Publications by authors named "J Dillinger"
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: The appropriate duration of treatment with beta-blocker drugs after a myocardial infarction is unknown. Data are needed on the safety and efficacy of the interruption of long-term beta-blocker treatment to reduce side effects and improve quality of life in patients with a history of uncomplicated myocardial infarction.
Methods: In a multicenter, open label, randomized, noninferiority trial conducted at 49 sites in France, we randomly assigned patients with a history of myocardial infarction, in a 1:1 ratio, to interruption or continuation of beta-blocker treatment.
Background: Intensive cardiac care units (ICCUs) were created to manage ventricular arrhythmias after acute coronary syndromes, but have diversified to include a more heterogeneous population, the characteristics of which are not well depicted by conventional methods.
Aims: To identify ICCU patient subgroups by phenotypic unsupervised clustering integrating clinical, biological, and echocardiographic data to reveal pathophysiological differences.
Methods: During 7-22 April 2021, we recruited all consecutive patients admitted to ICCUs in 39 centers.