European journal of clinical investigation · 2025
Machine learning-based prediction of atrial fibrillation in patients with atrial high-rate episodes.
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Askarinejad Amir, Bucci Tommaso, Asgharzadeh Niloofar, Amirjam Zahra, Tartaglia Enrico, Rossi Michele, Chen Yang, Zheng Yalin, Lip Gregory Y H, Haghjoo Majid
Аннотация
BACKGROUND: Given the modest performance of available predictive models in estimating the risk of atrial fibrillation (AF) in patients with atrial high-rate episodes (AHREs) detected by cardiac implantable electronic devices (CIEDs), this study explores the potential use of machine learning (ML) algorithms in this context.
PURPOSE: To assess the ability of ML techniques in identifying patients with AHRE at high risk of AF.
METHODS: In this prospective study, we enrolled patients without a prior history of AF who experienced at least one AHRE episode detected by CIEDs. ML techniques were applied to predict the 1-year risk of developing new-onset AF based on the following variables: age, BMI, sex, smoking, hypertension, diabetes, coronary artery disease, chronic kidney disease, dyslipidaema, history of stroke or transient ischaemic attack, vascular heart disease, left atrial enlargement (LAE) and congestive heart failure.
RESULTS: Study population consists of 100 patients (48% male, mean age 66.0 ± 18.0 years), of whom 24 developed AF (24%) after 1-year follow-up. The CatBoost ML model achieved the highest AUC (.857, 95% CI .671-.999) when compared to other ML models and all clinical risk scores. The top four most influential predictors of AF in the CatBoost model were LAE, hypertension, diabetes and age.
CONCLUSIONS: ML techniques are robust in predicting AF in patients with AHREs. Further validation in larger, independent cohorts is warranted.
PURPOSE: To assess the ability of ML techniques in identifying patients with AHRE at high risk of AF.
METHODS: In this prospective study, we enrolled patients without a prior history of AF who experienced at least one AHRE episode detected by CIEDs. ML techniques were applied to predict the 1-year risk of developing new-onset AF based on the following variables: age, BMI, sex, smoking, hypertension, diabetes, coronary artery disease, chronic kidney disease, dyslipidaema, history of stroke or transient ischaemic attack, vascular heart disease, left atrial enlargement (LAE) and congestive heart failure.
RESULTS: Study population consists of 100 patients (48% male, mean age 66.0 ± 18.0 years), of whom 24 developed AF (24%) after 1-year follow-up. The CatBoost ML model achieved the highest AUC (.857, 95% CI .671-.999) when compared to other ML models and all clinical risk scores. The top four most influential predictors of AF in the CatBoost model were LAE, hypertension, diabetes and age.
CONCLUSIONS: ML techniques are robust in predicting AF in patients with AHREs. Further validation in larger, independent cohorts is warranted.