Publications by authors named "R Sicari"
Background: Machine learning (ML) employs algorithms that learn from data, building models with the potential to predict events by aggregating a large number of variables and assessing their complex interactions. The aim of this study is to assess ML potential in identifying patients with ischemic heart disease (IHD) at high risk of cardiac death (CD).
Methods: 3987 (mean age 68 ± 11) hospitalized IHD patients were enrolled.
Article Synopsis
- A study examined how changes in pulmonary congestion, measured by ultrasound B-lines, can help predict outcomes in patients with acute heart failure, regardless if they had reduced (HFrEF) or preserved (HFpEF) ejection fraction.
- It involved 208 patients, showing that the number of B-lines at discharge was a key predictor of cardiovascular death or hospital readmissions within six months, especially when there were more than 15 B-lines.
- The findings suggest that managing pulmonary congestion effectively during hospital stays can lead to better health outcomes for heart failure patients.