medical community alike questioned the usefulness and efficacy of the vaccines against SARS-CoV-2. The widespread opinion was that the vaccines protected individuals especially against serious conditions which could require intensive care and may lead to the death of the patient rather than against the possibility of infection. In order to quantify the effect of the vaccination campaign, we calculated the relative risks of non-vaccinated and vaccinated individuals for all possible outcomes of the disease: infection, hospitalization, admission to intensive care and death. Relative risk was assessed by means of likelihood ratios, the ratios of the probability of an outcome in non-vaccinated individuals to the probability of the same outcome in vaccinated individuals. Results support the hypothesis that vaccination has an extensive protective effect against both critical conditions and death. Nonetheless, the relative magnitude of the protection in vaccinated individuals compared to those non-vaccinated appears to be higher against the former outcome than the latter, for reasons which need to be investigated further

Active preference-based optimization for human-in-the-loop feature selection

In various classification problems characterized by a large number of features, feature selection (FS) is essential to guarantee generalization capabilities. The FS problem is often ill-posed due to significant correlations among features, which may lead to several different feature subsets with comparable scores in terms of classification performance. However, not all these subsets are equivalent from a domain-oriented point of view due to known relationships among features and their different acquisition costs in production to deploy the trained classifier. In this paper, we consider the potential benefits of including the domain expert's preferences in the FS task, thus integrating both objective elements (e.g., classification accuracy) and subjective (often not quantifiable) considerations in the selection process. This goes in the direction of increasing the interpretability and the trustworthiness of the machine learning model, which is an often desired property in many application domains such as in medicine. The proposed method consists of an iterative procedure. At each iteration, the expert is asked to express a "human" preference on pairs of classifiers, each one trained from a different subset of features. The expressed preferences are used algorithmically to update a suitable surrogate function that mimics the latent subjective expert's objective function, and then to propose a new classifier for testing and comparison. The proposed method has been tested on academic and experimental FS problems, and notably, on a COVID'19 patients record. The preliminary experimental results are promising, in that a parsimonious and accurate solution is obtained after a relatively short number of iterations. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved

The Central Nervous System and Psychosocial Factors in Primary Microvascular Angina

Patients diagnosed with ischemia without obstructive coronary artery disease (INOCA) comprise the group of patients with primary microvascular angina (MVA). The pathophysiology underlying ischemia and angina is multifaceted. Differences in vascular tone, collateralization, environmental and psychosocial factors, pain thresholds, and cardiac innervation seem to contribute to clinical manifestations. There is evidence suggesting potential interactions between the clinical manifestations of MVA and non-cardiac conditions such as abnormal function of the central autonomic network (CAN) in the central nervous system (CNS), pain modulation pathways, and psychological, psychiatric, and social conditions. A few unconventional non-pharmacological and pharmacological techniques targeting these psychosocial conditions and modulating the CNS pathways have been proposed to improve symptoms and quality of life. Most of these unconventional approaches have shown encouraging results. However, these results are overall characterized by low levels of evidence both in observational studies and interventional trials. Awareness of the importance of microvascular dysfunction and MVA is gradually growing in the scientific community. Nonetheless, therapeutic success remains frustratingly low in clinical practice so far. This should promote basic and clinical research in this relevant cardiovascular field investigating, both pharmacological and non-pharmacological interventions. Standardization of definitions, clear pathophysiological-directed inclusion criteria, crossover design, adequate sample size, and mid-term follow-up through multicenter randomized trials are mandatory for future study in this field