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

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

^aDepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (S.S.K.)
^bSadiya S. Khan https://orcid.org/0000-0003-0643-1859 Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
^cDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
^dKunihiro Matsushita https://orcid.org/0000-0002-7179-718X Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
^eDepartment of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
^fYingying Sang https://orcid.org/0000-0002-6179-7864 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
^gShoshana H. Ballew https://orcid.org/0000-0002-7547-3764 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
^hDepartment of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)
ⁱMorgan E. Grams https://orcid.org/0000-0002-4430-6023 New York University Grossman School of Medicine, Department of Medicine, Division of Precision Medicine, New York, NY
^jAditya Surapaneni https://orcid.org/0000-0003-4978-5980 New York University Grossman School of Medicine, Department of Medicine, Division of Precision Medicine, New York, NY
^kJohns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Baltimore, MD (M.J.B.)
^lMichael J. Blaha https://orcid.org/0000-0001-5138-9683 Johns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Baltimore, MD
^mUniversity of Mississippi Medical Center, Jackson (A.P.C.)
ⁿApril P. Carson https://orcid.org/0000-0002-7970-6756 University of Mississippi Medical Center, Jackson, MS
^oDepartments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA (A.R.C.)
^pAlexander R. Chang https://orcid.org/0000-0002-8114-7447 Departments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA
^qAMGA (American Medical Group Association), Alexandria, VA (E.C.)
^rElizabeth Ciemins https://orcid.org/0000-0003-1262-7946 AMGA (American Medical Group Association), Alexandria, VA
^sDivision of Research, Kaiser Permanente Northern California, Oakland
^tDepartment of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA
^uDepartments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco
^vDepartment of Medicine (Nephrology), Stanford University School of Medicine, Palo Alto, CA (A.S,G.)
^wAlan S. Go https://orcid.org/0000-0001-9109-0811 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
^xDepartments of Epidemiology and Medicine, University of Alabama at Birmingham (O.M.G.)
^yOrlando M. Gutierrez https://orcid.org/0000-0001-6593-3571 Departments of Epidemiology and Medicine, University of Alabama at Birmingham, Birmingham, AL
^zNational Heart, Lung, and Blood Institute, Framingham, MA (S.-J.H.)
^aaShih-Jen Hwang https://orcid.org/0000-0002-2129-5704 National Heart, Lung, and Blood Institute, Framingham, MA
^abDivision of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, CA (S.K.J.)
^acSimerjot K. Jassal https://orcid.org/0000-0001-6781-2220 Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, San Diego, CA
^adMedicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis (C.P.K.)
^aeCsaba P. Kovesdy Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, TN
^afDepartment of Preventive Medicine, Northwestern University, Chicago, IL (D.M.L.-J.)
^agDonald M. Lloyd-Jones https://orcid.org/0000-0003-0847-6110 Department of Preventive Medicine, Northwestern University, Chicago, IL
^ahDepartment of Medicine, Epidemiology, and Biostatistics, University of California, San Francisco, and San Francisco VA Medical Center (M.G.S.)
^aiMichael G. Shlipak https://orcid.org/0000-0002-9559-204X Department of Medicine, Epidemiology, and Biostatistics, University of California, San Francisco, and San Francisco VA Medical Center, San Francisco, CA
^ajCenter for Asian Health Research and Education and the Department of Medicine, Stanford University School of Medicine, CA (L.P.P.)
^akLatha P. Palaniappan https://orcid.org/0000-0002-1245-665X Center for Asian Health Research and Education and the Department of Medicine, Stanford University School of Medicine, Stanford, CA
^alDepartment of Cardiology, Emory University, Atlanta, GA (L.S.)
^amLaurence Sperling https://orcid.org/0000-0001-9417-6370 Department of Cardiology, Emory University, Atlanta, GA
^anDepartment of Medicine, The Aga Khan University, Karachi, Pakistan
^aoTexas Heart Institute and Baylor College of Medicine, Houston (S.S.V.)
^apSalim S. Virani https://orcid.org/0000-0001-9541-6954 Department of Medicine, The Aga Khan University, Karachi, Pakistan, Texas Heart Institute and Baylor College of Medicine, Houston, TX
^aqProvidence Medical Research Center, Providence Inland Northwest Health, Spokane, WA
^arKidney Research Institute and Institute of Translational Health Sciences, University of Washington, Seattle (K.T.)
^asKatherine Tuttle https://orcid.org/0000-0002-2235-0103 Providence Medical Research Center, Providence Inland Northwest Health, Spokane, WA
^atUH 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, OH (I.J.N.)
^auIan J. Neeland https://orcid.org/0000-0003-2831-3618 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
^avDepartment of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA (S.L.C.)
^awSheryl L. Chow Department of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA
^axWashington DC VA Medical Center and George Washington University School of Medicine (J.R.)
^ayJanani Rangaswami https://orcid.org/0000-0002-4313-9825 Washington DC VA Medical Center and George Washington University School of Medicine, Washington, DC
^azDepartment of Biostatistics, Duke University Medical Center, Durham, NC (M.J.P.)
^baMichael J. Pencina https://orcid.org/0000-0002-1968-2641 Department of Biostatistics, Duke University Medical Center, Durham, NC
^bbDivision of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (C.E.N.)
^bcChiadi E. Ndumele https://orcid.org/0000-0002-1603-282X Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD

Abstract

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.

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

Abstract

Plain-language summary

Discussion