Publications by authors named "J L Selder"

Unlabelled: Atrial fibrillation (AF) is a prevalent and clinically significant cardiac arrhythmia, with a growing incidence. The primary objectives in AF management are symptom relief, stroke risk reduction, and prevention of tachycardia-induced cardiomyopathy. Two key strategies for rhythm control include antiarrhythmic drug therapy and pulmonary vein isolation (PVI), with PVI being recommended for selected patients.

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Aims: This study aimed to validate the machine learning-based Global Registry of Acute Coronary Events (GRACE) 3.0 score and PRAISE (Prediction of Adverse Events following an Acute Coronary Syndrome) in patients with acute coronary syndrome (ACS) treated with percutaneous coronary intervention (PCI) for predicting mortality.

Methods And Results: Data of consecutive patients with ACS treated with PCI in a tertiary centre in the Netherlands between 2014 and 2021 were used for external validation.

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Background: Left atrial volume index (LAVI) serves as a crucial marker for assessing left atrial (LA) remodeling, particularly in patients with mitral valve regurgitation (MR). Recent guidelines recommend a LAVI exceeding 60 mL/m as Class IIa recommendation for mitral valve repair surgery in asymptomatic MR patients with preserved left ventricular function. Traditionally, echocardiography is the standard for assessing LAVI in MR patients.

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Article Synopsis
  • The study aimed to assess the effectiveness of machine learning (ML) in predicting 5-year all-cause mortality in patients with chronic coronary syndrome (CCS) using clinical and transthoracic echocardiography (TTE) data.
  • Researchers gathered data from 1253 patients who underwent TTE at Amsterdam UMC between 2015 and 2017, training an eXtreme Gradient Boosting (XGBoost) model that outperformed traditional risk scores in predicting mortality.
  • The findings indicate that the ML model is more accurate, particularly identifying risks such as left ventricular dysfunction, showcasing its potential to better stratify high-risk CCS patients compared to existing methods.
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