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
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R587.1;R445.1

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安徽省健康卫生科研项目(AHWJ2023BAa20198)


Development and validation of a nomogram model for predicting early carotid artery elasticity impairment risk in type 2 diabetes mellitus based on ultrafast pulse wave imaging technology
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    摘要:

    目的:探究基于超声极速成像脉搏波技术获取的颈动脉弹性参数,构建并验证用于预测2型糖尿病(T2DM)患 者早期颈动脉弹性受损风险的列线图模型。方法:选取88例 T2DM 患者作为研究对象,所有患者均接受颈动脉超声极速成 像检查,获取颈动脉收缩期直径(Ds)、舒张期直径(Dd)、管壁僵硬度(β)、脉搏波传导速度(PWVβ)及顺应性(AC)等参数,同时 收集临床相关指标。依据β值将患者分为弹性正常组(β<8)和弹性受损组(β≥8)。采用单因素及多因素 Logistic回归分析 筛选 T2DM 患者颈动脉弹性受损的独立危险因素,并基于危险因素构建列线图预测模型;应用 Bootstrap法进行内部验证,并 采用校准曲线和受试者工作特征(ROC)曲线评估模型的校准度与区分度。结果:经单因素和 Logistic回归分析显示,糖尿病 病程、糖化血红蛋白(HbA1c)、PWVβ均是 T2DM 患者颈动脉弹性受损的独立危险因素,AC是 T2DM 患者颈动脉弹性受损 的保护因素(P<0.05)。基于此构建的列线图模型,其预测早期颈动脉弹性受损的曲线下面积(AUC)为 0.843(95%CI: 0.768~0.918)。校准曲线与 Hosmer-Lemeshow 检验(P>0.05)表明模型校准度良好,DCA 分析显示该模型在0.15~0.65 的阈值概率范围内具有明确的临床净获益。结论:本研究成功构建了包含糖尿病病程、HbA1c、SBP和 LDL-C四个指标的列 线图模型,该模型能直观、个体化地预测 T2DM 患者发生早期颈动脉弹性受损的风险,且具有良好的预测效能与校准度,为临 床早期干预提供可视化工具。

    Abstract:

    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.

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沈莉莉;徐婷婷;张静霞;赵茹;马芳.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[J].川北医学院学报,2026,41(5):575-579.

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  • 在线发布日期: 2026-05-29
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