Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets

Kunihiro Matsushita; Simerjot K. Jassal; Yingying Sang; Shoshana H. Ballew; Morgan E. Grams; Aditya Surapaneni; Johan Arnlov; Nisha Bansal; 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; 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; David MJ Naimark; Christoph Nowak; Takayoshi Ohkubo; Michelle J. Pena; Kevan R. Polkinghorne; Charumathi Sabanayagam; Toshimi Sairenchi; Markus P. Schneider; Varda Shalev; Michael 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; Elke Schaeffner; Josef Coresh

doi:10.1016/j.eclinm.2020.100552

Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets

Kunihiro Matsushita, Simerjot K. Jassal, Yingying Sang, Shoshana H. Ballew^*, Morgan E. Grams, Aditya Surapaneni, Johan Arnlov, Nisha Bansal, 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 HadaeghJiang He, Shih Jen Hwang, Tazeen H. Jafar, 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, David MJ Naimark, Christoph Nowak, Takayoshi Ohkubo, Michelle J. Pena, Kevan R. Polkinghorne, Charumathi Sabanayagam, Toshimi Sairenchi, Markus P. Schneider, Varda Shalev, Michael 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, Elke Schaeffner, Josef Coresh

^*Corresponding author for this work

Preventive Medicine

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

71 Scopus citations

Abstract

Background: 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. 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. Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-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/1.73m² with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m² 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. Funding: US National Kidney Foundation and the NIDDK.

Original language	English (US)
Article number	100552
Journal	EClinicalMedicine
Volume	27
DOIs	https://doi.org/10.1016/j.eclinm.2020.100552
State	Published - Oct 2020

Funding

The CKD-PC Data Coordinating Center is funded in part by a program grant from the US National Kidney Foundation (NKF funding sources include Janssen and Boehringer Ingelheim) and the National Institute of Diabetes and Digestive and Kidney Diseases ( R01DK100446 ). A variety of sources have supported enrollment and data collection including laboratory measurements, and follow-up in the collaborating cohorts of the CKD-PC (eAppendix 3). 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. Dr. Matsushita reports grants from NIH during the conduct of the study; grants and personal fees from Kyowa Kirin and personal fees from Akebia outside the submitted work. Dr. Grams reports grants from NKF and grants from NIH during the conduct of the study and received travel funds to speak at DCI Director's meeting. Dr. Ärnlöv reports personal fees from AstraZeneca outside the submitted work. Dr. Ebert reports personal fees from Bayer AG, personal fees from Siemens Healthineers, and personal fees from Roche Diagnostics outside the submitted work. Dr. Eckardt reports grants from Astra Zeneca, grants from Bayer, grants from FMC, and grants from Vifor during the conduct of the study; personal fees from Akebia, personal fees from Astellas, personal fees from Bayer, and personal fees from Vifor outside the submitted work. Dr. Gutierrez reports grants and personal fees from Akebia, grants and personal fees from Amgen, grants from GSK, personal fees from QED, grants from National Institutes of Health, and grants from American Heart Association outside the submitted work. Dr. Kovesdy reports personal fees from Amgen, personal fees from Sanofi-Aventis, personal fees from Fresenius Medical Care, personal fees from Keryx, grants from Shire, personal fees from Bayer, personal fees from Abbott, personal fees from Abbvie, personal fees from Dr. Schar, personal fees from Astra-Zeneca, personal fees from Takeda, personal fees from Tricida, and personal fees from Reata outside the submitted work. Dr. Levey reports grants from NIDDK during the conduct of the study. Dr. Lloyd-Jones reports grants from NIH during the conduct of the study. Dr. Muntner reports grant support and consulting fees unrelated to this project. Dr. Nadkarni reports grants, personal fees, non-financial support and other from Renalytix AI, non-financial support and other from Pensieve Health, personal fees from Reata, personal fees from AstraZeneca, and personal fees from GLG Consulting outside the submitted work. Dr. Ohkubo reports grants from Omron Healthcare Co. Ltd. outside the submitted work. Dr. Shlipak reports consultancy fees from Cricket Health and Intercept Pharmaceuticals and stocks/stock options from TAI Diagnostics where he is a Scientific Advisor outside the submitted work. Dr. Woodward reports personal fees from Amgen and personal fees from Kirin outside the submitted work. Dr. Zhang reports grants from National Natural Science Foundation of China, grants from Beijing Nova Program Interdisciplinary Cooperation Project, grants from University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research, grants from PKU-Baidu Fund, grants from Peking University, and grants from AstraZeneca during the conduct of the study. Dr. Schaeffner reports other from Siemens Healthineers, other support from Fresenius Kabi and other support from Fresenius Medical Care outside the submitted work. Dr. Coresh reports grants from NIH and grants from National Kidney Foundation during the conduct of the study; personal fees and other support from Healthy.io outside the submitted work. All other coauthors have nothing to disclose.

Keywords

Chronic kidney disease
cardiovascular disease
meta-analysis
risk prediction

ASJC Scopus subject areas

General Medicine

UN SDGs

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

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10.1016/j.eclinm.2020.100552

Cite this

Matsushita, K., Jassal, S. K., Sang, Y., Ballew, S. H., Grams, M. E., Surapaneni, A., Arnlov, J., Bansal, N., Bozic, M., Brenner, H., Brunskill, N. J., Chang, A. R., Chinnadurai, R., Cirillo, M., Correa, A., Ebert, N., Eckardt, K. U., Gansevoort, R. T., Gutierrez, O., ... Coresh, J. (2020). Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets. EClinicalMedicine, 27, Article 100552. https://doi.org/10.1016/j.eclinm.2020.100552

@article{bb396d32ec6d4201834e1c08734cc5ce,

title = "Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets",

abstract = "Background: 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. 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. Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-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/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m2 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. Funding: US National Kidney Foundation and the NIDDK.",

keywords = "Chronic kidney disease, cardiovascular disease, meta-analysis, risk prediction",

author = "Kunihiro Matsushita and Jassal, {Simerjot K.} and Yingying Sang and Ballew, {Shoshana H.} and Grams, {Morgan E.} and Aditya Surapaneni and Johan Arnlov and Nisha Bansal 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 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 Naimark, {David MJ} and Christoph Nowak and Takayoshi Ohkubo and Pena, {Michelle J.} and Polkinghorne, {Kevan R.} and Charumathi Sabanayagam and Toshimi Sairenchi and Schneider, {Markus P.} and Varda Shalev and Michael Shlipak 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 Elke Schaeffner and Josef Coresh",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors",

year = "2020",

month = oct,

doi = "10.1016/j.eclinm.2020.100552",

language = "English (US)",

volume = "27",

journal = "EClinicalMedicine",

issn = "2589-5370",

publisher = "Elsevier Ltd",

}

Matsushita, K, Jassal, SK, Sang, Y, Ballew, SH, Grams, ME, Surapaneni, A, Arnlov, J, Bansal, N, 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, Kayama, T, Kovesdy, CP, Landman, GW, Levey, AS, Lloyd-Jones, DM, Major, RW, Miura, K, Muntner, P, Nadkarni, GN, Naimark, DMJ, Nowak, C, Ohkubo, T, Pena, MJ, Polkinghorne, KR, Sabanayagam, C, Sairenchi, T, Schneider, MP, Shalev, V, Shlipak, M, Solbu, MD, Stempniewicz, N, Tollitt, J, Valdivielso, JM, van der Leeuw, J, Wang, AYM, Wen, CP, Woodward, M, Yamagishi, K, Yatsuya, H, Zhang, L, Schaeffner, E & Coresh, J 2020, 'Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets', EClinicalMedicine, vol. 27, 100552. https://doi.org/10.1016/j.eclinm.2020.100552

TY - JOUR

T1 - Incorporating kidney disease measures into cardiovascular risk prediction

T2 - Development and validation in 9 million adults from 72 datasets

AU - Matsushita, Kunihiro

AU - Jassal, Simerjot K.

AU - Sang, Yingying

AU - Ballew, Shoshana H.

AU - Grams, Morgan E.

AU - Surapaneni, Aditya

AU - Arnlov, Johan

AU - Bansal, Nisha

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 - 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 - Naimark, David MJ

AU - Nowak, Christoph

AU - Ohkubo, Takayoshi

AU - Pena, Michelle J.

AU - Polkinghorne, Kevan R.

AU - Sabanayagam, Charumathi

AU - Sairenchi, Toshimi

AU - Schneider, Markus P.

AU - Shalev, Varda

AU - Shlipak, Michael

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 - Schaeffner, Elke

AU - Coresh, Josef

PY - 2020/10

Y1 - 2020/10

N2 - Background: 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. 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. Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-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/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m2 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. Funding: US National Kidney Foundation and the NIDDK.

AB - Background: 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. 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. Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-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/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m2 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. Funding: US National Kidney Foundation and the NIDDK.

KW - Chronic kidney disease

KW - cardiovascular disease

KW - meta-analysis

KW - risk prediction

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DO - 10.1016/j.eclinm.2020.100552

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SN - 2589-5370

VL - 27

JO - EClinicalMedicine

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M1 - 100552

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Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets

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UN SDGs

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