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Development and Validation of the American Heart Association’s PREVENT Equations

医学 四分位间距 置信区间 十分位 内科学 心力衰竭 糖尿病 弗雷明翰风险评分 血压 心脏病学 疾病 统计 内分泌学 数学
作者
Sadiya S. Khan,Kunihiro Matsushita,Yingying Sang,Shoshana H. Ballew,Morgan E. Grams,Aditya Surapaneni,Michael J. Blaha,April P. Carson,Alex R. Chang,Elizabeth L. Ciemins,Alan S. Go,Orlando M. Gutiérrez,Shih‐Jen Hwang,Simerjot K Jassal,Csaba P. Kövesdy,Donald M. Lloyd‐Jones,Michael G. Shlipak,Latha Palaniappan,Laurence S. Sperling,Salim S. Virani,Katherine R. Tuttle,Ian J. Neeland,Sheryl L. Chow,Janani Rangaswami,Michael Pencina,Chiadi E Ndumele,Josef Coresh
出处
期刊:Circulation [Ovid Technologies (Wolters Kluwer)]
卷期号:149 (6): 430-449 被引量:74
标识
DOI:10.1161/circulationaha.123.067626
摘要

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS: Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763–0.809) in female and 0.757 (0.727–0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81–1.16) and 0.94 (0.81–1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD– and heart failure–specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004–0.005] and 0.005 [0.004–0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84–1.20] versus 1.39 [1.14–1.65]; P =0.01). CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.
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