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血管老化指数可独立预测城市老年人群中心血管事件和全因死亡率。

A Vascular Aging Index as Independent Predictor of Cardiovascular Events and Total Mortality in an Elderly Urban Population.

机构信息

1 Department of Clinical Sciences, Lund University, Malmö, Sweden.

2 Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden.

出版信息

Angiology. 2019 Nov;70(10):929-937. doi: 10.1177/0003319719857270. Epub 2019 Jun 24.

Abstract

The morphology and function of the arteries can be directly measured using different established methods. This prospective cohort study aimed to translate 2 of these, aortic pulse wave velocity (aPWV) and carotid intima-media thickness (cIMT), into a combined Vascular Aging Index (VAI) and then evaluate the predictive power of aPWV, cIMT, and VAI. Patients (n = 2718) were included from the cardiovascular arm of the Malmö Diet and Cancer Study (median age 71.9 years, 62.2% females). Total follow-up time was 16 448 person-years and a composite cardiovascular disease (CVD) end point was used. Cox regressions yielded adjusted hazard ratios (95% confidence interval) per 1 standard deviation increment of log aPWV, log cIMT, and log VAI of 1.25 (1.08-1.45, = .003), 1.27 (1.13-1.44, < .001), and 1.45 (1.26-1.68, < .001), respectively. The C-statistics increased from 0.714 to 0.734 when adding aPWV and cIMT to a model of conventional risk factors. Net Reclassification Index also showed a significant ( < .001) improvement for the classification of event-free patients and no change for patients with events. A VAI based on aPWV and cIMT had a good predictive performance. Used together, aPWV and cIMT incrementally and significantly improve the prediction of CVD events by correctly down-adjusting the predicted risk for noncases.

摘要

可以使用不同的既定方法直接测量动脉的形态和功能。这项前瞻性队列研究旨在将其中的两种方法(主动脉脉搏波速度(aPWV)和颈动脉内膜中层厚度(cIMT))转化为综合血管老化指数(VAI),然后评估 aPWV、cIMT 和 VAI 的预测能力。从马尔默饮食与癌症研究(Malmö Diet and Cancer Study)的心血管分支中纳入了 2718 名患者(中位年龄 71.9 岁,62.2%为女性)。总的随访时间为 16448 人年,并使用了复合心血管疾病(CVD)终点。Cox 回归得出,log aPWV、log cIMT 和 log VAI 每增加 1 个标准差,复合 CVD 终点的调整后风险比(95%置信区间)分别为 1.25(1.08-1.45, =.003)、1.27(1.13-1.44, <.001)和 1.45(1.26-1.68, <.001)。当将 aPWV 和 cIMT 添加到常规危险因素模型中时,C 统计量从 0.714 增加到 0.734。净重新分类指数也显示出显著( <.001)改善了无事件患者的分类,而对有事件的患者无变化。基于 aPWV 和 cIMT 的 VAI 具有良好的预测性能。联合使用 aPWV 和 cIMT 可通过正确下调非病例的预测风险,逐步且显著地提高 CVD 事件的预测能力。

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