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Development and Validation of the American Heart Association's PREVENT Equations.
Thursday,2024-04-04 11:07 America/Los_Angeles — ecterry
| Title | Development and Validation of the American Heart Association's PREVENT Equations. |
| Publication Type | Journal Article |
| Year of Publication | 2024 |
| Authors | Khan, SS, Matsushita, K, Sang, Y, Ballew, SH, Grams, ME, Surapaneni, A, Blaha, MJ, Carson, AP, Chang, AR, Ciemins, E, Go, AS, Gutierrez, OM, Hwang, S-J, Jassal, SK, Kovesdy, CP, Lloyd-Jones, DM, Shlipak, MG, Palaniappan, LP, Sperling, L, Virani, SS, Tuttle, K, Neeland, IJ, Chow, SL, Rangaswami, J, Pencina, MJ, Ndumele, CE, Coresh, J |
| Corporate/Institutional Authors | Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group |
| Journal | Circulation |
| Volume | 149 |
| Issue | 6 |
| Pagination | 430-449 |
| Date Published | 2024 Feb 06 |
| ISSN | 1524-4539 |
| Keywords | Adult, Aged, Albumins, American Heart Association, Atherosclerosis, Cardiovascular Diseases, Creatinine, Female, Glycated Hemoglobin, Heart Failure, Humans, Male, Middle Aged, Risk Assessment, Risk Factors |
| Abstract | <p><b>BACKGROUND: </b>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.</p><p><b>METHODS: </b>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.</p><p><b>RESULTS: </b>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]; =0.01).</p><p><b>CONCLUSIONS: </b>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.</p> |
| DOI | 10.1161/CIRCULATIONAHA.123.067626 |
| Alternate Journal | Circulation |
| PubMed ID | 37947085 |
| PubMed Central ID | PMC10910659 |
| Grant List | R01 MD014712 / MD / NIMHD NIH HHS / United States U2C DK114886 / DK / NIDDK NIH HHS / United States OT2 HL161847 / HL / NHLBI NIH HHS / United States U54 DK083912 / DK / NIDDK NIH HHS / United States R21 HL165376 / HL / NHLBI NIH HHS / United States OT2 OD032581 / OD / NIH HHS / United States UL1 TR002319 / TR / NCATS NIH HHS / United States HHSN268201700002I / HL / NHLBI NIH HHS / United States R01 DK100446 / DK / NIDDK NIH HHS / United States U01 DK100846 / DK / NIDDK NIH HHS / United States K24 HL150476 / HL / NHLBI NIH HHS / United States |
ePub date:
24/03