Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women

作者：Paul M. Ridker, Julie E. Buring, Nader Rifai, Nancy R. Cook · 发表于：JAMA · 年份：2007 · DOI：10.1001/jama.297.6.611 · 被引用次数：1815 · 研究领域：Acute Myocardial Infarction Research、Biomarkers in Disease Mechanisms、Diabetes, Cardiovascular Risks, and Lipoproteins

CONTEXT: Despite improved understanding of atherothrombosis, cardiovascular prediction algorithms for women have largely relied on traditional risk factors. OBJECTIVE: 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). MAIN OUTCOME MEASURE: 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. RESULTS: 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...