目的 探索血管健康指标(包括颈-股动脉脉搏波传导速度、颈-桡动脉脉搏波传导速度、心踝血管指数和踝臂指数)与冠状动脉粥样硬化性心脏病(简称冠心病)和脑梗塞的关系,初步评估北京血管健康分级的预测价值。方法 研究纳入2010—2017年首钢医院血管医学科至少有2次住院记录的受试者,排除基线时血管指标数据缺失且患有冠心病或脑梗塞的患者。建立两个队列,队列1(冠心病)入组467例受试者[平均年龄(63.4±12.3)岁,女性42.2%],队列2(脑梗塞)入组658例受试者[平均年龄(64.3±12.2)岁,女性48.7%],分别应用Cox比例风险回归建立冠心病或脑梗塞的预测模型。结果 队列1和队列2的中位随访时间分别为1.9年和2.1年,随访期间,队列1中有164例首发冠心病事件发生,队列2中有117例首发脑梗塞事件发生。将4种血管健康指标同时作为连续变量进行多变量调整分析,队列1中,4种指标均有统计学意义(P均<0.05);队列2中,仅心踝血管指数有统计学意义(P<0.05)。未调整模型中,北京血管健康分级对于冠心病和脑梗塞的预测价值均有统计学意义(P均<0.05),而在多变量调整模型中,北京血管健康分级仅对冠心病具有预测价值(P<0.05)。结论 不同的血管健康指标对于冠心病和脑梗塞的预测价值不同,其中心踝血管指数可能是一种较为稳定的指标。北京血管健康分级对于冠心病具有预测价值,而对于脑梗塞的预测价值还需进一步研究。

Objective: To explore the predictive value of carotid femoral artery pulse wave velocity (CF-PWV), carotid radial artery pulse wave velocity (CR-PWV), cardio-ankle vascular index (CAVI), and ankle brachial index (ABI) on coronary heart disease (CHD) and cerebral infarction (CI), and the preliminary validation of Beijing vascular health stratification (BVHS).Methods: Subjects with at least 2 in-patient records were included into the study between 2010 and 2017 from Vascular Medicine Center of Peking University Shougang Hospital. Subjects with CHD or CI, and without data of vascular function at baseline were excluded. Eventually, 467 subjects free of CHD [cohort 1, mean age: (63.4±12.3) years, female 42.2%] and 658 subjects free of CI [cohort 2, mean age: (64.3±12.2) years, female 48.7%] at baseline were included. The first in-patient records were as the baseline data, the second in-patient records were as a following-up data. Cox proportional hazard regression was used to establish the predictive models of CHD or CI derived from BVHS by multivariable-adjusted analysis.Results: The median follow-up time of cohort 1 and cohort 2 was 1.9 years and 2.1 years, respectively. During the follow-up, 164 first CHD events occurred in cohort 1 and 117 first CI events occurred in cohort 2. Four indicators were assessed as continuous variables simultaneously by multivariable-adjusted analysis. In cohort 1, CF-PWV, CR-PWV, ABI, and CAVI reached statistical significance in the multivariable-adjusted models (P<0.05). In cohort 2, only CAVI (P<0.05) was of statistical significance. In addition, the higher CF-PWV became a protector of CHD or CI (P<0.05). The prediction value of BVHS reached the statistical significance for CHD and CI in the unadjusted models (all P<0.05), however, BVHS could only predict the incidence of CHD (P<0.05), but not the incidence of CI (P>0.05) in the multivariable-adjusted models. CF-PWV, CR-PWV, ABI, and CAVI were associated factors of CHD independent of each other (P<0.05), only CAVI (P<0.05) was the risk factor of CI independent of the other three.Conclusion: The different vascular indicators might have different effect on CHD or CI. CAVI might be a stable predictor of both CHD and CI. Higher baseline CF-PWV was not necessarily a risk factor of CHD or CI because of proper vascular health management. BVHS was a potential factor for the prediction of CHD, and further research is needed to explore the prediction value for CI.