医学
全国健康与营养检查调查
危险系数
人口
弗雷明翰风险评分
队列
风险评估
比例危险模型
队列研究
广义估计方程
疾病
接收机工作特性
人口学
置信区间
环境卫生
内科学
统计
数学
计算机安全
社会学
计算机科学
作者
Britton Scheuermann,Alexandra R. Brown,Trenton D. Colburn,Hisham Hakeem,Chen Hoe Chow,Carl J. Ade
出处
期刊:JAMA network open
[American Medical Association]
日期:2024-10-11
卷期号:7 (10): e2438311-e2438311
标识
DOI:10.1001/jamanetworkopen.2024.38311
摘要
Importance The American Heart Association’s Predicting Risk of Cardiovascular Disease Events (PREVENT) equations were developed to extend and improve on previous cardiovascular disease (CVD) risk assessments for the purpose of treatment initiation and patient-clinician communication. Objective To assess prognostic capabilities, calibration, and discrimination of the PREVENT equations in a study sample representative of the noninstitutionalized, US general population. Design, Setting, and Participants This prognostic study used data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2010 data cycles. Participants included adults for whom 10-year follow-up data were available. Data curation and analyses took place from December 2023 through May 2024. Main Outcomes and Measures Primary measures were risk estimated by the PREVENT equations, as well as risk estimates from the previous Pooled Cohort Equations (PCEs). The primary outcome was composite CVD-related mortality at 10 years of follow-up. Additional analyses compared the PREVENT equations against the PCEs. Model discrimination was assessed with receiver-operator characteristic curves and Harrell C statistic from proportional hazard regression; model calibration was determined as the slope of predicted versus observed risk. Results The study cohort, accounting for NHANES complex survey design, consisted of 172.9 million participants (mean age, 45.0 years [95% CI, 44.6-45.4 years]; 52.1% women [95% CI, 51.5%-52.6%]). In analyses adjusted for the NHANES survey design, a 1% increase in PREVENT risk estimates was statistically significantly associated with increased CVD mortality risk (hazard ratio, 1.090; 95% CI, 1.087-1.094). PREVENT risk scores demonstrated excellent discrimination (C statistic, 0.890; 95% CI, 0.881-0.898) but moderate underfitting of the model (calibration slope, 1.13; 95% CI, 1.06-1.21). PREVENT risk models performed statistically significantly better than the PCEs, as assessed by the net reclassification index (0.093; 95% CI, 0.073-0.115). Conclusions and Relevance In this prognostic study of the PREVENT equations, PREVENT risk estimates demonstrated excellent discrimination and only modest discrepancies in calibration. These findings provided evidence supporting utilization of the PREVENT equations for application in the intended population as suggested by the American Heart Association.
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