Cited by 1,234Health Promotion and Cardiovascular PreventionCardiovascular Health and Risk FactorsLipoproteins and Cardiovascular Health

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

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Khan, Sadiya S., Matsushita, Kunihiro, Sang, Yingying, Ballew, Shoshana H., Grams, Morgan E., Surapaneni, Aditya, Blaha, Michael J., Carson, April P., Chang, Alexander R., Ciemins, Elizabeth, Go, Alan S., Gutierrez, Orlando M., Hwang, Shih-Jen, Jassal, Simerjot K., Kovesdy, Csaba P., Lloyd-Jones, Donald M., Shlipak, Michael G., Palaniappan, Latha P., Sperling, Laurence, Virani, Salim S., Tuttle, Katherine, Neeland, Ian J., Chow, Sheryl L., Rangaswami, Janani, Pencina, Michael J., Ndumele, Chiadi E., Coresh, Josef, for the Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group·2024·Circulation

DOI 10.1161/circulationaha.123.067626PMC PMC10910659

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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.

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Authors: Khan, S. S., Matsushita, K., Sang, Y., Ballew, S. H., Grams, M. E., Surapaneni, A., Blaha, M. J., Carson, A. P., Chang, A. R., Ciemins, E., Go, A. S., Gutierrez, O. M., Hwang, S.-J., Jassal, S. K., Kovesdy, C. P., Lloyd-Jones, D. M., Shlipak, M. G., Palaniappan, L. P., Sperling, L., Virani, S. S., Tuttle, K., Neeland, I. J., Chow, S. L., Rangaswami, J., Pencina, M. J., Ndumele, C. E., Coresh, J., & for the Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group
Affiliations: Northwestern University (US)
Johns Hopkins University (US)
New York University (US)
University of Mississippi Medical Center (US)
Departments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA (A.R.C.).
American Medical Group Association (US)
Kaiser Permanente (US)
Departments of Epidemiology and Medicine, University of Alabama at Birmingham (O.M.G.).
National Heart, Lung, and Blood Institute (US)
University of California San Diego (US)
University of Tennessee Health Science Center (US)
Department of Preventive Medicine, Northwestern University, Chicago, IL (D.M.L.-J.).
San Francisco VA Medical Center (US)
Stanford Medicine (US)
Emory University (US)
Aga Khan University (PK)
University of Washington (US)
University Hospitals of Cleveland (US)
Western University of Health Sciences (US)
George Washington University (US)
Duke University (US)
ORCID: Khan, Sadiya S.
Matsushita, Kunihiro
Sang, Yingying
Ballew, Shoshana H.
Grams, Morgan E.
Surapaneni, Aditya
Blaha, Michael J.
Carson, April P.
Chang, Alexander R.
Ciemins, Elizabeth
Go, Alan S.
Gutierrez, Orlando M.
Hwang, Shih-Jen
Jassal, Simerjot K.
Kovesdy, Csaba P.
Lloyd-Jones, Donald M.
Shlipak, Michael G.
Palaniappan, Latha P.
Sperling, Laurence
Virani, Salim S.
Tuttle, Katherine
Neeland, Ian J.
Chow, Sheryl L.
Rangaswami, Janani
Pencina, Michael J.
Ndumele, Chiadi E.
Coresh, Josef
Venue: Circulation
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2024-02-06
Volume: 149
Issue: 6
Pages: 430-449
ISSN: 0009-7322 (ISSN-L)
1524-4539
DOI: 10.1161/circulationaha.123.067626
PMC: PMC10910659
OpenAlex: W4388573970
Cited by: 1,234
References: 92
Citations by year: 2026: 631 · 2025: 467 · 2024: 134
FWCI: 314.97
Type: journal article
Language: en
Update notices: correction (2024-03-11)
correction (2026-10-14)
Open access: green
www.ncbi.nlm.nih.gov
Locations: Circulation (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Digital Commons-TMC (Texas Medical Center) (submittedVersion)
eCommons - AKU (Aga Khan University) (submittedVersion)
Funders: HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100446)
HHS | NIH | National Heart, Lung, and Blood Institute (R21HL165376)
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
Keywords: PREVENT equations, cardiovascular risk prediction, absolute cardiovascular risk, atherosclerotic cardiovascular disease, heart failure, albumin-creatinine ratio, estimated glomerular filtration rate, social deprivation index, HbA1c, risk discrimination and calibration
MeSH terms: Adult, Aged, Albumins, American Heart Association, Cardiovascular Diseases, Creatinine, Female, Heart Failure, Glycated Hemoglobin, Humans, Male, Middle Aged, Risk Factors, Risk Assessment, Atherosclerosis

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

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