影像学特征联合血糖水平和脂质代谢对主动脉瓣钙化的预测效能OA

Objective To explore the predictive efficacy of imaging characteristics combined with blood glucose level and lipid metabolism in aortic valve calcification(AVC),and to provide a reference for the early identification of AVC.Methods A total of 415 patients who tested negative for AVC and underwent their first coronary computed tomography angiography examination at the Affiliated Hospital of Xuzhou Medical University from January 2021 to January 2024 were selected as the subjects of this study.Based on the imaging outcomes at the end of the follow-up period,the patients were divided into the AVC group(n=124)and the non-AVC group(n=291).The demographic data,past medical history,imaging features and laboratory indicators(blood glucose,lipid metabolism,etc.)of the patients were collected.Univariate analysis was used to compare the differences between the two groups.Multivariate Logistic regression was used to screen the independent influencing factors of AVC.The predictive efficacy of single index and combined indexes was evaluated by ROC curve,calibration curve and decision curve analysis.Results Univariate analysis showed that the age,history of coronary heart disease,levels of HbA1c,coronary artery calcification(CAC)score,incidence of multi-site vascular calcification and coronary branch lesions in the AVC group were significantly higher than those in the non-AVC group,while the level of HDL-C in the AVC group was significantly lower than that in the non-AVC group(P<0.05).Multivariate logistic regression showed that age,HbA1c,CAC,ascending aortic calcification,aortic arch calcification and thoracic aortic calcification were independent risk factors for AVC,and HDL-C was an independent protective factor(P<0.05).ROC curve analysis showed that the AUC of the combined diagnosis of blood glucose level(HbA1c),lipid metabolism(HDL-C)and imaging characteristics(CAC+ascending aortic calcification+aortic arch calcification+thoracic aortic calcification)was 0.817,which was significantly higher than that of single indexes(P<0.05).The model had good goodness-of-fit(Hosmer-Lemeshow test,P=0.417)and significant clinical net benefit at high-risk thresholds(0.3-0.8).Conclusion The diagnostic model combining imaging features,blood glucose level and lipid metabolism has excellent diagnostic efficacy for AVC,and can be used as an effective tool for early clinical identification of AVC.