Publications by authors named "Laura Alvarez Florez"
Background: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias.
Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed.
Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.
Maarten Z H Kolk Samuel Ruipérez-Campillo Brototo Deb Erik J Bekkers Cornelis P Allaart Laura Alvarez Florez
Europace
August 2023
Article Synopsis
- Left ventricular ejection fraction (LVEF) alone is not sufficient for predicting sudden cardiac death (SCD), prompting a study to enhance risk assessment for ICD implantation using machine learning (ML) with clinical data and ECG features.
- A multicentric analysis involved 1010 patients with serious heart conditions (average age 65, mostly male) who had LVEF ≤ 35% and received ICDs for SCD prevention, collecting both clinical info and ECG data before implantation.
- Machine learning models demonstrated high reliability in predicting non-arrhythmic mortality, achieving an impressive AUROC of 0.90 in a development cohort and 0.79 in an external validation cohort, indicating strong potential for personalized risk stratification.