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Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission.We studied an Italian cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 who were hospitalized in 13 cardiology units during Spring 2020. The Lasso procedure was used to select the most relevant covariates. The dataset was randomly divided into a training set containing 80% of the data, used for estimating the model, and a test set with the remaining 20%. A Random Forest modeled in-hospital mortality with the selected set of covariates: its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity and related 95% confidence interval (CI). This model was then compared with the one obtained by the Gradient Boosting Machine (GBM) and with logistic regression. Finally, to understand if each model has the same performance in the training and test set, the two AUCs were compared using the DeLong's test. Among 701 patients enrolled (mean age 67.2 ± 13.2 years, 69.5% male individuals), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9-24) days. Variables selected by the Lasso procedure were: age, oxygen saturation, PaO2/FiO2, creatinine clearance and elevated troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, lower creatinine clearance levels and higher prevalence of elevated troponin (all P 0.001). The best performance out of the samples was provided by Random Forest with an AUC of 0.78 (95% CI: 0.68-0.88) and a sensitivity of 0.88 (95% CI: 0.58-1.00). Moreover, Random Forest was the unique model that provided similar performance in sample and out of sample (DeLong test P = 0.78).In a large COVID-19 population, we showed that a customizable machine learning-based score derived from clinical variables is feasible and effective for the prediction of in-hospital mortality.

AIMS Implantable cardioverter defibrillator (ICD) is an effective treatment to reduce mortality in patients with symptomatic heart failure and left ventricular ejection fraction (LVEF) 35% or less. LVEF presents a low sensitivity for predicting arrhythmic events. Aim of this study was to identify predictors of sustained ventricular arrhythmias (SVAs), overall and according to the cause of heart failure. METHODS Single-center, retrospective, cohort study of 193 patients (51 nonischemic and 142 ischemic) with chronic heart failure and LVEF less than 35% who had received ICD for primary prevention of sudden cardiac death. We collected clinical data, echocardiographic parameters and SVAs detected by the ICD. RESULTS During a median follow-up of 1440 days, 32 (16.2%) patients had SVAs. SVAs incidence was similar in patients with nonischemic (15.6%) and ischemic cause of heart failure (16.9%). Hypertension, diabetes, chronic renal failure, atrial fibrillation, chronic obstructive pulmonary disease, New York Heart Association class at least III were predictors at univariate analysis of SVAs. A clinical score, assigning one point to each of these variables, was associated with a significantly increased risk of SVAs [odds ratio for each point increase = 1.92, 95% confidence interval 1.40-2.65, P