title: 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.
abstract: BACKGROUND
  Whether estimated glomerular filtration rate (eGFR) calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Study equation (eGFRCKDEPI) improves risk prediction compared to that calculated using the Modification of Diet in Renal Disease (MDRD) study equation (eGFRMDRD) has not been examined in a prospective study in Japanese people.
  
  
  RESULTS
  Participants (n=24,560) were divided into 4 stages (1, ≥90; 2, 60-89 (reference); 3a, 45-59; 3b+ <45 ml·min(-1)·1.73 m(-2)) according to eGFRCKDEPI or eGFRMDRD. 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 eGFRCKDEPI vs. eGFRMDRD 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 eGFRCKDEPI than eGFRMDRD on univariate analysis. NRI analysis indicated that the use of eGFRCKDEPI instead of eGFRMDRD offered a significant improvement in reclassification of death risk.
authors: Department of Hygiene and Preventive Medicine, Iwate Medical University, Iwate, Japan. masakio@iwate-med.ac.jp
authors: Masaki
authors: M
authors: Ohsawa
authors: Kozo
authors: K
authors: Tanno
authors: Kazuyoshi
authors: K
authors: Itai
authors: Tanvir Chowdhury
authors: TC
authors: Turin
authors: Tomonori
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authors: Okamura
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authors: Ogawa
authors: Kuniaki
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authors: Ogasawara
authors: Tomoaki
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authors: Fujioka
authors: Toshiyuki
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authors: Onoda
authors: Yuki
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authors: Yoshida
authors: Shin-ichi
authors: S
authors: Omama
authors: Yasuhiro
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authors: Ishibashi
authors: Motoyuki
authors: M
authors: Nakamura
authors: Shinji
authors: S
authors: Makita
authors: Fumitaka
authors: F
authors: Tanaka
authors: Toru
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authors: Kuribayashi
authors: Tomiko
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authors: Koyama
authors: Kiyomi
authors: K
authors: Sakata
authors: Akira
authors: A
authors: Okayama
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000401:mortality; D015897:Comorbidity; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007564:Japan / Q000453:epidemiology; D007668:Kidney / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012372:ROC Curve; D051436:Renal Insufficiency, Chronic / Q000175:diagnosis* / Q000453:epidemiology* / Q000401:mortality / Q000503:physiopathology; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D013997:Time Factors