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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 naïve 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/FiO2 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 naïve 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 naïve 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 naïve 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.

Background COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. Materials and methods We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot. Results 242 patients were included [median age, 64 years (56–71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6–18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). Conclusions We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

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.

Despite a relative contraindication, mechanical support with Impella ™ left ventricular assist device has already been described for ischaemic ventricular septal defect treatment, either as a bridge to surgery, as intraoperative mechanical haemodynamic support, or to ensure intraprocedural haemodynamic stability during device closure. We describe two cases of ventricular septal defect complicating acute myocardial infarction, where the percutaneous ImpellaCP was implanted early (differently than previously described) with the aim of preventing haemodynamic instability, while deferring surgical repair. We present a report of haemodynamic, echocardiographic, biochemical, and clinical data of two consecutive cases of ImpellaCP use, within a minimally invasive monitoring and therapeutic approach. In two cases of subacute myocardial infarction-related ventricular septal defect not amenable to percutaneous device closure, the use ImpellaCP was successful: it was followed by effective and rapid right and left ventricular unloading, by major haemodynamic instability prevention and protection from systemic venous congestion, from kidney and splanchnic organ failures. This allowed bridging to appropriately timed surgical repair. These cases suggest a potentially effective, clinically grounded strategy in the early management of ischaemic ventricular septal defect patients, with the aim of deferring surgery beyond the safer 7 days cutoff associated with a lower perioperative mortality.