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

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 DM, Nowak C, Ohkubo T, Pena MJ, Polkinghorne KR, Sabanayagam C, Sairenchi T, Schneider M, 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)

Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 27

Article Number: 100552

DOI: 10.1016/j.eclinm.2020.100552

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.

Authors with CRIS profile

Involved external institutions

University of California, San Diego United States (USA) (US) Duke-NUS Medical School / 杜克—国大医学研究生院 Singapore (SG) University of Tsukuba / 筑波大学 Japan (JP) Fujita Health University Japan (JP) University of California San Francisco (UCSF) United States (USA) (US) Maccabi Institute for Health Services Research Israel (IL) China Medical University Hospital Taiwan (TW) Charité - Universitätsmedizin Berlin Germany (DE) University Medical Centre Utrecht (UMC Utrecht) Netherlands (NL) Salford Royal NHS Foundation Trust United Kingdom (GB) Peking University First Hospital / 北大国际医院 China (CN) Icahn School of Medicine at Mount Sinai United States (USA) (US) Shiga University of Medical Science Japan (JP) Shahid Beheshti University of Medical Sciences Iran, Islamic Republic of (IR) Johns Hopkins University (JHU) United States (USA) (US) Ruprecht-Karls-Universität Heidelberg Germany (DE) Yamagata University (YU) Japan (JP) University of Groningen / Rijksuniversiteit Groningen Netherlands (NL) Teikyo University Japan (JP) University of Washington United States (USA) (US) Tufts Medical Center United States (USA) (US) Geisinger Medical Center United States (USA) (US) The University of Tennessee Health Science Center United States (USA) (US) Monash University Australia (AU) Leicester General Hospital United Kingdom (GB) National University of Singapore (NUS) Singapore (SG) Institut de Recerca Biomèdica de Lleida (IRBLleida) Spain (ES) University of Alabama at Birmingham (UAB) United States (USA) (US) Northwestern University United States (USA) (US) Dokkyo Medical University Japan (JP) Tulane University United States (USA) (US) University Hospital of North Norway / Universitetssykehuset Nord-Norge (UNN) Norway (NO) University of Toronto Canada (CA) Karolinska Institute Sweden (SE) University of Hong Kong (HKU) / 香港大學 Hong Kong (HK) Gelre ziekenhuizen Apeldoorn Netherlands (NL) Università degli Studi di Napoli Federico II Italy (IT) American Medical Group Association (AMGA) United States (USA) (US) University of Mississippi Medical Center United States (USA) (US)

How to cite

APA:

Matsushita, K., Jassal, S.K., Sang, Y., Ballew, S.H., Grams, M.E., Surapaneni, A.,... Coresh, J. (2020). Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets. EClinicalMedicine, 27. https://doi.org/10.1016/j.eclinm.2020.100552

MLA:

Matsushita, Kunihiro, et al. "Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets." EClinicalMedicine 27 (2020).

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