Comparison of Predictability of Future Cardiovascular Events Between Chronic Kidney Disease (CKD) Stage Based on CKD Epidemiology Collaboration Equation and That Based on Modification of Diet in Renal Disease Equation in the Japanese General Population – Iwate KENCO Study –

Masaki Ohsawa; Kozo Tanno; Kazuyoshi Itai; Tanvir Chowdhury Turin; Tomonori Okamura; Akira Ogawa; Kuniaki Ogasawara; Tomoaki Fujioka; Toshiyuki Onoda; Yuki Yoshida; Shin-ichi Omama; Yasuhiro Ishibashi; Motoyuki Nakamura; Shinji Makita; Fumitaka Tanaka; Toru Kuribayashi; Tomiko Koyama; Kiyomi Sakata; Akira Okayama

doi:10.1253/circj.CJ-12-0982

Renal Disease

Comparison of Predictability of Future Cardiovascular Events Between Chronic Kidney Disease (CKD) Stage Based on CKD Epidemiology Collaboration Equation and That Based on Modification of Diet in Renal Disease Equation in the Japanese General Population

– Iwate KENCO Study –

Masaki Ohsawa, Kozo Tanno, Kazuyoshi Itai, Tanvir Chowdhury Turin, Tomonori Okamura, Akira Ogawa, Kuniaki Ogasawara, Tomoaki Fujioka, Toshiyuki Onoda, Yuki Yoshida, Shin-ichi Omama, Yasuhiro Ishibashi, Motoyuki Nakamura, Shinji Makita, Fumitaka Tanaka, Toru Kuribayashi, Tomiko Koyama, Kiyomi Sakata, Akira Okayama

Author information

Masaki Ohsawa
Department of Hygiene and Preventive Medicine, Iwate Medical University
Kozo Tanno
Department of Hygiene and Preventive Medicine, Iwate Medical University
Kazuyoshi Itai
The First Institute of Health Service, Japan Anti-Tuberculosis Association
Tanvir Chowdhury Turin
Department of Medicine, University of Calgary
Tomonori Okamura
Department of Preventive Medicine and Public Health, Keio University
Akira Ogawa
Department of Neurosurgery, Iwate Medical University
Kuniaki Ogasawara
Department of Neurosurgery, Iwate Medical University
Tomoaki Fujioka
Department of Urology, Iwate Medical University
Toshiyuki Onoda
Department of Hygiene and Preventive Medicine, Iwate Medical University
Yuki Yoshida
Department of Neurosurgery, Iwate Medical University
Shin-ichi Omama
Department of Neurosurgery, Iwate Medical University
Yasuhiro Ishibashi
Department of Internal Medicine, Iwate Medical University
Motoyuki Nakamura
Department of Internal Medicine, Iwate Medical University
Shinji Makita
Department of Internal Medicine, Iwate Medical University
Fumitaka Tanaka
Department of Internal Medicine, Iwate Medical University
Toru Kuribayashi
Department of Health and Physical Education, Faculty of Education, Iwate University
Tomiko Koyama
Iwate Health Service Association
Kiyomi Sakata
Department of Hygiene and Preventive Medicine, Iwate Medical University
Akira Okayama
The First Institute of Health Service, Japan Anti-Tuberculosis Association

Keywords: Chronic kidney disease, CKD-EPI equation, Estimated glomerular filtration rate, MDRD equation, Model assessment

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Supplementary material

2013 Volume 77 Issue 5 Pages 1315-1325

DOI https://doi.org/10.1253/circj.CJ-12-0982

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Abstract

Background: Whether estimated glomerular filtration rate (eGFR) calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Study equation (eGFR_CKDEPI) improves risk prediction compared to that calculated using the Modification of Diet in Renal Disease (MDRD) study equation (eGFR_MDRD) has not been examined in a prospective study in Japanese people. Methods and Results: Participants (n=24,560) were divided into 4 stages (1, ≥90; 2, 60–89 (reference); 3a, 45–59; 3b+ <45ml·min^–1·1.73m^–2) according to eGFR_CKDEPI or eGFR_MDRD. Endpoints were all-cause death, myocardial infarction (MI) and stroke. Area under the receiver operating characteristic curves (95% confidence intervals) for predicting all-cause death, MI and stroke by eGFR_CKDEPI vs. eGFR_MDRD were 0.680 (0.662–0.697) vs. 0.582 (0.562–0.602); 0.718 (0.665–0.771) vs. 0.642 (0.581–0.703); and 0.656 (0.636–0.676) vs. 0.576 (0.553–0.599), respectively. Multivariate-adjusted Cox regression and Poisson regression analysis results were similar for adjusted incidence rates and adjusted hazard ratios in each corresponding stage between the 2 models and no differences were found in model assessment parameters. Net reclassification improvement (NRI) for predicting all-cause death, MI and stroke were estimated to be 6.7% (P<0.001), –1.89% (P=0.029) and –0.20% (P=0.421), respectively. Conclusions: Better discrimination was achieved using eGFR_CKDEPI than eGFR_MDRD on univariate analysis. NRI analysis indicated that the use of eGFR_CKDEPI instead of eGFR_MDRD offered a significant improvement in reclassification of death risk. (Circ J 2013; 77: 1315–1325)

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