{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T20:51:09Z","timestamp":1792443069498,"version":"4.5.0"},"reference-count":34,"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":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors such as age, sex, BP, lipids, smoking, and diabetes. Commonly used tools in the general population include the Framingham Risk Score, Systematic Coronary Risk Evaluation, atherosclerotic cardiovascular disease Pooled Cohort Equations, and QResearch Cardiovascular Risk Algorithm (QRISK), the latter being regularly updated using large-scale UK health records and including a wider range of variables such as CKD. CKD is a strong, independent risk factor for CV disease, but most standard models omit kidney function and CKD-specific factors (\n                      <jats:italic toggle=\"yes\">e.g<\/jats:italic>\n                      ., inflammation, vascular calcification, and mineral metabolism disorders). Consequently, they often underestimate CV risk in CKD, particularly in advanced stages and among dialysis patients, where competing risks (\n                      <jats:italic toggle=\"yes\">e.g<\/jats:italic>\n                      ., non-CV death) further complicate risk estimation. To overcome these limitations, enhanced and CKD-specific models have been developed. Systematic Coronary Risk Evaluation Add-ons incorporate eGFR and albuminuria to refine prediction in CKD. QRISK includes CKD as a variable. The CKD Prognosis Consortium and Predicting Risk of Cardiovascular Disease EVENTs models are tailored to CKD populations and leverage large, diverse datasets to improve discrimination and calibration. Reflecting this evidence, the 2024 Kidney Disease Improving Global Outcomes guidelines recommend QRISK, CKD Prognosis Consortium, and Predicting Risk of Cardiovascular Disease EVENTs for CV risk prediction in CKD. Artificial intelligence and machine learning approaches may further enhance risk prediction by exploiting complex clinical and laboratory data, but they require external validation, transparency, and assessment of clinical utility before broad implementation. CV risk calculators should support, not replace, clinical judgment and must be embedded within individualized, multifactorial management strategies. This narrative review summarizes the main characteristics, strengths, and limitations of currently available CV risk tools for CKD, focusing on those endorsed by major international guidelines and used in routine practice. We adopt a global perspective, while acknowledging that most evidence derives from high-income settings, which limits generalizability.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.2215\/cjn.0000001025","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T17:00:33Z","timestamp":1770656433000},"page":"1826-1835","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":3,"title":["Cardiovascular Risk Assessment Tools in CKD"],"prefix":"10.2215","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-1996","authenticated-orcid":false,"given":"Carmine","family":"Zoccali","sequence":"first","affiliation":[{"name":"Renal Research Institute, New York City, New York"},{"name":"Institute of Molecular Biology and Genetics (Biogem), Ariano Irpino, Italy"},{"name":"Associazione Ipertensione, Nefrologia Trapianto (IPNET), c\/o Nefrologia, Grande Ospedale Metropolitano, Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0007-1947","authenticated-orcid":false,"given":"Vianda S.","family":"Stel","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, ERA Registry, Amsterdam UMC Location, University of Amsterdam, Amsterdam, The Netherlands"},{"name":"Amsterdam Public Health Research Institute, Quality of Care, Amsterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0322-976X","authenticated-orcid":false,"given":"Francesca","family":"Mallamaci","sequence":"additional","affiliation":[{"name":"Associazione Ipertensione, Nefrologia Trapianto (IPNET), c\/o Nefrologia, Grande Ospedale Metropolitano, Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Tripepi","sequence":"additional","affiliation":[{"name":"Clinical Epidemiology Unit, CNR Institute of Clinical Physiology, Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1297-0675","authenticated-orcid":false,"given":"Mehmet","family":"Kanbay","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, Ko\u00e7 University School of Medicine, Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7369-7812","authenticated-orcid":false,"given":"Michel","family":"Jadoul","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0444-8569","authenticated-orcid":false,"given":"Kitty J","family":"Jager","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, ERA Registry, Amsterdam UMC Location, University of 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