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. 2024 Feb 6;149(6):430-449.
doi: 10.1161/CIRCULATIONAHA.123.067626. Epub 2023 Nov 10.

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

Sadiya S Khan  1 , Kunihiro Matsushita  2 , Yingying Sang  2   3 , Shoshana H Ballew  2   3 , Morgan E Grams  4 , Aditya Surapaneni  4 , Michael J Blaha  5 , April P Carson  6 , Alexander R Chang  7 , Elizabeth Ciemins  8 , Alan S Go  9 , Orlando M Gutierrez  10 , Shih-Jen Hwang  11 , Simerjot K Jassal  12 , Csaba P Kovesdy  13 , Donald M Lloyd-Jones  14 , Michael G Shlipak  15 , Latha P Palaniappan  16 , Laurence Sperling  17 , Salim S Virani  18 , Katherine Tuttle  19 , Ian J Neeland  20 , Sheryl L Chow  21 , Janani Rangaswami  22 , Michael J Pencina  23 , Chiadi E Ndumele  24 , Josef Coresh  2   3 ; Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group
Affiliations

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

Sadiya S Khan et al. Circulation. .

Erratum in

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.

Keywords: cardiovascular diseases; heart failure; kidney diseases; models, cardiovascular; risk assessment; social determinants of health.

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Conflict of interest statement

Disclosures Dr Coresh reports grants from the National Institutes of Health (NIH) and from the National Kidney Foundation during the conduct of the study and personal fees and other support from Healthy.io outside the submitted work. Dr Khan reports grants from NIH during the conduct of the study (R21 HL165376). Dr Chang reports consulting fees from Novartis, Reata, and Amgen, and grants from Novartis, Novo Nordisk, NIH, and the National Kidney Foundation. Dr Palaniappan reports grants from NIH during the conduct of the study (K24 HL150476). Dr Tuttle reports unpaid support from the American Heart Association and grant support from NIH (01MD014712, OT2OD032581, U2CDK114886, UL1TR002319, U54DK083912, U01DK100846, OT2HL161847, and UM1AI109568); the Centers for Disease Control and Prevention (75D301-21-P-12254), as well as Bayer and Travere, paid to the institution; consulting fees from Eli Lilly, Boehringer Ingelheim, AstraZeneca, Goldfinch Bio, Novo Nordisk, Bayer, and Travere Therapeutics Inc; honoraria from Eli Lilly, AstraZeneca, Novo Nordisk, and Bayer; unpaid participation as a Data Safety and Monitoring Board chair for National Institute of Diabetes and Digestive and Kidney diseases (NIDDK)/NIH and George Clinical Institute; and unpaid participation as a Diabetic Kidney Disease collaborative chair for the American Society of Nephrology. Dr Neeland reports speaker and consulting fees from Boehringer Ingelheim, Eli Lilly and Co, Nestle Health Science, and Bayer Pharmaceuticals. Dr Virani reports grants from NIH, US Department of Veterans Affairs, Tahir and Jooma Family. The views expressed in this article are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the US Department of Health and Human Services. All other coauthors have nothing to disclose.

Figures

Figure 1.
Figure 1.
Sex-specific calibration plots in the validation sample for the PREVENT base model for total CVD, ASCVD and HF. Predicted vs. observed risk by decile within each validation cohort (OLDW cohorts are shown in gray).
Figure 2.
Figure 2.
Estimated 10-year risk of total cardiovascular disease, atherosclerotic cardiovascular disease and heart failure stratified by sex (females on the left and males on the right for each outcome) at varying ages (35, 50 and 65 years) according to the number of elevated risk factors (from 0 to 5) adjusted for competing risks of non-CVD death. Optimal risk factor levels are defined as non-HDL cholesterol (3.5 mmol/L; 135 mg/dl), high density lipoprotein cholesterol (1.5 mmol/L, 58 mg/dl), SBP 120 mmHg, no diabetes, no smoking, no hypertension medications, no statin use, and eGFR 90 ml/min/1.73m2. Elevated risk factor levels included non-high density lipoprotein cholesterol (5.5 mmol/L; 213 mg/dl), SBP 150 mmHg, diabetes, or smoking and eGFR 45 ml/min/1.73m2. For multiple elevated risk factors, the risk shown is the average risk of all combinations.
Figure 2.
Figure 2.
Estimated 10-year risk of total cardiovascular disease, atherosclerotic cardiovascular disease and heart failure stratified by sex (females on the left and males on the right for each outcome) at varying ages (35, 50 and 65 years) according to the number of elevated risk factors (from 0 to 5) adjusted for competing risks of non-CVD death. Optimal risk factor levels are defined as non-HDL cholesterol (3.5 mmol/L; 135 mg/dl), high density lipoprotein cholesterol (1.5 mmol/L, 58 mg/dl), SBP 120 mmHg, no diabetes, no smoking, no hypertension medications, no statin use, and eGFR 90 ml/min/1.73m2. Elevated risk factor levels included non-high density lipoprotein cholesterol (5.5 mmol/L; 213 mg/dl), SBP 150 mmHg, diabetes, or smoking and eGFR 45 ml/min/1.73m2. For multiple elevated risk factors, the risk shown is the average risk of all combinations.
Figure 3.
Figure 3.
Estimated 30-year risk of total cardiovascular disease, atherosclerotic cardiovascular disease and heart failure stratified by sex (females on the left and males on the right for each outcome) at varying ages (35, 50 and 65 years) according to the number of elevated risk factors (from 0 to 5) adjusted for competing risks of non-CVD death. Optimal risk factor levels are non-HDL cholesterol (3.5 mmol/L; 135 mg/dl), high density lipoprotein cholesterol (1.5 mmol/L, 58 mg/dl), SBP 120 mmHg, no diabetes, no smoking, no hypertension medications, and no statins and eGFR 90 ml/min/1.73m2. Elevated risk factor levels considered are non-high density lipoprotein cholesterol (5.5 mmol/L; 213 mg/dl), SBP 150 mmHg, diabetes, or smoking and eGFR 45 ml/min/1.73m2. For multiple elevated risk factors, the risk shown is the average risk of all combinations.
Figure 3.
Figure 3.
Estimated 30-year risk of total cardiovascular disease, atherosclerotic cardiovascular disease and heart failure stratified by sex (females on the left and males on the right for each outcome) at varying ages (35, 50 and 65 years) according to the number of elevated risk factors (from 0 to 5) adjusted for competing risks of non-CVD death. Optimal risk factor levels are non-HDL cholesterol (3.5 mmol/L; 135 mg/dl), high density lipoprotein cholesterol (1.5 mmol/L, 58 mg/dl), SBP 120 mmHg, no diabetes, no smoking, no hypertension medications, and no statins and eGFR 90 ml/min/1.73m2. Elevated risk factor levels considered are non-high density lipoprotein cholesterol (5.5 mmol/L; 213 mg/dl), SBP 150 mmHg, diabetes, or smoking and eGFR 45 ml/min/1.73m2. For multiple elevated risk factors, the risk shown is the average risk of all combinations.
Figure 4.
Figure 4.
Key Takeaways of the American Heart Association PREVENT Equations. The AHA PREVENT equations offer several key conceptual and methodological advances in the approach utilized to estimating cardiovascular disease risk.
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Comment in

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