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dc.contributor.authorMatsushita, K
dc.contributor.authorJassal, Simerjot
dc.contributor.authorSolbu, Marit Dahl
dc.contributor.authorSang, Yingying
dc.contributor.authorBallew, Shoshana H.
dc.contributor.authorGrams, Morgan E.
dc.contributor.authorSurapaneni, Aditya
dc.contributor.authorArnlov, Johan
dc.contributor.authorBansal, Nisha
dc.contributor.authorBozic, Milica
dc.contributor.authorBrenner, Hermann
dc.contributor.authorBrunskill, Nigel J.
dc.contributor.authorChang, Alex R.
dc.contributor.authorChinnadurai, Rajkumar
dc.contributor.authorCirillo, Massimo
dc.contributor.authorCorrea, Adolfo
dc.contributor.authorEbert, Natalie
dc.contributor.authorEckardt, Kai-Uwe
dc.contributor.authorGansevoort, Ron T.
dc.contributor.authorGutierrez, Orlando
dc.contributor.authorHadaegh, Farzad
dc.contributor.authorHe, Jiang
dc.contributor.authorHwang, Shih-Jen
dc.contributor.authorJafar, Tazeen H.
dc.contributor.authorKayama, Takamasa
dc.contributor.authorKovesdy, Csaba P.
dc.contributor.authorLandman, Gijs W.
dc.contributor.authorLevey, Andrew S.
dc.contributor.authorLloyd-Jones, Donald M.
dc.contributor.authorMajor, Rupert W.
dc.contributor.authorMiura, Katsuyuki
dc.contributor.authorMuntner, Paul
dc.contributor.authorNadkarni, Girish N.
dc.contributor.authorNaimark, David M.J.
dc.contributor.authorNowak, Christoph
dc.contributor.authorOhkubo, Takayoshi
dc.contributor.authorPena, Michelle J.
dc.contributor.authorPolkinghorne, Kevan R.
dc.contributor.authorSabanayagam, Charumathi
dc.contributor.authorSairenchi, Toshimi
dc.contributor.authorSchneider, Markus P.
dc.contributor.authorShalev, Varda
dc.contributor.authorShlipak, Michael
dc.contributor.authorStempniewicz, Nikita
dc.contributor.authorTollitt, James
dc.contributor.authorValdivielso, José M.
dc.contributor.authorvan der Leeuw, Joep
dc.contributor.authorWang, Angela Yee-Moon
dc.contributor.authorWen, Chi-Pang
dc.contributor.authorWoodward, Mark
dc.contributor.authorYatsuya, Hiroshi
dc.contributor.authorZhang, Luxia
dc.contributor.authorSchaeffner, Elke
dc.contributor.authorCoresh, Josef
dc.date.accessioned2022-02-08T10:19:15Z
dc.date.available2022-02-08T10:19:15Z
dc.date.issued2020-10-04
dc.description.abstractBackground: 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.<p> <p>Methods: 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). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch.<p> <p>Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Dc-statistic 0.027 [95% CI 0.018 0.036] and 0.010 [0.007 0.013] and categorical net reclassification improvement 0.080 [0.032 0.127] and 0.056 [0.044 0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89 3.40) in very high-risk CKD (e.g., eGFR 30 44 ml/min/ 1.73m<sup>2</sup> with albuminuria 30 mg/g), 1.86 (1.48 2.44) in high-risk CKD (e.g., eGFR 45 59 ml/min/1.73m<sup>2</sup> with albuminuria 30 299 mg/g), and 1.37 (1.14 1.69) in moderate risk CKD (e.g., eGFR 60 89 ml/min/ 1.73m2 with albuminuria 30 299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37 1.81), 1.24 (1.10 1.54), and 1.21 (0.98 1.46). Interpretation: The “CKD Patch” can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available.en_US
dc.identifier.citationMatsushita K, Jassal S, Solbu MD. Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets. EClinicalMedicine. 2020en_US
dc.identifier.cristinIDFRIDAID 1881217
dc.identifier.doi10.1016/j.eclinm.2020.100552
dc.identifier.issn2589-5370
dc.identifier.urihttps://hdl.handle.net/10037/23954
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalEClinicalMedicine
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.titleIncorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasetsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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