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    3种心血管风险评估模型识别颈动脉易损斑块的效能比较

    Comparison of the performance of three cardiovascular risk assessment models in identifying vulnerable carotid plaques

      摘要
    • 摘要:
      目的 评估3种心血管风险评估模型作为颈动脉易损斑块初筛工具的适用性。
      方法 采用横断面设计，选取2019—2023年在广西壮族自治区柳州市调查的986例当地居民作为研究对象。计算中国动脉粥样硬化性心血管疾病风险预测（prediction for atherosclerotic cardiovascular disease risk in China，China-PAR）模型、Framingham风险评分（Framingham risk score，FRS）模型以及世界卫生组织心血管疾病风险图表（WHO实验室模型）的10年心血管疾病风险值。以受试者工作特征曲线下面积（area under the receiver operating characteristic curve，AUC）、Hosmer-Lemeshow拟合优度检验、校准图、灵敏度、特异度和阳性预测值评估模型效能，并通过约登指数最大化探索各模型的最佳截断值。
      结果 3种模型对颈动脉易损斑块的区分能力良好（AUC：0.71~0.72），但校准度均较差（P＜0.001）。模型原始阈值下FRS模型具有高灵敏度（89.6%）、低特异度（32.2%）及低阳性预测值（27.6%），其余模型则表现为低灵敏度（WHO实验室模型：39.8%，China-PAR模型：53.4%）、高特异度（WHO实验室模型：84.2%，China-PAR模型：72.5%）及高阳性预测值（WHO实验室模型：42.1%，China-PAR模型：35.9%）。优化阈值后，4.5%阈值下的WHO实验室模型的灵敏度高于FRS模型（74.2% vs. 67.0%，P=0.003）和China-PAR模型（74.2% vs. 67.9%，P=0.018），特异度低于China-PAR模型（61.7% vs. 65.2%，P=0.006）和FRS模型（61.7% vs. 66.5%，P＜0.001），阳性预测值（35.9%）与China-PAR模型（36.1%）及FRS模型（36.6%）比较，差异无统计学意义（均P＞0.05）。
      结论 3种模型对颈动脉易损斑块均具有良好的区分能力，但直接套用原始开发阈值初筛时难以平衡漏诊与误诊。优化阈值后，WHO实验室模型综合表现最佳。提示广西地区基层医疗机构可初步采用WHO实验室模型优化后的风险阈值（4.5%）进行初筛，对高危者进一步行颈动脉超声检查。

       

      Abstract:
      Objective To evaluate the applicability of three cardiovascular risk assessment models as primary screening tools for vulnerable carotid plaques.
      Methods Using a cross-sectional design, we enrolled 986 local residents from Liuzhou, Guangxi surveyed between 2019 and 2023 as participants. The 10-year cardiovascular disease risk values were calculated using the assessment models of prediction for atherosclerotic cardiovascular disease risk in China (China-PAR), Framingham risk score (FRS), and World Health Organization cardiovascular disease risk charts (WHO laboratory-based model). The model performance was assessed using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, calibration plots, sensitivity, specificity and positive predictive value, and the optimal cut-offs for each model were explored by maximizing the Youden's index.
      Results The 3 models exhibited good discriminatory ability for vulnerable carotid plaques (AUC: 0.71–0.72), but showed poor calibration (P < 0.001). At the original cut-offs of each model, the FRS showed high sensitivity (89.6%), low specificity (32.2%) and low positive predictive value (27.6%). The other two models showed low sensitivity (WHO laboratory-based model: 39.8%; China-PAR model: 53.4%), high specificity (WHO laboratory-based model: 84.2%; China-PAR model: 72.5%), and high positive predictive value (WHO laboratory-based model: 42.1%; China-PAR model: 35.9%). After cut-off optimization, the WHO laboratory-based model at the optimal cut-off of 4.5% exhibited a higher sensitivity than the FRS model (74.2% vs. 67.0%, P=0.003) and the China-PAR model (74.2% vs. 67.9%, P=0.018), and exhibited a lower specificity than the China-PAR model (61.7% vs. 65.2%, P=0.006) and the FRS model (61.7% vs. 66.5%, P < 0.001). No statistically significant differences in positive predictive value were observed between the WHO laboratory-based model (35.9%) and either the China-PAR model (36.1%) or the FRS model (36.6%)(P > 0.05).
      Conclusion The 3 models exhibit satisfactory discriminatory performance in identifying vulnerable carotid plaques. However, when directly applying the original development cut-offs for initial screening, it is difficult to balance missed diagnoses and misdiagnoses. After cut-off optimization, the WHO laboratory-based model achieves the best overall performance. These conclusions suggest that primary medical institutions in Guangxi region can initially use the cut-off risk (4.5%) optimized by the WHO laboratory-based model for initial screening and conduct further carotid ultrasonography for high-risk individuals.

       

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