An echocardiographic model for predicting the recurrence of paroxysmal atrial fibrillation after circumferential pulmonary vein ablation.

ABSTRACT:

Background

Atrial fibrillation (AF) is a highly prevalent arrhythmia, with substantial associated morbidity and mortality. Circumferential pulmonary vein ablation (CPVA) is an effective rhythm control strategy, however, recurrence is an important factor influencing treatment decisions.

Hypothesis

To develop a predictive model based on left atrial (LA) structure and function, and evaluate its efficiency in predicting the recurrence of AF after CPVA.

Methods

Patients with paroxysmal AF who underwent CPVA were enrolled in this study and randomly divided into a development set and a validation set. The clinical and echocardiographic data of each patient were collected. In the development set, a least absolute shrinkage and selection operator (LASSO) regression was used to establish a LA ultrasound feature. By combining that LA ultrasound feature with independent clinical risk factors, we established an echocardiographic model using multivariate logistic regression and plotted the corresponding nomogram.

Results

The LA ultrasound feature established by LASSO regression included nine echocardiographic indicators related to LA structure and function. It also exhibited good predictive ability in both the development set and the validation set (AUC:0.944, 95%CI: 0.910-0.978; AUC:0.878, 95%CI: 0.816-0.942). Logistic regression analysis indicated that LA ultrasound feature and AF duration were independent predictors for AF recurrence. The combined model including LA ultrasound feature and AF duration also showed good discriminability in both the development set (AUC: 0.950, 95% CI:0.914-0.985) and the validation set (AUC: 0.890, 95% CI: 0.831-0.949). The calibration curve showed good agreement between the predicted value and observed value.

Conclusions

Our model that is based on LA structure and function measured by echocardiography is a useful non-invasive preoperative tool, which exhibits good accuracy in predicting the recurrence of AF after CPVA.

SUBMITTER: Miao Y 

PROVIDER: S-EPMC8571557 | biostudies-literature |

REPOSITORIES: biostudies-literature