MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge.
Br J Gen Pract. 2010 Aug;60(577):e327-34. doi: 10.3399/bjgp10X515098.
Population-based screening for cardiovascular disease (CVD) risk, incorporating blood tests, is proposed in several countries.
The aim of this study was to evaluate whether a simple approach to identifying individuals at high risk of CVD using routine data might be effective.
Prospective cohort study (EPIC-Norfolk).
Norfolk area, UK.
A total of 21 867 men and women aged 40-74 years, who were free from CVD and diabetes at baseline, participated in the study. The discrimination (the area under the receiver operating characteristic curve [aROC]), calibration, sensitivity/specificity, and positive/negative predictive value were evaluated for different risk thresholds of the Framingham risk equations and the Cambridge diabetes risk score (as an example of a simple risk score using routine data from electronic general practice records).
During 203 664 person-years of follow-up, 2213 participants developed a first CVD event (10.9 per 1000 person-years). The Cambridge diabetes risk score predicted CVD events reasonably well (aROC 0.72; 95% confidence interval [CI] = 0.71 to 0.73), while the Framingham risk score had the best predictive ability (aROC 0.77; 95% CI = 0.76 to 0.78). The Framingham risk score overestimated risk of developing CVD in this representative British population by 60%.
A risk score incorporating routinely available data from GP records performed reasonably well at predicting CVD events. This suggests that it might be more efficient to use routine data as the first stage in a stepwise population screening programme to identify people at high risk of developing CVD before more time- and resource-consuming tests are used.
一些国家提出了基于人群的心血管疾病（CVD）风险筛查，包括血液检查。
本研究旨在评估使用常规数据识别 CVD 高危个体的简单方法是否有效。
前瞻性队列研究（EPIC-Norfolk）。
英国诺福克地区。
共有 21867 名年龄在 40-74 岁、基线时无 CVD 和糖尿病的男性和女性参加了这项研究。评估了Framingham 风险方程和剑桥糖尿病风险评分（作为使用电子全科医生记录中的常规数据的简单风险评分的一个例子）的不同风险阈值的区分度（接收者操作特征曲线下的面积[aROC]）、校准、敏感性/特异性和阳性/阴性预测值。
在 203664 人年的随访期间，2213 名参与者发生了首次 CVD 事件（10.9/1000 人年）。剑桥糖尿病风险评分对 CVD 事件的预测效果相当好（aROC 0.72；95%置信区间[CI] = 0.71 至 0.73），而Framingham 风险评分具有最佳的预测能力（aROC 0.77；95%CI = 0.76 至 0.78）。Framingham 风险评分高估了该代表性英国人群发生 CVD 的风险，高估了 60%。
纳入来自全科医生记录的常规数据的风险评分在预测 CVD 事件方面表现相当不错。这表明，在使用更耗时、资源更密集的测试之前，使用常规数据作为逐步人群筛查计划的第一步，以识别发生 CVD 风险较高的人群，可能更有效率。
