RESUMO
The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the "Ospedali Riuniti Padova Sud" COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78-0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.
RESUMO
A 52-year-old male with no history of familiar sudden death arrived at our Emergency Department after syncope with loss of consciousness occurred during high fever. The thoracic high-resolution computed tomography demonstrated bilateral multiple ground-glass opacities. The nose-pharyngeal swab resulted positive for SARS-CoV-2. The 12-lead ECG presented a "coved-type" aspect in leads V1 and V2 at the fourth intercostal space and a first degree atrio-ventricular block. As soon as the temperature went down, the 12-lead ECG resumed a normal aspect, maintaining a long PR interval.
