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

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...

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Autores principales: Matsushita, Kunihiro, Jassal, Simerjot K, Sang, Yingying, Ballew, Shoshana H, Grams, Morgan E, Surapaneni, Aditya, Arnlov, Johan, Bansal, Nisha, Bozic, Milica, Brenner, Hermann, Brunskill, Nigel J, Chang, Alex R, Chinnadurai, Rajkumar, Cirillo, Massimo, Correa, Adolfo, Ebert, Natalie, Eckardt, Kai-Uwe, Gansevoort, Ron T, Gutierrez, Orlando, Hadaegh, Farzad, He, Jiang, Hwang, Shih-Jen, Jafar, Tazeen H, Kayama, Takamasa, Kovesdy, Csaba P, Landman, Gijs W, Levey, Andrew S, Lloyd-Jones, Donald M, Major, Rupert W., Miura, Katsuyuki, Muntner, Paul, Nadkarni, Girish N, Naimark, David MJ, Nowak, Christoph, Ohkubo, Takayoshi, Pena, Michelle J, Polkinghorne, Kevan R, Sabanayagam, Charumathi, Sairenchi, Toshimi, Schneider, Markus P, Shalev, Varda, Shlipak, Michael, Solbu, Marit D, Stempniewicz, Nikita, Tollitt, James, Valdivielso, José M, van der Leeuw, Joep, Wang, Angela Yee-Moon, Wen, Chi-Pang, Woodward, Mark, Yamagishi, Kazumasa, Yatsuya, Hiroshi, Zhang, Luxia, Schaeffner, Elke, Coresh, Josef
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599294/
https://www.ncbi.nlm.nih.gov/pubmed/33150324
http://dx.doi.org/10.1016/j.eclinm.2020.100552
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author Matsushita, Kunihiro
Jassal, Simerjot K
Sang, Yingying
Ballew, Shoshana H
Grams, Morgan E
Surapaneni, Aditya
Arnlov, Johan
Bansal, Nisha
Bozic, Milica
Brenner, Hermann
Brunskill, Nigel J
Chang, Alex R
Chinnadurai, Rajkumar
Cirillo, Massimo
Correa, Adolfo
Ebert, Natalie
Eckardt, Kai-Uwe
Gansevoort, Ron T
Gutierrez, Orlando
Hadaegh, Farzad
He, Jiang
Hwang, Shih-Jen
Jafar, Tazeen H
Kayama, Takamasa
Kovesdy, Csaba P
Landman, Gijs W
Levey, Andrew S
Lloyd-Jones, Donald M
Major, Rupert W.
Miura, Katsuyuki
Muntner, Paul
Nadkarni, Girish N
Naimark, David MJ
Nowak, Christoph
Ohkubo, Takayoshi
Pena, Michelle J
Polkinghorne, Kevan R
Sabanayagam, Charumathi
Sairenchi, Toshimi
Schneider, Markus P
Shalev, Varda
Shlipak, Michael
Solbu, Marit D
Stempniewicz, Nikita
Tollitt, James
Valdivielso, José M
van der Leeuw, Joep
Wang, Angela Yee-Moon
Wen, Chi-Pang
Woodward, Mark
Yamagishi, Kazumasa
Yatsuya, Hiroshi
Zhang, Luxia
Schaeffner, Elke
Coresh, Josef
author_facet Matsushita, Kunihiro
Jassal, Simerjot K
Sang, Yingying
Ballew, Shoshana H
Grams, Morgan E
Surapaneni, Aditya
Arnlov, Johan
Bansal, Nisha
Bozic, Milica
Brenner, Hermann
Brunskill, Nigel J
Chang, Alex R
Chinnadurai, Rajkumar
Cirillo, Massimo
Correa, Adolfo
Ebert, Natalie
Eckardt, Kai-Uwe
Gansevoort, Ron T
Gutierrez, Orlando
Hadaegh, Farzad
He, Jiang
Hwang, Shih-Jen
Jafar, Tazeen H
Kayama, Takamasa
Kovesdy, Csaba P
Landman, Gijs W
Levey, Andrew S
Lloyd-Jones, Donald M
Major, Rupert W.
Miura, Katsuyuki
Muntner, Paul
Nadkarni, Girish N
Naimark, David MJ
Nowak, Christoph
Ohkubo, Takayoshi
Pena, Michelle J
Polkinghorne, Kevan R
Sabanayagam, Charumathi
Sairenchi, Toshimi
Schneider, Markus P
Shalev, Varda
Shlipak, Michael
Solbu, Marit D
Stempniewicz, Nikita
Tollitt, James
Valdivielso, José M
van der Leeuw, Joep
Wang, Angela Yee-Moon
Wen, Chi-Pang
Woodward, Mark
Yamagishi, Kazumasa
Yatsuya, Hiroshi
Zhang, Luxia
Schaeffner, Elke
Coresh, Josef
author_sort Matsushita, Kunihiro
collection PubMed
description 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/1.73m(2) with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m(2) with albuminuria 30–299 mg/g), and 1.37 (1.14–1.69) in moderate risk CKD (e.g., eGFR 60–89 ml/min/1.73m(2) 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.
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spelling pubmed-75992942020-11-03 Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets Matsushita, Kunihiro Jassal, Simerjot K Sang, Yingying Ballew, Shoshana H Grams, Morgan E Surapaneni, Aditya Arnlov, Johan Bansal, Nisha Bozic, Milica Brenner, Hermann Brunskill, Nigel J Chang, Alex R Chinnadurai, Rajkumar Cirillo, Massimo Correa, Adolfo Ebert, Natalie Eckardt, Kai-Uwe Gansevoort, Ron T Gutierrez, Orlando Hadaegh, Farzad He, Jiang Hwang, Shih-Jen Jafar, Tazeen H Kayama, Takamasa Kovesdy, Csaba P Landman, Gijs W Levey, Andrew S Lloyd-Jones, Donald M Major, Rupert W. Miura, Katsuyuki Muntner, Paul Nadkarni, Girish N Naimark, David MJ Nowak, Christoph Ohkubo, Takayoshi Pena, Michelle J Polkinghorne, Kevan R Sabanayagam, Charumathi Sairenchi, Toshimi Schneider, Markus P Shalev, Varda Shlipak, Michael Solbu, Marit D Stempniewicz, Nikita Tollitt, James Valdivielso, José M van der Leeuw, Joep Wang, Angela Yee-Moon Wen, Chi-Pang Woodward, Mark Yamagishi, Kazumasa Yatsuya, Hiroshi Zhang, Luxia Schaeffner, Elke Coresh, Josef EClinicalMedicine Research Paper 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/1.73m(2) with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m(2) with albuminuria 30–299 mg/g), and 1.37 (1.14–1.69) in moderate risk CKD (e.g., eGFR 60–89 ml/min/1.73m(2) 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. Elsevier 2020-10-14 /pmc/articles/PMC7599294/ /pubmed/33150324 http://dx.doi.org/10.1016/j.eclinm.2020.100552 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Matsushita, Kunihiro
Jassal, Simerjot K
Sang, Yingying
Ballew, Shoshana H
Grams, Morgan E
Surapaneni, Aditya
Arnlov, Johan
Bansal, Nisha
Bozic, Milica
Brenner, Hermann
Brunskill, Nigel J
Chang, Alex R
Chinnadurai, Rajkumar
Cirillo, Massimo
Correa, Adolfo
Ebert, Natalie
Eckardt, Kai-Uwe
Gansevoort, Ron T
Gutierrez, Orlando
Hadaegh, Farzad
He, Jiang
Hwang, Shih-Jen
Jafar, Tazeen H
Kayama, Takamasa
Kovesdy, Csaba P
Landman, Gijs W
Levey, Andrew S
Lloyd-Jones, Donald M
Major, Rupert W.
Miura, Katsuyuki
Muntner, Paul
Nadkarni, Girish N
Naimark, David MJ
Nowak, Christoph
Ohkubo, Takayoshi
Pena, Michelle J
Polkinghorne, Kevan R
Sabanayagam, Charumathi
Sairenchi, Toshimi
Schneider, Markus P
Shalev, Varda
Shlipak, Michael
Solbu, Marit D
Stempniewicz, Nikita
Tollitt, James
Valdivielso, José M
van der Leeuw, Joep
Wang, Angela Yee-Moon
Wen, Chi-Pang
Woodward, Mark
Yamagishi, Kazumasa
Yatsuya, Hiroshi
Zhang, Luxia
Schaeffner, Elke
Coresh, Josef
Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
title Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
title_full Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
title_fullStr Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
title_full_unstemmed Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
title_short Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets
title_sort incorporating kidney disease measures into cardiovascular risk prediction: development and validation in 9 million adults from 72 datasets
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599294/
https://www.ncbi.nlm.nih.gov/pubmed/33150324
http://dx.doi.org/10.1016/j.eclinm.2020.100552
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