2024-03-29T12:53:57Z
https://tsukuba.repo.nii.ac.jp/oai
oai:tsukuba.repo.nii.ac.jp:00056691
2022-04-27T09:32:20Z
2780:340
3:62:5587:8296
Incorporating kidney disease measures into cardiovascular risk prediction : Development and validation in 9 million adults from 72 datasets
山岸, 良匡
ヤマギシ, カズマサ
YAMAGISHI, Kazumasa
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
Yatsuya, Hiroshi
Zhang, Luxia
Schaeffner, Elke
Coresh, Josef
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.
journal article
Elsevier
2020-10
application/pdf
EClinicalMedicine
27
100552
25895370
https://tsukuba.repo.nii.ac.jp/record/56691/files/ECM_27-100552.pdf
eng
33150324
10.1016/j.eclinm.2020.100552
© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)