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

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50 Citations (Scopus)


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 languageEnglish
Article number100552
Publication statusPublished - 10-2020

All Science Journal Classification (ASJC) codes

  • General Medicine


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