Publications by authors named "Jean-Baptiste Brette"
Article Synopsis
- The study focused on the use of a machine learning model using initial transthoracic echocardiography (TTE) to predict in-hospital major adverse events (MAEs) in patients admitted to intensive cardiac care units (ICCU).
- A total of 1,499 patients were evaluated, and the model showed significant accuracy, highlighting five key TTE parameters that contributed to its predictions.
- The machine learning model outperformed traditional scoring methods, indicating it could serve as a better tool for risk stratification in heart patients.
Background: The prevalence and short-term cardiovascular consequences of recent cannabis use in patients admitted to an intensive cardiac care unit for acute coronary syndrome is not well established.
Aims: To assess the prevalence of recent cannabis use detected by prospective systematic screening, and its prognostic value in predicting the occurrence of in-hospital major adverse events in consecutive patients with acute coronary syndrome.
Methods: From 07 to 22 April 2021, all consecutive patients admitted to an intensive cardiac care unit in 39 centres throughout France were studied prospectively.
Background: Aortic stenosis (AS) and transthyretin (ATTR) cardiac amyloidosis (CA) share the same clinical profiles and cardiac phenotype. Amyloid deposits have been frequently reported in aortic valves of patients with severe AS referred for surgical aortic valve replacement (SAVR). The aim of this study was to determine the clinical and myocardial status of patients with aortic valve amyloidosis after aortic valve surgery.
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