Construction of Prediction Model and Evaluation of Postoperative Recurrence of Paroxysmal Atrial Fibrillation Based on Electrocardiogram Quantitative Parameters

ZHANG Miaomiao; REN Ye; YAO Dan

doi:10.3639/j.issn.2095-3097.2024.09.033

Abstract：Objective To construct a model for predicting the postoperative recurrence of paroxysmal atrial fibrillation(PAF)based on quantitative electrocardiogram(ECG)parameters and evaluate its predictive efficacy.Methods A retrospec-tive study was conducted.A total of 93 PAF patients who underwent radiofrequency ablation in our hospital from July 2021 to September 2023 were selected.According to the recurrence status within 3 months after surgery,the patients were divided into a recurrence group(n=21)and a non-recurrence group(n=72).The clinical data and 12-lead ECG detection parameters[f-wave type,P-wave duration(PWD),QRS-wave duration,maximum P-wave duration(Pmax),P-wave dispersion(Pd),corrected QT interval(QTc),PR interval,T-wave peak-to-end interval(Tp-Te)]were compared between the two groups.Multivariate Logis-tic regression was used to analyze the factors influencing the postoperative recurrence of PAF,and the ROC curve was used to analyze the predictive value of quantitative ECG parameters for the postoperative recurrence of PAF patients.Results The disease course,the proportion of fine f waves,PWD,QRS-wave duration,Pmax,Pd,and QTc in the recurrence group were all higher than those in the non-recurrence group(P＜0.05).Multivariate Logistic regression analysis showed that the ECG f-wave type,disease course,PWD,QRS-wave duration,Pmax,Pd,and QTc were independent influencing factors for the postoperative recurrence of PAF patients(P＜0.05).The ROC analysis results showed that the areas under the curve of PWD,QRS-wave du-ration,Pmax,Pd,QTc,and the combined detection for predicting the postoperative recurrence of PAF were 0.829,0.811,0.765,0.834,0.728.and 0.927,respectively.Conclusion PWD,QRS-wave duration,P,Pd,and QTc are independent influenc-ing factors for the postoperative recurrence of PAF patients.The prediction model constructed based on these parameters has a high predictive value for the postoperative recurrence of PAF patients.