@article{oai:tsukuba.repo.nii.ac.jp:00056691,
 author = {山岸, 良匡 and YAMAGISHI, Kazumasa and Matsushita, Kunihiro and Jassal, Simerjot K and Sang, Yingying and Ballew, Shoshana H and Grams, Morgan E and Surapaneni, Aditya and Arnlov, Johan and Bansal, Nisha and Bozic, Milica and Brenner, Hermann and Brunskill, Nigel J and Chang, Alex R and Chinnadurai, Rajkumar and Cirillo, Massimo and Correa, Adolfo and Ebert, Natalie and Eckardt, Kai-Uwe and Gansevoort, Ron T and Gutierrez, Orlando and Hadaegh, Farzad and He, Jiang and Hwang, Shih-Jen and Jafar, Tazeen H and Kayama, Takamasa and Kovesdy, Csaba P and Landman, Gijs W and Levey, Andrew S and Lloyd-Jones, Donald M and Major, Rupert W. and Miura, Katsuyuki and Muntner, Paul and Nadkarni, Girish N and Naimark, David MJ and Nowak, Christoph and Ohkubo, Takayoshi and Pena, Michelle J and Polkinghorne, Kevan R and Sabanayagam, Charumathi and Sairenchi, Toshimi and Schneider, Markus P and Shalev, Varda and Shlipak, Michael and Solbu, Marit D and Stempniewicz, Nikita and Tollitt, James and Valdivielso, José M and van der Leeuw, Joep and Wang, Angela Yee-Moon and Wen, Chi-Pang and Woodward, Mark and Yatsuya, Hiroshi and Zhang, Luxia and Schaeffner, Elke and Coresh, Josef},
 journal = {EClinicalMedicine},
 month = {Oct},
 note = {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.},
 title = {Incorporating kidney disease measures into cardiovascular risk prediction : Development and validation in 9 million adults from 72 datasets},
 volume = {27},
 year = {2020},
 yomi = {ヤマギシ, カズマサ}
}