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
N1 - Publisher Copyright:
© 2020 The Authors
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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U2 - 10.1016/j.eclinm.2020.100552
DO - 10.1016/j.eclinm.2020.100552
M3 - Article
AN - SCOPUS:85094590024
SN - 2589-5370
VL - 27
JO - EClinicalMedicine
JF - EClinicalMedicine
M1 - 100552
ER -