Development and validation of the American Heart Association’s PREVENT equations
Open accessAbstract
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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Sensitivity analyses applying the American Heart Association PREVENT risk formula based on a diverse population produced a directionally consistent treatment effect.
“We have conducted sensitivity analyses by applying the American Heart Association (AHA) Predicting Risk of CVD Events (PREVENT) risk formula based on diverse population, which produced directionally consistent treatment effect.33”
From Tirzepatide compared with semaglutide and 10-year cardiovascular disease risk reduction in obesity: <i>post-hoc</i> analysis of the SURMOUNT-5 trial (2025) · cites this paper as [33]
The American Heart Association's PREVENT Equations use a social deprivation index to recognize the impact that social deprivation can have on cardiovascular-kidney-metabolic conditions.
“The American Heart Association’s PREVENT Equations use a social deprivation index to recognize the impact that social deprivation can have on cardiovascular-kidney-metabolic conditions [96,99].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [96]
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Self-citationPerformance of the ACC/AHA Pooled Cohort Cardiovascular Risk Equations in Clinical Practice
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Self-citationHistorical Redlining, Socioeconomic Distress, and Risk of Heart Failure Among Medicare Beneficiaries
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Race- and Sex-Specific Population Attributable Fractions of Incident Heart Failure
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Self-citationToward the elimination of race-based medicine: replace race with racism as preeclampsia risk factor
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Self-citationHeart Failure Risk Associated With Severity of Modifiable Heart Failure Risk Factors: The ARIC Study
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Self-citationScreening for Prediabetes and Diabetes: Clinical Performance and Implications for Health Equity
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Self-citationCould Flozins Be the Statins for Risk-Based Primary Prevention of Heart Failure?
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Self-citationRemoving Race from Kidney Disease Diagnosis
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Self-citationRangaswami, J., & Mathew, R. O. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.1512 · Cited by 5 · see citing works
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KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease
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Self-citationPrevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020
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Self-citationCardiovascular disease and cancer: shared risk factors and mechanisms
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Self-citation2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia
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Self-citationPrevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages by Social Determinants of Health
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Self-citationPrognostic value of cardiovascular biomarkers in the population
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Albuminuria in Cardiovascular, Kidney, and Metabolic Disorders: A State-of-the-Art Review
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Self-citationCumulative Incidence of Mortality Associated with Cardiovascular–Kidney–Metabolic (CKM) Syndrome
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Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels
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Self-citationExternal Validation of the American Heart Association PREVENT Cardiovascular Disease Risk Equations
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Brazilian Guidelines of Hypertension – 2025
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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
- Published
- 2024-02-06
- Volume
- 149
- Issue
- 6
- Pages
- 430-449
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC10910659
- OpenAlex
- W4388573970
- Cited by
- 1,234
- References
- 92
- Citations by year
- 2026: 631 · 2025: 467 · 2024: 134 · 2023: 2
- FWCI
- 314.97
- Type
- journal article
- Language
- en
- Update notices
- 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 › Surgery › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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