Objective:To explore the risk factors in patients with proximal aortic dilatation after undergoing aortic valve replacement(AVR),and to construct a model that predicts the risk of postoperative proximal aortic dilatation in these patients.Methods:A retrospective analysis was conducted on the clinical data of patients undergoing aortic valve replacement surgery at Beijing Anzhen Hospital from January 2018 to October 2022.Using postoperative proximal aortic dilatation as the endpoint,70％of these patients were randomly divided into a training set,while the remaining 30％were allocated to a validation set.Apply binary multivariate Logistic regression to explore risk factors in the training set,construct a model,draw a column chart,and validate the model's discrimination and calibration in the validation set.Results:Of the 979 patients included in this study,120(12.7％)had endpoint events(postoperative recurrent aortic lesions).Multivariate logistic regression analysis showed that male,hypertension,aortic sinus index(DAS/BSA),ascending aortic index(DAA/BSA),and LVEED were risk factors for recurrent aortic disease after aortic valve replacement surgery.The model constructed based on the above five predictive factors has good discrimination in the training set,with a consistency index(C-index)of 0.718(95％CI:0.665-0.771),and high accuracy.The C-index of the model in the validation set is 0.727(95％CI:0.640-0.816).In patients with a predicted risk of less than 50％of the primary endpoint events,the calibration curve indicates that the predicted risk is basically consistent with the observed risk.Conclusions:Establishing a risk prediction model for recurrent aortic disease after AVR surgery can effectively predict the incidence of such patients and help identify high-risk aortic valve replacement patients in this population for optimizing surgical strategies.
关键词
主动脉瓣置换术/再次手术/主动脉综合征/预测模型
Key words
Aortic valve replacement/Reoperation/Aortic syndrome/Prediction model
维普
万方数据