超声极速成像脉搏波构建2型糖尿病早期颈动脉弹性受损列线图模型并验证OA

Objective:To explore the use of carotid artery elasticity parameters obtained via ultrafast pulse wave imaging technology to develop and validate a nomogram model for predicting the risk of early carotid artery elasticity impairment in patients with type 2 diabetes mellitus(T2DM).Methods:88 patients with T2DM were selected as the study subjects.All patients underwent carotid artery ultrafast ultrasound imaging to obtain parameters including carotid systolic diameter(Ds),diastolic diameter(Dd),stiffness index(β),pulse wave velocity(PWVβ),and arterial compliance(AC).Clinical indicators were also collected.Patients were divided into a normal elasticity group(β<8)and an impaired elasticity group(β≥8)based on the β value.Univariate and multivariate Logistic regression analyses were used to screen for independent risk factors of carotid artery elasticity impairment in T2DM patients.A nomogram prediction model was constructed based on these factors.Internal validation was performed using the Bootstrap method,and the model's calibration and discrimination were assessed using calibration curves and the receiver operating characteristic(ROC)curve.Results:Univariate and Logistic regression analysis revealed that diabetes duration,glycated hemoglobin(HbA1c),and PWVβ were independent risk factors,whereas AC was a protective factor for carotid elasticity impairment in patients with T2DM(P<0.05).The nomogram model constructed based on these factors had an area under the curve(AUC)of 0.843(95%CI:0.768～0.918)for predicting early carotid artery elasticity impairment.The calibration curve and the Hosmer-Lemeshow test(P>0.05)indicated good model calibration.Decision curve analysis(DCA)showed that the model had a clear clinical net benefit within the threshold probability range of 0.15～0.65.Conclusion:This study successfully developed a nomogram model incorporating four indicators:diabetes duration,HbA1c,SBP,and LDL-C.This model can intuitively and individually predict the risk of early carotid artery elasticity impairment in T2DM patients,demonstrating good predictive performance and calibration.It can serve as a visual tool for early clinical intervention.