Machine learning models incorporating inflammatory biomarkers and clinical parameters predicted early and late atrial fibrillation recurrence after catheter ablation with mean AUCs of 0.70 and 0.77.
Cohort (n=114)
Can machine learning models using inflammatory biomarkers predict early and late atrial fibrillation recurrence in overweight and obese patients after catheter ablation?
Machine learning models incorporating inflammatory biomarkers and clinical parameters show valuable performance in predicting early and late atrial fibrillation recurrence after catheter ablation in overweight and obese patients.
Effect estimate: mean AUC 0.70 for ERAF; mean AUC 0.77 for LRAF
BACKGROUND: Inflammation is associated with atrial fibrillation (AF) recurrence after catheter ablation. This study aimed to create machine learning models of inflammatory biomarkers and clinical parameters to predict early recurrence of atrial fibrillation (ERAF) and late recurrence of atrial fibrillation (LRAF) after catheter ablation. METHODS: The study included 114 patients with abnormal body weight, defined as body mass index (BMI) > 25 kg/m², and paroxysmal, persistent, or long-standing persistent AF. Blood samples were collected at baseline and 24 hours after ablation. Almost 120 clinical and laboratory parameters were selected to develop machine learning models of ERAF and LRAF. Shapley additive explanations (SHAP) were derived to explain the obtained predictions. The models were trained and tuned for hyperparameters by five-fold cross-validation and subsequently evaluated on independent test sets using stratified sampling. RESULTS: ERAF was observed in 20.3% of patients. LRAF was diagnosed in 26.3% of patients. The ERAF model with 5 variables - monocyte count, platelet-to-lymphocyte ratio (PLR), fibrinogen, and parameters measured before ablation, such as glomerular filtration rate (GFR) and left atrial volume - performed well in the studied cohort (mean AUC, 0.70 ± 0.06). The LRAF model with parameters such as neutrophil-to-lymphocyte ratio (NLR), soluble vascular cell adhesion molecule-1 (sVCAM-1), troponin, monocyte-to-HDL-C ratio (MHR), adiponectin, and ERAF also showed valuable performance (mean AUC, 0.77 ± 0.11). CONCLUSIONS: The machine learning model based on inflammatory biomarkers is a valuable tool for predicting ERAF. The combination of ERAF and inflammatory biomarkers significantly improves LRAF prediction.
Budzianowski et al. (Thu,) conducted a cohort in Atrial fibrillation (n=114). Inflammatory biomarkers and clinical parameters was evaluated on Early recurrence of atrial fibrillation (ERAF) and late recurrence of atrial fibrillation (LRAF) (mean AUC 0.70 for ERAF; mean AUC 0.77 for LRAF). Machine learning models incorporating inflammatory biomarkers and clinical parameters predicted early and late atrial fibrillation recurrence after catheter ablation with mean AUCs of 0.70 and 0.77.