{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:49:48Z","timestamp":1791560988492,"version":"4.3.4"},"reference-count":26,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["CJASN"],"published-print":{"date-parts":[[2026,10]]},"abstract":"\n Key Points<\/jats:title>\n \n \n \n The Predicting Risk of Cardiovascular Disease EVENTs equations optimize risk prediction in the modern era of increasing cardiovascular kidney metabolic disease prevalence.<\/jats:p>\n <\/jats:list-item>\n \n Risk prediction is most helpful to intensify therapy in those with less advanced kidney disease.<\/jats:p>\n <\/jats:list-item>\n \n In those with advanced kidney disease, risk indices provide little utility beyond biomarkers alone such as eGFR and albuminuria.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n Background<\/jats:title>\n Cardiovascular kidney metabolic syndrome is a growing public health problem and leading cause of cardiovascular mortality. We evaluated model performance of the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations in a large single health system population in patients with CKD and across strata of kidney function and compared findings where applicable to those of the pooled cohort equations (PCE).<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Veterans with eGFR measurements were divided into three groups: eGFR \u226560, eGFR 30\u201359, and eGFR 15\u201329. We assessed the base PREVENT equations for total cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure as well as add-on equations using albuminuria. Accuracy of equations was assessed with c-index as a measure of discrimination and calibration curve slopes as a measure of calibration.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Overall c-index was 0.651 for PREVENT-CVD, 0.633 for PREVENT-ASCVD, and 0.673 for PREVENT-heart failure. Discrimination of PREVENT-ASCVD was modestly greater than that of PCE (0.633 versus 0.629, respectively). For all three base PREVENT equations, discrimination declined with advancing kidney disease. Overall calibration slopes for the PREVENT equations ranged from 0.78 to 1.27. PREVENT-ASCVD had superior calibration compared to PCE with a calibration slope of 1.27 and 0.53, respectively. In those with urine albumin to creatinine ratio (UACR) measurements, discrimination of UACR add-on equations improved from base equations in those with less advanced kidney disease (c-index 0.624 for PREVENT-CVD UACR versus 0.607 for PREVENT-CVD base equation in those with eGFR \u226560, and c-index 0.586 for PREVENT-CVD UACR versus 0.563 for PREVENT-CVD base equation in those with eGFR 30\u201359). This trend was similar for the PREVENT-ASCVD and PREVENT-heart failure UACR add-on equations.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n In a CKD population, the PREVENT equations perform best in those less advanced stages of CKD and cardiovascular kidney metabolic, presenting an opportunity to apply disease-preventing and disease-modifying therapeutics in a timely manner.<\/jats:p>\n <\/jats:sec>","DOI":"10.2215\/cjn.0000001129","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T19:00:38Z","timestamp":1785351638000},"page":"1744-1752","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Validation of the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations in a CKD Population"],"prefix":"10.2215","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4198-6515","authenticated-orcid":false,"given":"Nikitha","family":"Murthy","sequence":"first","affiliation":[{"name":"Department of Cardiology, Loma Linda Medical Center, Loma Linda, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1243-0370","authenticated-orcid":false,"given":"Alyssa","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Loma Linda Medical Center, Loma Linda, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5024-7891","authenticated-orcid":false,"given":"Janani","family":"Rangaswami","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Washington DC Veterans Affairs Medical Center, Washington, DC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0643-1859","authenticated-orcid":false,"given":"Sadiya S.","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5582-3165","authenticated-orcid":false,"given":"Roy O.","family":"Mathew","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Loma Linda VA Healthcare System, Loma Linda, California"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"issue":"18","key":"B1-20261009","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1001\/jama.2021.5469","article-title":"The leading causes of death in the US for 2020","volume":"325","author":"Ahmad","year":"2021","journal-title":"JAMA."},{"issue":"18","key":"B2-20261009","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1016\/j.jacc.2024.03.368","article-title":"Prevalence of the cardiovascular-kidney-metabolic syndrome in the United States","volume":"83","author":"Minhas","year":"2024","journal-title":"J Am Coll Cardiol."},{"issue":"17","key":"B3-20261009","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1161\/CIRCULATIONAHA.105.169404","article-title":"Diagnosis and management of the metabolic syndrome","volume":"112","author":"Grundy","year":"2005","journal-title":"Circulation."},{"issue":"4","key":"B4-20261009","doi-asserted-by":"crossref","first-page":"S117","DOI":"10.1016\/j.kint.2023.10.018","article-title":"KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease","volume":"105","author":"Stevens","year":"2024","journal-title":"Kidney Int."},{"issue":"20","key":"B5-20261009","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1161\/CIR.0000000000001186","article-title":"A synopsis of the evidence for the science and clinical management of cardiovascular-kidney-metabolic (CKM) syndrome: a scientific statement from the American Heart Association","volume":"148","author":"Ndumele","year":"2023","journal-title":"Circulation."},{"issue":"6","key":"B6-20261009","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1161\/CIRCULATIONAHA.123.067626","article-title":"Development and validation of the American Heart Association\u2019s PREVENT equations","volume":"149","author":"Khan","year":"2024","journal-title":"Circulation."},{"issue":"1","key":"B7-20261009","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1093\/eurjpc\/zwac176","article-title":"Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP","volume":"30","author":"Matsushita","year":"2023","journal-title":"Eur J Prev Cardiol."},{"key":"B8-20261009","doi-asserted-by":"crossref","first-page":"100552","DOI":"10.1016\/j.eclinm.2020.100552","article-title":"Incorporating kidney disease measures into cardiovascular risk prediction: development and validation in 9 million adults from 72 datasets","volume":"27","author":"Matsushita","year":"2020","journal-title":"EClinicalMedicine."},{"issue":"3","key":"B9-20261009","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1681\/ASN.2021060747","article-title":"Risk prediction models for atherosclerotic cardiovascular disease in patients with chronic kidney disease: the CRIC study","volume":"33","author":"Bundy","year":"2022","journal-title":"J Am Soc Nephrol."},{"key":"B10-20261009","article-title":"Cardiovascular risk models in chronic kidney disease: a systematic review and external validation of models","author":"Major","year":"2022","journal-title":"medRxiv."},{"issue":"3","key":"B11-20261009","doi-asserted-by":"crossref","first-page":"e60008","DOI":"10.1371\/journal.pone.0060008","article-title":"Framingham risk score with cardiovascular events in chronic kidney disease","volume":"8","author":"Chen","year":"2013","journal-title":"PLoS One."},{"issue":"24","key":"B12-20261009","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1161\/CIR.0000000000001191","article-title":"Novel prediction equations for absolute risk assessment of total cardiovascular disease incorporating cardiovascular-kidney-metabolic health: a scientific statement from the American Heart Association","volume":"148","author":"Khan","year":"2023","journal-title":"Circulation."},{"issue":"6","key":"B13-20261009","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1016\/j.mayocp.2022.01.018","article-title":"Socioeconomic deprivation and premature cardiovascular mortality in the United States","volume":"97","author":"Bevan","year":"2022","journal-title":"Mayo Clin Proc."},{"issue":"8","key":"B14-20261009","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.1038\/s41591-025-03789-2","article-title":"Performance of the American Heart Association\u2019s PREVENT risk score for cardiovascular risk prediction in a multiethnic population","volume":"31","author":"Mathew","year":"2025","journal-title":"Nat Med."},{"issue":"19","key":"B15-20261009","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1056\/NEJMoa2102953","article-title":"New creatinine- and cystatin C\u2013based equations to estimate GFR without race","volume":"385","author":"Inker","year":"2021","journal-title":"New Engl J Med."},{"issue":"6","key":"B16-20261009","doi-asserted-by":"crossref","first-page":"426","DOI":"10.7326\/M20-0529","article-title":"Conversion of urine protein\u2013creatinine ratio or urine dipstick protein to urine albumin\u2013creatinine ratio for use in chronic kidney disease screening and prognosis","volume":"173","author":"Sumida","year":"2020","journal-title":"Ann Intern Med."},{"key":"B18-20261009","doi-asserted-by":"crossref","first-page":"e069249","DOI":"10.1136\/bmj-2021-069249","article-title":"Validation of prediction models in the presence of competing risks: a guide through modern methods","volume":"377","author":"van Geloven","year":"2022","journal-title":"BMJ."},{"issue":"3","key":"B20-20261009","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.jacc.2007.03.037","article-title":"The framingham predictive instrument in chronic kidney disease","volume":"50","author":"Weiner","year":"2007","journal-title":"J Am Coll Cardiol."},{"issue":"20","key":"B21-20261009","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1161\/CIR.0000000000001184","article-title":"Cardiovascular-kidney-metabolic health: a presidential advisory from the American Heart Association","volume":"148","author":"Ndumele","year":"2023","journal-title":"Circulation."},{"issue":"12","key":"B22-20261009","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.2215\/CJN.0000000831","article-title":"Predictive performance of the cardiovascular event risk test 2 risk score in hemodialysis patients with ESKD","volume":"20","author":"Witoslawska","year":"2025","journal-title":"Clin J Am Soc Nephrol."},{"issue":"11","key":"B23-20261009","first-page":"e114","article-title":"2025 AHA\/ACC\/AANP\/AAPA\/ABC\/ACCP\/ACPM\/AGS\/AMA\/ASPC\/NMA\/PCNA\/SGIM guideline for the prevention, detection, evaluation and management of high blood pressure in adults: a report of the American College of Cardiology\/American Heart Association Joint Committee on clinical practice guidelines","volume":"152","author":"Jones","year":"2025","journal-title":"Circulation."},{"issue":"17","key":"B24-20261009","first-page":"e1154","article-title":"2026 ACC\/AHA\/AACVPR\/ABC\/ACPM\/ADA\/AGS\/APhA\/ASPC\/NLA\/PCNA guideline on the management of dyslipidemia: a report of the American College of Cardiology\/American Heart Association Joint Committee on clinical practice guidelines","volume":"153","author":"Blumenthal","year":"2026","journal-title":"Circulation."},{"issue":"4S","key":"B25-20261009","first-page":"S117","article-title":"KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease","volume":"105","year":"2024","journal-title":"Kidney Int."},{"issue":"5","key":"B26-20261009","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1016\/j.kint.2022.08.012","article-title":"Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO)","volume":"102","author":"De Boer","year":"2022","journal-title":"Kidney Int."},{"issue":"11","key":"B27-20261009","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s41581-022-00616-6","article-title":"Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease","volume":"18","author":"Matsushita","year":"2022","journal-title":"Nat Rev Nephrol."},{"issue":"4","key":"B28-20261009","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.cardfail.2021.10.007","article-title":"Ten-year risk-prediction equations for incident heart failure hospitalizations in chronic kidney disease: findings from the chronic renal insufficiency cohort study and the multi-ethnic study of atherosclerosis","volume":"28","author":"Mehta","year":"2022","journal-title":"J Card Fail."}],"container-title":["Clinical Journal of the American Society of Nephrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.2215\/CJN.0000001129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:00:53Z","timestamp":1791558053000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.2215\/CJN.0000001129"}},"subtitle":["A Nationwide Veterans Analysis"],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":26,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.2215\/cjn.0000001129","relation":{},"ISSN":["1555-9041","1555-905X"],"issn-type":[{"value":"1555-9041","type":"print"},{"value":"1555-905X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,29]]},"assertion":[{"value":"2026-03-03","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-21","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}