A comparative assessment of non-laboratory-based versus commonly used laboratory-based cardiovascular disease risk scores in the NHANES III population.
A comparative assessment of non-laboratory-based versus commonly used laboratory-based cardiovascular disease risk scores in the NHANES III population.
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DOI：
10.1371/journal.pone.0020416
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Gaziano TA
中科院分区：
文献类型：
--
作者：
Pandya A;Weinstein MC;Gaziano TA
国家和国际主要CVD风险筛查指南侧重于使用总CVD风险评分。最近，我们开发了一种非基于实验室的CVD风险评分（输入：年龄，性别，吸烟，糖尿病，收缩压，高血压治疗，体重指数），与基于实验室的评分（输入包括胆固醇值）相比，它可以更快地评估风险，成本更低。我们的目的是在外部验证人群中评估非基于实验室的风险评分与四种常用的基于实验室的评分（FractionalCVD [2008，1991版本]和低风险和高风险环境的系统性冠状动脉风险评价[SCORE]）的互换性。分析基于第三次全国健康和营养检查调查（NHANES III）中年龄在25-74岁之间，没有CVD或癌症病史的成年人（n = 5，999）的个人水平，评分特定的风险排名。 风险表征一致性是基于二分风险表征（10年风险阈值>10-20%）和斯皮尔曼等级相关性的重叠。使用受试者操作特征曲线分析（10年CVD死亡结局）评估风险区分。男性和女性的风险表征一致性范围分别为91.9-95.7%和94.2-95.1%，斯皮尔曼相关性范围分别为0.957-0.980和0.946-0.970。在男性中，非基于实验室的、Fractionary（2008，1991）和SCORE（高，低）函数的c统计量分别为0.782、0.776、0.781、0.785和0.785，相对于非基于实验室的评分的差异p值分别为0.44、0.89、0.68和0.65。在女性中，相应的c统计量分别为0.809、0.834、0.821、0.792和0.792，相应的p值分别为0.04、0.34、0.11和0.09。每个分数都很好地区分了CVD死亡的风险，并且在非实验室为基础的风险评分和实验室为基础的风险评分之间的风险表征具有高度一致性，这表明非实验室为基础的评分可以在资源有限的环境中成为Fraction或SCORE功能的有用代理。未来的外部验证研究可以评估性别特异性风险歧视结果是否适用于其他人群。
National and international primary CVD risk screening guidelines focus on using total CVD risk scores. Recently, we developed a non-laboratory-based CVD risk score (inputs: age, sex, smoking, diabetes, systolic blood pressure, treatment of hypertension, body-mass index), which can assess risk faster and at lower costs compared to laboratory-based scores (inputs include cholesterol values). We aimed to assess the exchangeability of the non-laboratory-based risk score to four commonly used laboratory-based scores (Framingham CVD [2008, 1991 versions], and Systematic COronary Risk Evaluation [SCORE] for low and high risk settings) in an external validation population. Analyses were based on individual-level, score-specific rankings of risk for adults in the Third National Health and Nutrition Examination Survey (NHANES III) aged 25–74 years, without history of CVD or cancer (n = 5,999). Risk characterization agreement was based on overlap in dichotomous risk characterization (thresholds of 10-year risk >10–20%) and Spearman rank correlation. Risk discrimination was assessed using receiver operator characteristic curve analysis (10-year CVD death outcome). Risk characterization agreement ranged from 91.9–95.7% and 94.2–95.1% with Spearman correlation ranges of 0.957–0.980 and 0.946–0.970 for men and women, respectively. In men, c-statistics for the non-laboratory-based, Framingham (2008, 1991), and SCORE (high, low) functions were 0.782, 0.776, 0.781, 0.785, and 0.785, with p-values for differences relative to the non-laboratory-based score of 0.44, 0.89, 0.68 and 0.65, respectively. In women, the corresponding c-statistics were 0.809, 0.834, 0.821, 0.792, and 0.792, with corresponding p-values of 0.04, 0.34, 0.11 and 0.09, respectively. Every score discriminated risk of CVD death well, and there was high agreement in risk characterization between non-laboratory-based and laboratory-based risk scores, which suggests that the non-laboratory-based score can be a useful proxy for Framingham or SCORE functions in resource-limited settings. Future external validation studies can assess whether the sex-specific risk discrimination results hold in other populations.
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