{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Matsushita K"],"funding":["NIDDK NIH HHS","National Institute of Diabetes and Digestive","National Institute for Health Research (NIHR)","US National Kidney Foundation"],"pagination":["100552"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7599294"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27"],"pubmed_abstract":["<h4>Background</h4>Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. \"CKD Patch\" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures.<h4>Methods</h4>Utilizing data from 4,143,535 adults from 35 datasets, we developed several \"CKD Patches\" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). T"],"journal":["EClinicalMedicine"],"pubmed_title":["Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets."],"pmcid":["PMC7599294"],"funding_grant_id":["U01 DK061022","R01DK100446","R01 DK103612","U01 DK060963","R01 DK100446","CL-2019-11-501"],"pubmed_authors":["Stempniewicz N","Major RW","Shlipak M","Eckardt KU","Schaeffner E","Chinnadurai R","Yatsuya H","Zhang L","Sang Y","Levey AS","Gutierrez O","Sabanayagam C","Tollitt J","Jassal SK","Yamagishi K","Muntner P","Cirillo M","He J","Sairenchi T","Naimark DM","Arnlov J","Solbu MD","Pena MJ","Landman GW","Ebert N","Shalev V","Brenner H","van der Leeuw J","Brunskill NJ","Valdivielso JM","Gansevoort RT","Miura K","Chang AR","Correa A","Nowak C","Matsushita K","Surapaneni A","Hadaegh F","Hwang SJ","Wang AY","Jafar TH","Schneider MP","Bozic M","Lloyd-Jones DM","Ohkubo T","Nadkarni GN","Woodward M","Grams ME","Ballew SH","Polkinghorne KR","Coresh J","Wen CP","Bansal N","Kayama T","Kovesdy CP"],"additional_accession":[]},"is_claimable":false,"name":"Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets.","description":"<h4>Background</h4>Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. \"CKD Patch\" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures.<h4>Methods</h4>Utilizing data from 4,143,535 adults from 35 datasets, we developed several \"CKD Patches\" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). T","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Oct","modification":"2025-04-04T19:21:24.287Z","creation":"2020-11-08T09:46:24Z"},"accession":"S-EPMC7599294","cross_references":{"pubmed":["33150324"],"doi":["10.1016/j.eclinm.2020.100552"]}}