Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

ABSTRACT: 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 models were tested and compared with current clinical risk scores on a cohort of 26,183 patients (mean age 70.13 (standard deviation 10.13); 44.8% female) with non-valvular AF. Inputted into the ML models were 23 demographic variables alongside comorbidities and current treatments. For one-year stroke prediction, ML achieved an area under the curve (AUC) of 0.653 (95% confidence interval 0.576-0.730), compared to the CHADS₂ and CHA₂DS₂-VASc scores performance of 0.587 (95% CI 0.559-0.615) and 0.535 (95% CI 0.521-

SUBMITTER: Joddrell M 

PROVIDER: S-EPMC11544011 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature