Comparison between Duke, ESC-2015, ISCVID-2023 and ESC-2023 criteria for the diagnosis of TAVR-related infective endocarditis.

Background: Transcatheter aortic valve replacement-related infective endocarditis (TAVR-IE) is associated with a poor prognosis. TAVR-IE diagnosis is challenging, and benefits of the most recent classifications (ESC-2015, ISCVID-2023 and ESC-2023) have not been compared with the conventional Duke criteria on this population.

Objectives: The primary objective was to compare the diagnostic value of the Duke, ESC-2015, ISCVID-2023, and ESC-2023 criteria for the diagnosis of TAVR-IE.

Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study.

Aims: Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores.

Methods And Results: We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019.

Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study.

Background: Targeting ischemic strokes patients at risk of incident atrial fibrillation (AF) for prolonged cardiac monitoring and oral anticoagulation remains a challenge. Clinical risk scores have been developed to predict post-stroke AF with suboptimal performances. Machine learning (ML) models are developing in the field of AF prediction and may be used to discriminate post-stroke patients at risk of new onset AF.