Predictive value of a model based on clinical features and plasma biomarkers for AF after pacemaker implantation surgery
10.3969/j.issn.1009-0126.2025.06.011
- VernacularTitle:基于临床特征及血浆标志物构建的模型对起搏器置入术后心房颤动的预测价值
- Author:
Mengchao JIN
1
;
Hui LI
1
;
Siliang PENG
1
;
Xinru GUO
1
Author Information
1. 215004 苏州大学附属第二医院心内科
- Publication Type:Journal Article
- Keywords:
pacemaker,artificial;
atrial natriuretic factor;
atrial fibrillation;
natriuretic peptide,brain
- From:
Chinese Journal of Geriatric Heart Brain and Vessel Diseases
2025;27(6):742-746
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a prediction model for atrial fibrillation(AF)after pacemaker implantation based on clinical features and plasma atrial natriuretic peptide(ANP)and brain na-triuretic peptide(BNP).Methods A retrospective analysis was conducted on 242 patients under-going pacemaker implantation in our department from January 2020 to October 2023.According to the occurrence of postoperative AF or not,they were divided into an AF group(61 cases)and a non-AF group(181 cases).The risk factors of AF after pacemaker implantation were analyzed,and a risk prediction model of AF after pacemaker implantation was constructed based on clinical features and plasma ANP and BNP levels.Results The AF group had significantly advanced age,larger proportions of hypertension and coronary heart disease,larger left ventricular diameter,and higher ANP,BNP,IL-6 and IL-8 levels,but lower proportion of using calcium antagonists when compared with the non-AF group(P＜0.01).Binary logistic regression analysis showed that hy-pertension,coronary heart disease,ANP,BNP and IL-6 were risk factors(P＜0.05,P＜0.01),and taking calcium antagonists was protective factor for AF after pacemaker implantation(P＜0.05).Hosmer Lemeshow fitting test indicated the model had a good fitness(x2=7.264,P=0.508).ROC curve analysis showed that the area under curve(AUC)value of the risk model for AF after pacemaker implantation in the training set was 0.826(95％CI:0.768-0.884),with an accuracy of 79.3％(192/242),and the AUC value of the model in the validation set was 0.835(95％CI:0.733-0.938).Conclusion Our AF prediction model based on clinical features and plasma ANP and BNP had good performance,and can provide auxiliary reference in predicting AF in patients undergoing pacemaker implantation.
