Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Kunihiro Matsushita; Stephen Kaptoge; Steven H.J. Hageman; Yingying Sang; Shoshana H. Ballew; Morgan E. Grams; Aditya Surapaneni; Luanluan Sun; Johan Arnlov; Milica Bozic; Hermann Brenner; Nigel J. Brunskill; Alex R. Chang; Rajkumar Chinnadurai; Massimo Cirillo; Adolfo Correa; Natalie Ebert; Kai Uwe Eckardt; Ron T. Gansevoort; Orlando Gutierrez; Farzad Hadaegh; Jiang He; Shih Jen Hwang; Tazeen H. Jafar; Simerjot K. Jassal; Takamasa Kayama; Csaba P. Kovesdy; Gijs W. Landman; Andrew S. Levey; Donald M. Lloyd-Jones; Rupert W. Major; Katsuyuki Miura; Paul Muntner; Girish N. Nadkarni; Christoph Nowak; Takayoshi Ohkubo; Michelle J. Pena; Kevan R. Polkinghorne; Toshimi Sairenchi; Elke Schaeffner; Markus P. Schneider; Varda Shalev; Michael G. Shlipak; Marit D. Solbu; Nikita Stempniewicz; James Tollitt; José M. Valdivielso; Joep Van Der Leeuw; Angela Yee Moon Wang; Chi Pang Wen; Mark Woodward; Kazumasa Yamagishi; Hiroshi Yatsuya; Luxia Zhang; Jannick A.N. Dorresteijn; Emanuele Di Angelantonio; Frank L.J. Visseren; Lisa Pennells; Josef Coresh

doi:10.1093/eurjpc/zwac176

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Kunihiro Matsushita, Stephen Kaptoge, Steven H.J. Hageman, Yingying Sang, Shoshana H. Ballew, Morgan E. Grams, Aditya Surapaneni, Luanluan Sun, Johan Arnlov, Milica Bozic, Hermann Brenner, Nigel J. Brunskill, Alex R. Chang, Rajkumar Chinnadurai, Massimo Cirillo, Adolfo Correa, Natalie Ebert, Kai Uwe Eckardt, Ron T. Gansevoort, Orlando GutierrezFarzad Hadaegh, Jiang He, Shih Jen Hwang, Tazeen H. Jafar, Simerjot K. Jassal, Takamasa Kayama, Csaba P. Kovesdy, Gijs W. Landman, Andrew S. Levey, Donald M. Lloyd-Jones, Rupert W. Major, Katsuyuki Miura, Paul Muntner, Girish N. Nadkarni, Christoph Nowak, Takayoshi Ohkubo, Michelle J. Pena, Kevan R. Polkinghorne, Toshimi Sairenchi, Elke Schaeffner, Markus P. Schneider, Varda Shalev, Michael G. Shlipak, Marit D. Solbu, Nikita Stempniewicz, James Tollitt, José M. Valdivielso, Joep Van Der Leeuw, Angela Yee Moon Wang, Chi Pang Wen, Mark Woodward, Kazumasa Yamagishi, Hiroshi Yatsuya, Luxia Zhang, Jannick A.N. Dorresteijn, Emanuele Di Angelantonio, Frank L.J. Visseren, Lisa Pennells, Josef Coresh^*

^*Corresponding author for this work

Preventive Medicine

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Aims: The 2021 European Society of Cardiology (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, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. Methods: 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. Results: 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. Conclusion: 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.

Original language	English (US)
Pages (from-to)	8-16
Number of pages	9
Journal	European journal of preventive cardiology
Volume	30
Issue number	1
DOIs	https://doi.org/10.1093/eurjpc/zwac176
State	Published - Jan 1 2023

Keywords

Cardiovascular disease
Chronic kidney disease
Meta-analysis
Risk prediction

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine
Epidemiology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/eurjpc/zwac176

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Matsushita, K., Kaptoge, S., Hageman, S. H. J., Sang, Y., Ballew, S. H., Grams, M. E., Surapaneni, A., Sun, L., Arnlov, J., Bozic, M., Brenner, H., Brunskill, N. J., Chang, A. R., Chinnadurai, R., Cirillo, M., Correa, A., Ebert, N., Eckardt, K. U., Gansevoort, R. T., ... Coresh, J. (2023). Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP. European journal of preventive cardiology, 30(1), 8-16. https://doi.org/10.1093/eurjpc/zwac176

@article{9128d1ecbe9b4fb08cce2d094a36201d,

title = "Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP",

abstract = "Aims: The 2021 European Society of Cardiology (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, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. Methods: 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. Results: 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. Conclusion: 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, Chronic kidney disease, Meta-analysis, Risk prediction",

author = "Kunihiro Matsushita and Stephen Kaptoge and Hageman, {Steven H.J.} and Yingying Sang and Ballew, {Shoshana H.} and Grams, {Morgan E.} and Aditya Surapaneni and Luanluan Sun and Johan Arnlov and Milica Bozic and Hermann Brenner and Brunskill, {Nigel J.} and Chang, {Alex R.} and Rajkumar Chinnadurai and Massimo Cirillo and Adolfo Correa and Natalie Ebert and Eckardt, {Kai Uwe} and Gansevoort, {Ron T.} and Orlando Gutierrez and Farzad Hadaegh and Jiang He and Hwang, {Shih Jen} and Jafar, {Tazeen H.} and Jassal, {Simerjot K.} and Takamasa Kayama and Kovesdy, {Csaba P.} and Landman, {Gijs W.} and Levey, {Andrew S.} and Lloyd-Jones, {Donald M.} and Major, {Rupert W.} and Katsuyuki Miura and Paul Muntner and Nadkarni, {Girish N.} and Christoph Nowak and Takayoshi Ohkubo and Pena, {Michelle J.} and Polkinghorne, {Kevan R.} and Toshimi Sairenchi and Elke Schaeffner and Schneider, {Markus P.} and Varda Shalev and Shlipak, {Michael G.} and Solbu, {Marit D.} and Nikita Stempniewicz and James Tollitt and Valdivielso, {Jos{\'e} M.} and {Van Der Leeuw}, Joep and Wang, {Angela Yee Moon} and Wen, {Chi Pang} and Mark Woodward and Kazumasa Yamagishi and Hiroshi Yatsuya and Luxia Zhang and Dorresteijn, {Jannick A.N.} and {Di Angelantonio}, Emanuele and Visseren, {Frank L.J.} and Lisa Pennells and Josef Coresh",

note = "Funding Information: The CKD Prognosis Consortium (CKD-PC) Data Coordinating Centre is funded in part by a programme grant from the US National Kidney Foundation (NKF funding sources include Boehringer Ingelheim) and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100446). A variety of sources have supported enrolment and data collection including laboratory measurements, and follow-up in the collaborating cohorts of the CKD-PC (see Supplementary material online, eAppendix S3). These funding sources include government agencies such as national institutes of health and medical research councils as well as foundations and industry sponsors. The funders of the study had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. In addition, the funders had no right to veto publication or to control the decision regarding to which journal the paper would be submitted. Publisher Copyright: {\textcopyright} 2022 The Author(s).",

year = "2023",

month = jan,

day = "1",

doi = "10.1093/eurjpc/zwac176",

language = "English (US)",

volume = "30",

pages = "8--16",

journal = "European journal of preventive cardiology",

issn = "2047-4873",

publisher = "SAGE Publications Ltd",

number = "1",

}

Matsushita, K, Kaptoge, S, Hageman, SHJ, Sang, Y, Ballew, SH, Grams, ME, Surapaneni, A, Sun, L, Arnlov, J, Bozic, M, Brenner, H, Brunskill, NJ, Chang, AR, Chinnadurai, R, Cirillo, M, Correa, A, Ebert, N, Eckardt, KU, Gansevoort, RT, Gutierrez, O, Hadaegh, F, He, J, Hwang, SJ, Jafar, TH, Jassal, SK, Kayama, T, Kovesdy, CP, Landman, GW, Levey, AS, Lloyd-Jones, DM, Major, RW, Miura, K, Muntner, P, Nadkarni, GN, Nowak, C, Ohkubo, T, Pena, MJ, Polkinghorne, KR, Sairenchi, T, Schaeffner, E, Schneider, MP, Shalev, V, Shlipak, MG, Solbu, MD, Stempniewicz, N, Tollitt, J, Valdivielso, JM, Van Der Leeuw, J, Wang, AYM, Wen, CP, Woodward, M, Yamagishi, K, Yatsuya, H, Zhang, L, Dorresteijn, JAN, Di Angelantonio, E, Visseren, FLJ, Pennells, L & Coresh, J 2023, 'Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP', European journal of preventive cardiology, vol. 30, no. 1, pp. 8-16. https://doi.org/10.1093/eurjpc/zwac176

TY - JOUR

T1 - Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

AU - Matsushita, Kunihiro

AU - Kaptoge, Stephen

AU - Hageman, Steven H.J.

AU - Sang, Yingying

AU - Ballew, Shoshana H.

AU - Grams, Morgan E.

AU - Surapaneni, Aditya

AU - Sun, Luanluan

AU - Arnlov, Johan

AU - Bozic, Milica

AU - Brenner, Hermann

AU - Brunskill, Nigel J.

AU - Chang, Alex R.

AU - Chinnadurai, Rajkumar

AU - Cirillo, Massimo

AU - Correa, Adolfo

AU - Ebert, Natalie

AU - Eckardt, Kai Uwe

AU - Gansevoort, Ron T.

AU - Gutierrez, Orlando

AU - Hadaegh, Farzad

AU - He, Jiang

AU - Hwang, Shih Jen

AU - Jafar, Tazeen H.

AU - Jassal, Simerjot K.

AU - Kayama, Takamasa

AU - Kovesdy, Csaba P.

AU - Landman, Gijs W.

AU - Levey, Andrew S.

AU - Lloyd-Jones, Donald M.

AU - Major, Rupert W.

AU - Miura, Katsuyuki

AU - Muntner, Paul

AU - Nadkarni, Girish N.

AU - Nowak, Christoph

AU - Ohkubo, Takayoshi

AU - Pena, Michelle J.

AU - Polkinghorne, Kevan R.

AU - Sairenchi, Toshimi

AU - Schaeffner, Elke

AU - Schneider, Markus P.

AU - Shalev, Varda

AU - Shlipak, Michael G.

AU - Solbu, Marit D.

AU - Stempniewicz, Nikita

AU - Tollitt, James

AU - Valdivielso, José M.

AU - Van Der Leeuw, Joep

AU - Wang, Angela Yee Moon

AU - Wen, Chi Pang

AU - Woodward, Mark

AU - Yamagishi, Kazumasa

AU - Yatsuya, Hiroshi

AU - Zhang, Luxia

AU - Dorresteijn, Jannick A.N.

AU - Di Angelantonio, Emanuele

AU - Visseren, Frank L.J.

AU - Pennells, Lisa

AU - Coresh, Josef

N1 - Funding Information: The CKD Prognosis Consortium (CKD-PC) Data Coordinating Centre is funded in part by a programme grant from the US National Kidney Foundation (NKF funding sources include Boehringer Ingelheim) and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100446). A variety of sources have supported enrolment and data collection including laboratory measurements, and follow-up in the collaborating cohorts of the CKD-PC (see Supplementary material online, eAppendix S3). These funding sources include government agencies such as national institutes of health and medical research councils as well as foundations and industry sponsors. The funders of the study had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. In addition, the funders had no right to veto publication or to control the decision regarding to which journal the paper would be submitted. Publisher Copyright: © 2022 The Author(s).

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Aims: The 2021 European Society of Cardiology (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, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. Methods: 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. Results: 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. Conclusion: 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.

AB - Aims: The 2021 European Society of Cardiology (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, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. Methods: 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. Results: 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. Conclusion: 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.

KW - Cardiovascular disease

KW - Chronic kidney disease

KW - Meta-analysis

KW - Risk prediction

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DO - 10.1093/eurjpc/zwac176

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VL - 30

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JF - European journal of preventive cardiology

IS - 1

ER -

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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