AIMC Topic: Atrial Fibrillation

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Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

European journal of clinical investigation Dec 11, 2024
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...
Humans Machine Learning Algorithms Area Under Curve Anticoagulants Registries Stroke Myocardial Infarction Thrombosis Administration, Oral Atrial Fibrillation Ischemic Attack, Transient Thromboembolism Middle Aged Risk Assessment ROC Curve Logistic Models Ischemic Stroke Female Risk Factors Aged Aged, 80 and over Male
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Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Trends in cardiovascular medicine Dec 7, 2024
Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing signif...
Risk Factors Risk Assessment Treatment Outcome Recovery of Function Predictive Value of Tests Stroke Atrial Fibrillation Humans Stroke Rehabilitation Artificial Intelligence Machine Learning Decision Support Techniques Precision Medicine
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Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation.

Computers in biology and medicine Dec 5, 2024
BACKGROUND: Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a prevalence of 1%-2%. AF is also associated with an increased risk of cardiovascular diseases (CVD), such as stroke, heart failure, and coronary artery dise...
Software Humans Algorithms Neural Networks, Computer Electrocardiography Signal Processing, Computer-Assisted Atrial Fibrillation
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Left Atrial Wall Thickness Measured by a Machine Learning Method Predicts AF Recurrence After Pulmonary Vein Isolation.

Journal of cardiovascular electrophysiology Nov 29, 2024
BACKGROUND: Left atrial (LA) remodeling plays a significant role in the progression of atrial fibrillation (AF). Although LA wall thickness (LAWT) has emerged as an indicator of structural remodeling, its impact on AF outcomes remains unclear. We aim...
Humans Registries Catheter Ablation Female Machine Learning Recurrence Risk Assessment Fibrosis Risk Factors Retrospective Studies Atrial Function, Left Time Factors Atrial Fibrillation Aged Pulmonary Veins Heart Atria Treatment Outcome Male Atrial Remodeling Heart Rate Action Potentials Middle Aged Predictive Value of Tests
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Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?

Computer methods and programs in biomedicine Nov 28, 2024
BACKGROUND: Machine learning-based analysis can accurately detect atrial fibrillation (AF) from photoplethysmograms (PPGs), however the computational requirements for analyzing raw PPG waveforms can be significant. The analysis of PPG-derived peak-to...
Signal Processing, Computer-Assisted Aged Humans Atrial Fibrillation Male Neural Networks, Computer Middle Aged Machine Learning Smartphone Algorithms Photoplethysmography Female
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Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning.

Cardiovascular diabetology Nov 26, 2024
BACKGROUND: The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourab...
Middle Aged Risk Assessment Blood Glucose Time Factors Hospital Mortality Predictive Value of Tests Cause of Death Atrial Fibrillation Glycemic Control Reproducibility of Results Risk Factors Aged Humans Aged, 80 and over Machine Learning Male Decision Support Techniques Prognosis Databases, Factual Retrospective Studies Biomarkers Critical Illness Intensive Care Units Female
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Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation.

JMIR medical informatics Nov 22, 2024
BACKGROUND: Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of mor...
Humans Adult Machine Learning Female Male Aged Middle Aged Aged, 80 and over Time Factors Young Adult Atrial Fibrillation
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Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial.

European journal of clinical investigation Nov 18, 2024
BACKGROUND: The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging. Our aim was to evaluate the performance of machine learning (ML) in identif...
Stroke Heart Failure Atrial Fibrillation Blood Urea Nitrogen Humans Machine Learning Female Aged Male Stroke Volume Middle Aged Support Vector Machine Ischemic Stroke Area Under Curve
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Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

Scientific reports Nov 7, 2024
Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML m...
Aged, 80 and over Male Middle Aged Risk Assessment Observational Studies as Topic Area Under Curve Registries Stroke Atrial Fibrillation Machine Learning Prognosis Female Risk Factors Aged Humans
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Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.

Circulation. Arrhythmia and electrophysiology Oct 24, 2024
BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertab...
Risk Factors Hospitalization Aged Aged, 80 and over Male Machine Learning Databases, Factual Prognosis Retrospective Studies Female Atrial Fibrillation Heart Rate Middle Aged Risk Assessment Time Factors Predictive Value of Tests Electrocardiography, Ambulatory Humans
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