figshare
Browse
PIIS2589537020302960.pdf (527.53 kB)

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

Download (527.53 kB)
journal contribution
posted on 2020-11-25, 16:43 authored by K Matsushita, SK Jassal, Y Sang, SH Ballew, ME Grams, A Surapaneni, J Arnlov, N Bansal, M Bozic, H Brenner, NJ Brunskill, AR Chang, R Chinnadurai, M Cirillo, A Correa, N Ebert, KU Eckardt, RT Gansevoort, O Gutierrez, F Hadaegh, J He, SJ Hwang, TH Jafar, T Kayama, CP Kovesdy, GW Landman, AS Levey, DM Lloyd-Jones, RW Major, K Miura, P Muntner, GN Nadkarni, DM Naimark, C Nowak, T Ohkubo, MJ Pena, KR Polkinghorne, C Sabanayagam, T Sairenchi, MP Schneider, V Shalev, M Shlipak, MD Solbu, N Stempniewicz, J Tollitt, JM Valdivielso, J van der Leeuw, AYM Wang, CP Wen, M Woodward, K Yamagishi, H Yatsuya, L Zhang, E Schaeffner, J Coresh
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.

History

Citation

EClinicalMedicine, Volume 27, October 2020, 100552

Author affiliation

Department of Cardiovascular Sciences

Version

  • VoR (Version of Record)

Published in

EClinicalMedicine

Volume

27

Pagination

100552

Publisher

Elsevier BV

issn

2589-5370

eissn

2589-5370

Acceptance date

2020-09-04

Copyright date

2020

Available date

2020-10-14

Spatial coverage

England

Language

eng

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC