Development and validation of the HWARNER models for predicting atrial tachyarrhythmias recurrence after AF ablation: a retrospective cohort study with emphasis on HbA1c
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Predictive models for recurrence following atrial fibrillation (AF) radiofrequency catheter ablation (RFCA) have potential value in minimizing the risk of recurrent atrial tachyarrhythmias (ATAs). This study aims to develop and validate models to optimize patient selection and post-ablation management. METHODS: A retrospective analysis of consecutive AF patients undergoing RFCA from January 2021 to August 2023 identified risk factors for ATAs recurrence through regression analysis, leading to the development of two predictive models, HWARNER 1 and HWARNER 2, which were preliminarily externally validated. RESULTS: Seven risk factors were identified: Weight > 77.5 kg, HbA1c > 5.75%, RA (dimension / > 46.5 mm), NT-proBNP > 650.18 pg/ml, Early AF recurrence, Redo procedures, and Height > 171.5 cm. Based on these factors, the HWARNER models showed high predictive accuracy with AUCs of 0.775 and 0.773, outperforming established models. Kaplan–Meier analysis revealed significant differences in ATAs recurrence between low- and high-risk groups (HWARNER 1: 93.0% vs. 59.1%, P < 0.001; HWARNER 2: 94.5% vs. 67.9%, P < 0.001) with an median follow-up of one year. In subgroup analysis, HbA1c showed stronger predictive value in patients with BMI > 26.7 (OR = 7.191, 95% CI: 1.375–37.594, P = 0.019). In paroxysmal AF (PAF) patients, the pulmonary vein isolation plus low voltage zones-stepwise (PVI + LVZs-Stepwise) protocol showed no benefit over PVI alone. CONCLUSION: The HWARNER models have the potential to predict ATAs recurrence after RFCA, providing a valuable tool for patient selection and management. HbA1c is an important independent risk factor.
Source: BMC Cardiovascular Disorders
Authors: Min Xu, Ruikun Jia, Juan Chan, Hanlin Wang, Qidong Gao, Sitai Liu, Dayong Zhang, Yunfeng Di, Kaijun Cui
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
Award: G2214639961
OpenAlex ID: W4416724016
