Menzies Research Institute Tasmania, University of Tasmania, Hobart, Australia.
Am J Hypertens. 2012 Feb;25(2):190-4. doi: 10.1038/ajh.2011.192. Epub 2011 Oct 20.
With few exceptions, tools used to estimate cardiovascular disease (CVD) risk in those without prior events are based mainly on data from middle-aged subjects. Given the ever increasing number of older people, many with hypertension, a risk score relevant to this group is warranted. Our aim was to develop a cardiovascular risk equation suitable for risk prediction in elderly, hypertensive populations.
We utilized cardiovascular end point data from 4.1 years median follow-up in 5,426 hypertensive subjects without previous CVD from the Second Australian National Blood Pressure Study (ANBP2). Our risk model, based on Cox regression, was developed using 75% of subjects without evident CVD (n = 4,072), randomly selected and stratified by age and gender, and internally validated using the remaining 25%. The model was also externally validated against the Dubbo Study dataset.
The final model included sex, age, physical activity in the 2 weeks prior to entry into study, family history, use of anticoagulants, centrally acting antihypertensive agents or diabetes medication, and an interaction term for sex and diabetes medication. The C-statistic was 0.65 (0.62-0.67) for our predictive model on the model development dataset and 0.62 (0.57-0.67) on the internal validation dataset. The Dubbo Data C-statistic for CVD was 0.68 (95% CI 0.65-0.71).
All models performed similarly. Because of greater ease of implementation, we recommend that existing algorithms be extended into older age groups.
除了少数例外,用于估计无既往事件人群心血管疾病(CVD)风险的工具主要基于来自中年人群的数据。鉴于老年人的数量不断增加,其中许多人患有高血压,因此需要一个与该人群相关的风险评分。我们的目的是开发一种适用于老年高血压人群风险预测的心血管风险方程。
我们利用来自第二次澳大利亚国家血压研究(ANBP2)的中位随访 4.1 年的 5426 例无既往 CVD 的高血压患者的心血管终点数据。我们的风险模型基于 Cox 回归,使用 75%无明显 CVD 的患者(n=4072)进行开发,这些患者随机选择并按年龄和性别分层,其余 25%用于内部验证。该模型还针对 Dubbo 研究数据集进行了外部验证。
最终模型包括性别、年龄、研究入组前 2 周的体力活动、家族史、使用抗凝剂、中枢作用降压药或糖尿病药物,以及性别和糖尿病药物的交互项。该预测模型在模型开发数据集上的 C 统计量为 0.65(0.62-0.67),在内部验证数据集上为 0.62(0.57-0.67)。Dubbo 数据的 CVD C 统计量为 0.68(95%CI 0.65-0.71)。
所有模型的表现相似。由于实施起来更容易,我们建议将现有的算法扩展到老年人群。