{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Khan SS"],"funding":["NCATS NIH HHS","HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases","NIDDK NIH HHS","NHLBI NIH HHS","NIMHD NIH HHS","HHS | NIH | National Heart, Lung, and Blood Institute","NIH HHS","NIGMS NIH HHS"],"pagination":["430-449"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10910659"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["149(6)"],"pubmed_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."],"journal":["Circulation"],"pubmed_title":["Development and Validation of the American Heart Association's PREVENT Equations."],"pmcid":["PMC10910659"],"funding_grant_id":["P20 GM109036","K24 HL150476","U54 DK083912","R01DK100446","R21HL165376","U01 DK060963","U24 DK060990","UL1 TR002319","HHSN268201700002I","U01 DK100846","R01 HL165452","OT2 OD032581","R01 MD014712","OT2 HL161847","R01 DK100446","U2C DK114886","R21 HL165376"],"pubmed_authors":["Virani SS","Chang AR","Sang Y","Matsushita K","Surapaneni A","Pencina MJ","Go AS","Hwang SJ","Neeland IJ","Ndumele CE","Blaha MJ","Jassal SK","Lloyd-Jones DM","Chow SL","Khan SS","Shlipak MG","Tuttle K","Grams ME","Ballew SH","Sperling L","Ciemins E","Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group","Coresh J","Palaniappan LP","Rangaswami J","Gutierrez OM","Carson AP","Kovesdy CP"],"additional_accession":[]},"is_claimable":false,"name":"Development and Validation of the American Heart Association's PREVENT Equations.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Feb","modification":"2025-07-11T03:03:49.804Z","creation":"2025-04-06T22:25:28.095Z"},"accession":"S-EPMC10910659","cross_references":{"pubmed":["37947085"],"doi":["10.1161/circulationaha.123.067626","10.1161/CIRCULATIONAHA.123.067626"]}}