Ridker Paul M, Buring Julie E, Rifai Nader, Cook Nancy R
Donald W. Reynolds Center for Cardiovascular Research and the Center for Cardiovascular Disease Prevention, Brigham and Women's Hospital, Boston, MA 02215, USA.
JAMA. 2007 Feb 14;297(6):611-9. doi: 10.1001/jama.297.6.611.
Despite improved understanding of atherothrombosis, cardiovascular prediction algorithms for women have largely relied on traditional risk factors.
To develop and validate cardiovascular risk algorithms for women based on a large panel of traditional and novel risk factors.
DESIGN, SETTING, AND PARTICIPANTS: Thirty-five factors were assessed among 24 558 initially healthy US women 45 years or older who were followed up for a median of 10.2 years (through March 2004) for incident cardiovascular events (an adjudicated composite of myocardial infarction, ischemic stroke, coronary revascularization, and cardiovascular death). We used data among a random two thirds (derivation cohort, n = 16 400) to develop new risk algorithms that were then tested to compare observed and predicted outcomes in the remaining one third of women (validation cohort, n = 8158).
Minimization of the Bayes Information Criterion was used in the derivation cohort to develop the best-fitting parsimonious prediction models. In the validation cohort, we compared predicted vs actual 10-year cardiovascular event rates when the new algorithms were compared with models based on covariates included in the Adult Treatment Panel III risk score.
In the derivation cohort, a best-fitting model (model A) and a clinically simplified model (model B, the Reynolds Risk Score) had lower Bayes Information Criterion scores than models based on covariates used in Adult Treatment Panel III. In the validation cohort, all measures of fit, discrimination, and calibration were improved when either model A or B was used. For example, among participants without diabetes with estimated 10-year risks according to the Adult Treatment Panel III of 5% to less than 10% (n = 603) or 10% to less than 20% (n = 156), model A reclassified 379 (50%) into higher- or lower-risk categories that in each instance more accurately matched actual event rates. Similar effects were achieved for clinically simplified model B limited to age, systolic blood pressure, hemoglobin A(1c) if diabetic, smoking, total and high-density lipoprotein cholesterol, high-sensitivity C-reactive protein, and parental history of myocardial infarction before age 60 years. Neither new algorithm provided substantive information about women at very low risk based on the published Adult Treatment Panel III score.
We developed, validated, and demonstrated highly improved accuracy of 2 clinical algorithms for global cardiovascular risk prediction that reclassified 40% to 50% of women at intermediate risk into higher- or lower-risk categories.
尽管对动脉粥样硬化血栓形成的认识有所提高，但针对女性的心血管疾病预测算法在很大程度上仍依赖于传统风险因素。
基于大量传统和新型风险因素开发并验证针对女性的心血管疾病风险算法。
设计、地点和参与者：对24558名年龄在45岁及以上、最初健康的美国女性进行了35项因素的评估，这些女性接受了中位时间为10.2年（截至2004年3月）的随访，以观察心血管事件（心肌梗死、缺血性中风、冠状动脉血运重建和心血管死亡的综合判定结果）。我们使用随机抽取的三分之二的数据（推导队列，n = 16400）来开发新的风险算法，然后在其余三分之一的女性（验证队列，n = 8158）中进行测试，以比较观察到的和预测的结果。
在推导队列中，使用贝叶斯信息准则最小化来开发最佳拟合的简约预测模型。在验证队列中，当将新算法与基于成人治疗小组III风险评分中包含的协变量的模型进行比较时，我们比较了预测的与实际的10年心血管事件发生率。
在推导队列中，一个最佳拟合模型（模型A）和一个临床简化模型（模型B，雷诺兹风险评分）的贝叶斯信息准则得分低于基于成人治疗小组III中使用的协变量的模型。在验证队列中，使用模型A或B时，所有拟合、区分和校准指标均得到改善。例如，在根据成人治疗小组III估计10年风险为5%至小于10%（n = 603）或10%至小于20%（n = 156）的无糖尿病参与者中，模型A将379名（50%）重新分类为更高或更低风险类别，在每种情况下都更准确地匹配了实际事件发生率。对于仅限于年龄、收缩压、糖尿病患者的糖化血红蛋白A1c、吸烟、总胆固醇和高密度脂蛋白胆固醇、高敏C反应蛋白以及60岁前心肌梗死家族史的临床简化模型B，也取得了类似效果。基于已发表的成人治疗小组III评分，两种新算法都没有为极低风险的女性提供实质性信息。
我们开发、验证并证明了两种用于全球心血管疾病风险预测的临床算法具有显著提高的准确性，这些算法将40%至50%的中度风险女性重新分类为更高或更低风险类别。
