Nomogram for severe low-voltage areas prediction in older patients with paroxysmal atrial fibrillation: a retrospective cohort study

Low-voltage areas (LVA) are recognized as substrate for atrial fibrillation (AF) recurrence following pulmonary vein isolation. This study aimed to develop a nomogram for predicting the probability of severe LVA (≥ 10% ) in older patients with paroxysmal AF. The older patients aged 65–80 years with paroxysmal AF were retrospectively analyzed from January 2018 - June 2021 and April 2022 - March 2024. Patients were divided into a training cohort and an external validation cohort based on enrollment period. By integrating the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analysis, crucial variables were determined for establishment of the nomogram. Model performance was assessed by bootstrap validation, receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Meanwhile, the external validation cohort was conducted to evaluate the model’s generalizability. This study enrolled 873 patients, and 60 patients suffered from severe LVA. Sex, age and left atrial diameter emerged as independent predictors and were incorporated into the nomogram. The mean optimism-corrected C-index of Bootstrap validation was 0.765 (95% CI: 0.6782–0.8519). The area under the ROC curve was found to be 0.782 (95% CI: 0.718–0.845) in the training cohort (694) and 0.774 (95% CI: 0.623–0.924) in the validation cohort (179). Additionally, the calibration curves displayed a favorable agreement between predicted and observed outcomes, and DCA indicated clinical benefit when the threshold probability ranged from 11% to 83% (training) and from 15% to 72% (validation). This is the first study to establish and externally validate a nomogram for the prediction of severe LVA in older patients with paroxysmal AF. This model may facilitate accurate risk stratification, guide individualized procedural planning, and inform optimal strategies for substrate modification.