{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T14:20:06Z","timestamp":1792678806994,"version":"4.5.0"},"reference-count":90,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"6","funder":[{"DOI":"10.13039\/100000062","name":"HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["R01DK100446"],"award-info":[{"award-number":["R01DK100446"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000050","name":"HHS | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"crossref","award":["R21HL165376"],"award-info":[{"award-number":["R21HL165376"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circulation"],"published-print":{"date-parts":[[2024,2,6]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>The derivation sample included individual-level participant data from 25 data sets (N=3\u2009281\u2009919) 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\u2009330\u2009085 participants from 21 additional data sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Among 6\u2009612\u2009004 adults included, mean\u00b1SD age was 53\u00b112 years, and 56% were women. Over a mean\u00b1SD follow-up of 4.8\u00b13.1 years, there were 211\u2009515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763\u20130.809) in female and 0.757 (0.727\u20130.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81\u20131.16) and 0.94 (0.81\u20131.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD\u2013 and heart failure\u2013specific 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 (\u0394C-statistic [interquartile interval] 0.004 [0.004\u20130.005] and 0.005 [0.004\u20130.007] 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 (&gt;300 mg\/g; 1.05 [0.84\u20131.20] versus 1.39 [1.14\u20131.65];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =0.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circulationaha.123.067626","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T05:00:21Z","timestamp":1699592421000},"page":"430-449","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1282,"title":["Development and Validation of the American Heart Association\u2019s PREVENT Equations"],"prefix":"10.1161","volume":"149","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0643-1859","authenticated-orcid":true,"given":"Sadiya S.","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (S.S.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7179-718X","authenticated-orcid":true,"given":"Kunihiro","family":"Matsushita","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6179-7864","authenticated-orcid":true,"given":"Yingying","family":"Sang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)."},{"name":"Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7547-3764","authenticated-orcid":true,"given":"Shoshana H.","family":"Ballew","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)."},{"name":"Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4430-6023","authenticated-orcid":true,"given":"Morgan E.","family":"Grams","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4978-5980","authenticated-orcid":true,"given":"Aditya","family":"Surapaneni","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5138-9683","authenticated-orcid":true,"given":"Michael J.","family":"Blaha","sequence":"additional","affiliation":[{"name":"Johns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Baltimore, MD (M.J.B.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-6756","authenticated-orcid":true,"given":"April P.","family":"Carson","sequence":"additional","affiliation":[{"name":"University of Mississippi Medical Center, Jackson (A.P.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8114-7447","authenticated-orcid":true,"given":"Alexander R.","family":"Chang","sequence":"additional","affiliation":[{"name":"Departments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA (A.R.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1262-7946","authenticated-orcid":true,"given":"Elizabeth","family":"Ciemins","sequence":"additional","affiliation":[{"name":"AMGA (American Medical Group Association), Alexandria, VA (E.C.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9109-0811","authenticated-orcid":true,"given":"Alan S.","family":"Go","sequence":"additional","affiliation":[{"name":"Division of Research, Kaiser Permanente Northern California, Oakland; Department of Health Systems Science, Kaiser Permanente Bernard J. 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