Development and Validation of the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations


Sadiya S. Khan, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.
Kunihiro Matsushita, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Yingying Sang, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Shoshana H. Ballew, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Morgan E. Grams, New York University Grossman School of Medicine, Department of Medicine, Division of Precision Medicine, New York, NY.
Aditya Surapaneni, New York University Grossman School of Medicine, Department of Medicine, Division of Precision Medicine, New York, NY.
Michael J. Blaha, Johns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Baltimore, MD.
April P. Carson, University of Mississippi Medical Center, Jackson, MS.
Alexander R. Chang, Departments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA.
Elizabeth Ciemins, AMGA (American Medical Group Association), Alexandria, VA.
Alan S. Go, Division of Research, Kaiser Permanente Northern California, Oakland, CA, Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco, CA, Department of Medicine (Nephrology), Stanford University School of Medicine, Palo Alto, CA.
Orlando M. Gutierrez, Departments of Epidemiology and Medicine, University of Alabama at Birmingham, Birmingham, AL.
Shih-Jen Hwang, National Heart, Lung, and Blood Institute, Framingham, MA.
Simerjot K. Jassal, Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, San Diego, CA.
Csaba P. Kovesdy, Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, TN.
Donald M. Lloyd-Jones, Department of Preventive Medicine, Northwestern University, Chicago, IL.
Michael G. Shlipak, Department of Medicine, Epidemiology, and Biostatistics, University of California, San Francisco, and San Francisco VA Medical Center, San Francisco, CA.
Latha P. Palaniappan, Center for Asian Health Research and Education and the Department of Medicine, Stanford University School of Medicine, Stanford, CA.
Laurence Sperling, Department of Cardiology, Emory University, Atlanta, GA.
Salim S. Virani, Department of Medicine, The Aga Khan University, Karachi, Pakistan, Texas Heart Institute and Baylor College of Medicine, Houston, TX.
Katherine Tuttle, Providence Medical Research Center, Providence Inland Northwest Health, Spokane, WA; Kidney Research Institute and Institute of Translational Health Sciences, University of Washington, Seattle, WA.
Ian J. Neeland, UH Center for Cardiovascular Prevention, Translational Science Unit, Center for Integrated and Novel Approaches in Vascular-Metabolic Disease (CINEMA), Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH.
Sheryl L. Chow, Department of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA.
Janani Rangaswami, Washington DC VA Medical Center and George Washington University School of Medicine, Washington, DC.
Michael J. Pencina, Department of Biostatistics, Duke University Medical Center, Durham, NC.
Chiadi E. Ndumele, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD.
Josef Coresh, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Document Type

Journal Article

Publication Date







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 AHA Predicting Risk of CVD EVENTs (PREVENT) equations among US adults aged 30-79 years without known CVD. The derivation sample included individual-level participant data from 25 datasets (N=3,281,919) between 1992-2017. The primary outcome was CVD (atherosclerotic CVD [ASCVD] and heart failure [HF]). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, anti-hypertensive or statin use, diabetes) and estimated glomerular filtration rate [eGFR]. 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 dataset and meta-analyzed. Discrimination was assessed using Harrell's C-statistic. Calibration was calculated as the slope of the observed vs. predicted risk by decile. Additional equations to predict each CVD subtype (ASCVD, HF) and include optional predictors (urine albumin-to-creatinine ratio [UACR], hemoglobin A1c [HbA1c]), and social deprivation index [SDI]) were also developed. External validation was performed in 3,330,085 participants from 21 additional datasets. Among 6,612,004 adults included, mean (SD) age was 53 (12) years and 56% were female. 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 [IQI]: 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (IQI 0.81 -1.16) and 0.94 (0.81-1.13) among females and males, respectively. Similar estimates for discrimination and calibration were observed for ASCVD- and HF-specific models. The improvement in discrimination was small but statistically significant when UACR, HbA1c, and SDI were added together to the base model to total CVD (ΔC-statistic [IQI] 0.004 [0.004, 0.005] and 0.005 [0.004, 0.007] among females and males, respectively). Calibration improved significantly when UACR was added to the base model among those with marked albuminuria (>300mg/g) (1.05 [0.84-1.20] vs. 1.39 [1.14-1.65], p=0.01). PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults using routinely available clinical variables.