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@ARTICLE{Matsushita:181232,
      author       = {K. Matsushita and S. Kaptoge and S. H. Hageman and Y. Sang
                      and S. H. Ballew and M. E. Grams and A. Surapaneni and L.
                      Sun and J. Arnlov and M. Bozic and H. Brenner$^*$ and N. J.
                      Brunskill and A. R. Chang and R. Chinnadurai and M. Cirillo
                      and A. Correa and N. Ebert and K. U. Eckardt and R. T.
                      Gansevoort and O. Gutierrez and F. Hadaegh and J. He and S.
                      J. Hwang and T. H. Jafar and S. K. Jassal and T. Kayama and
                      C. P. Kovesdy and G. W. Landman and A. S. Levey and D. M.
                      Lloyd-Jones and R. W. Major and K. Miura and P. Muntner and
                      G. N. Nadkarni and C. Nowak and T. Ohkubo and M. J. Pena and
                      K. R. Polkinghorne and T. Sairenchi and E. Schaeffner and M.
                      P. Schneider and V. Shalev and M. G. Shlipak and M. D. Solbu
                      and N. Stempniewicz and J. Tollitt and J. M. Valdivielso and
                      J. van der Leeuw and A. Y. M. Wang and C. P. Wen and M.
                      Woodward and K. Yamagishi and H. Yatsuya and L. Zhang and J.
                      A. Dorresteijn and E. Di Angelantonio and F. L. Visseren and
                      L. Pennells and J. Coresh},
      collaboration = {C. K. D. P. Consortium},
      title        = {{I}ncluding {M}easures of {C}hronic {K}idney {D}isease to
                      {I}mprove {C}ardiovascular {R}isk {P}rediction by {SCORE}2
                      and {SCORE}2-{OP}.},
      journal      = {European journal of preventive cardiology},
      volume       = {30},
      number       = {1},
      issn         = {2047-4873},
      address      = {London [u.a.]},
      publisher    = {Sage Publ.},
      reportid     = {DKFZ-2022-01878},
      pages        = {8-16},
      year         = {2023},
      note         = {2023 Jan 11;30(1):8-16},
      abstract     = {The 2021 ESC guideline on cardiovascular disease (CVD)
                      prevention categorizes moderate and severe chronic kidney
                      disease (CKD) as high and very-high CVD risk status
                      regardless of other factors like age and does not include
                      estimated glomerular filtration rate (eGFR) and albuminuria
                      in its algorithms, SCORE2 and SCORE2-OP, to predict CVD
                      risk. We developed and validated an 'Add-on' to incorporate
                      CKD measures into these algorithms, using a validated
                      approach.In 3,054,840 participants from 34 datasets, we
                      developed three Add-ons (eGFR only, eGFR + urinary
                      albumin-to-creatinine ratio [ACR] [the primary Add-on], and
                      eGFR + dipstick proteinuria) for SCORE2 and SCORE2-OP. We
                      validated c-statistics and net reclassification improvement
                      (NRI), accounting for competing risk of non-CVD death, in
                      5,997,719 participants from 34 different datasets.In the
                      target population of SCORE2 and SCORE2-OP without diabetes,
                      the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR)
                      improved c-statistic by 0.006 $(95\%CI$ 0.004-0.008) and
                      0.016 (0.010-0.023), respectively, for SCORE2 and 0.012
                      (0.009-0.015) and 0.024 (0.014-0.035), respectively, for
                      SCORE2-OP. Similar results were seen when we included
                      individuals with diabetes and tested the CKD Add-on (eGFR +
                      dipstick). In 57,485 European participants with CKD, SCORE2
                      or SCORE2-OP with a CKD Add-on showed a significant NRI
                      (e.g., 0.100 [0.062-0.138] for SCORE2) compared to the
                      qualitative approach in the ESC guideline.Our Add-ons with
                      CKD measures improved CVD risk prediction beyond SCORE2 and
                      SCORE2-OP. This approach will help clinicians and patients
                      with CKD refine risk prediction and further personalize
                      preventive therapies for CVD.},
      keywords     = {cardiovascular disease (Other) / chronic kidney disease
                      (Other) / meta-analysis (Other) / risk prediction (Other)},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35972749},
      doi          = {10.1093/eurjpc/zwac176},
      url          = {https://inrepo02.dkfz.de/record/181232},
}