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 -

Circulation Journal

Volumn 77, Issue 5, 2013, Pages 1315-1325

  Ohsawa, Masaki ^a   Tanno, Kozo ^a   Itai, Kazuyoshi ^d   Turin, Tanvir Chowdhury ^f   Okamura, Tomonori ^e   Ogawa, Akira ^a   Ogasawara, Kuniaki ^a   Fujioka, Tomoaki ^a   Onoda, Toshiyuki ^a   Yoshida, Yuki ^a   Omama, Shin Ichi ^a   Ishibashi, Yasuhiro ^a   Nakamura, Motoyuki ^a   Makita, Shinji ^a   Tanaka, Fumitaka ^a   Kuribayashi, Toru ^b   Koyama, Tomiko ^c   Sakata, Kiyomi ^a   Okayama, Akira ^d  

^a IWATE MEDICAL UNIVERSITY   (Japan)

^b IWATE UNIVERSITY   (Japan)

^c Iwate Health Service Association   (Japan)

^d FUKUJUJI HOSPITAL   (Japan)

^e KEIO UNIVERSITY   (Japan)

^f UNIVERSITY OF CALGARY   (Canada)

Author keywords

Chronic kidney disease; CKD EPI equation; Estimated glomerular filtration rate; Mdrd equation; Model assessment

Indexed keywords

ACUTE HEART INFARCTION; ADULT; AGED; ARTICLE; CARDIOVASCULAR DISEASE; CARDIOVASCULAR MORTALITY; CARDIOVASCULAR RISK; CEREBROVASCULAR ACCIDENT; CHRONIC KIDNEY DISEASE; COHORT ANALYSIS; COLORIMETRY; COMPARATIVE STUDY; DEATH; DIET; ENZYMATIC ASSAY; FEMALE; GLOMERULUS FILTRATION RATE; HUMAN; JAPANESE; MAJOR CLINICAL STUDY; MALE; PREDICTION; PROSPECTIVE STUDY; QUESTIONNAIRE; RECEIVER OPERATING CHARACTERISTIC;

ADULT; AGED; AGED, 80 AND OVER; AREA UNDER CURVE; CARDIOVASCULAR DISEASES; COMORBIDITY; FEMALE; GLOMERULAR FILTRATION RATE; HUMANS; JAPAN; KIDNEY; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; MULTIVARIATE ANALYSIS; PREDICTIVE VALUE OF TESTS; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; PROSPECTIVE STUDIES; RENAL INSUFFICIENCY, CHRONIC; RISK ASSESSMENT; RISK FACTORS; ROC CURVE; SEVERITY OF ILLNESS INDEX; TIME FACTORS;

EID: 84876780373     PISSN: 13469843     EISSN: 13474820     Source Type: Journal    
DOI: 10.1253/circj.CJ-12-0982     Document Type: Article

References (40)

1. Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al. Kidney disease as a risk factor for development of cardiovascular disease: A statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 2003; 108: 2154-2169.
2. Ninomiya T, Kiyohara Y, Kubo M, Tanizaki Y, Doi Y, Okubo K, et al. Chronic kidney disease and cardiovascular disease in a general Japanese population: The Hisayama Study. Kidney Int 2005; 68: 228-236.
3. Irie F, Iso H, Sairenchi T, Fukasawa N, Yamagishi K, Ikehara S, et al. The relationships of proteinuria, serum creatinine, glomerular filtration rate with cardiovascular disease mortality in Japanese general population. Kidney Int 2006; 69: 1264-1271.
4. Nakamura K, Okamura T, Hayakawa T, Kadowaki T, Kita Y, Ohnishi H, et al. Chronic kidney disease is a risk factor for cardiovascular death in a community-based population in Japan: NIPPON DATA90. Circ J 2006; 70: 954-959.
5. Nakayama M, Metoki H, Terawaki H, Ohkubo T, Kikuya M, Sato T, et al. Kidney dysfunction as a risk factor for first symptomatic stroke events in a general Japanese population: The Ohasama study. Nephrol Dial Transplant 2007; 22: 1910-1915.
6. Ueshima K, Oba K, Yasuno S, Fujimoto A, Tanaka S, Ogihara T, et al. Influence of coronary risk factors on coronary events in Japanese high-risk hypertensive patients: Primary and secondary prevention of ischemic heart disease in a subanalysis of the Candesartan Antihypertensive Survival Evaluation in Japan (CASE-J) trial. Circ J 2011; 75: 2411-2416.
7. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification and stratification. Am J Kidney Dis 2002; 39(Suppl): S1-S266.
8. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006; 145: 247-254.
9. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604-612.
10. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009; 53: 982-992.
11. Horio M, Imai E, Yasuda Y, Watanabe T, Matsuo S. Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: Accuracy and use for population estimates. Am J Kidney Dis 2010; 56: 32-38.
12. Matsushita K, Selvin E, Bash LD, Astor BC, Coresh J. Risk implications of the new CKD Epidemiology Collaboration (CKD-EPI) equation compared with the MDRD Study equation for estimated GFR: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 2010; 55: 648-659.
13. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36.
14. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-387.
15. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: Results of a multiple ethnic groups investigation. JAMA 2001; 286: 180-187.
16. D'Agostino RB. Risk prediction and finding new independent prognostic factors. J Hypertens 2006; 24: 643-645.
17. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med 2006; 145: 21-29.
18. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115: 928-935.
19. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: The Reynolds Risk Score. JAMA 2007; 297: 611-619.
20. Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat Med 2008; 27: 157-172; discussion 207-212.
21. Ohsawa M, Itai K, Tanno K, Onoda T, Ogawa A, Nakamura M, et al. Cardiovascular risk factors in the Japanese northeastern rural population. Int J Cardiol 2009; 137: 226-235.
22. Makita S, Nakamura M, Satoh K, Tanaka F, Onoda T, Kawamura K, et al. Serum C-reactive protein levels can be used to predict future ischemic stroke and mortality in Japanese men from the general population. Atherosclerosis 2009; 204: 234-238.
23. Seino Y, Nanjo K, Tajima N, Kadowaki T, Kashiwagi A, Araki H, et al. Report of the committee on the classification and diagnostic criteria of diabetes mellitus. Diabetol Int 2010; 1: 2-20.
24. Nakamura M, Onoda T, Itai K, Ohsawa M, Satou K, Sakai T, et al. Association between serum C-reactive protein levels and microalbuminuria: A population-based cross-sectional study in northern Iwate, Japan. Intern Med 2004; 43: 919-925.
25. Schwab S, Christensen R, Dougherty K, Klahr S. Quantitation of proteinuria by the use of protein-to-creatinine ratios in single urine samples. Arch Intern Med 1987; 147: 943-944.
26. Jensen JS, Borch-Johnsen K, Feldt-Rasmussen B, Appleyard M, Jensen G. Urinary albumin excretion and history of acute myocardial infarction in a cross-sectional population study of 2,613 individuals. J Cardiovasc Risk 1997; 4: 121-125.
27. Omama S, Yoshida Y, Ogawa A, Onoda T, Okayama A. Differences in circadian variation of cerebral infarction, intracerebral haemorrhage and subarachnoid haemorrhage by situation at onset. J Neurol Neurosurg Psychiatry 2006; 77: 1345-1349.
28. Ogawa M, Tanaka F, Onoda T, Ohsawa M, Itai K, Sakai T, et al. A community based epidemiological and clinical study of hospitalization of patients with congestive heart failure in Northern Iwate, Japan. Circ J 2007; 71: 455-459.
29. Akaike H. Information theory and an extension of the maximum likelihood principle. Budapest: Akadimiai Kiado, 1973.
30. Schwarz G. Estimating the dimension of a model. Ann Statist 1978; 6: 461-464.
31. Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011; 30: 11-21.
32. Ohsawa M, Tanno K, Itai K, Turin TC, Okamura T, Ogawa A, et al. Concordance of CKD stages in estimation by the CKD-EPI equation and estimation by the MDRD equation in the Japanese general population: The Iwate KENCO Study. Int J Cardiol 2012 September 17, doi:10.1016/j.ijcard.2012.08.025 [Epub ahead of print].
33. Tonelli M, Klarenbach SW, Lloyd AM, James MT, Bello AK, Manns BJ, et al. Higher estimated glomerular filtration rates may be associated with increased risk of adverse outcomes, especially with concomitant proteinuria. Kidney Int 2011; 80: 1306-1314.
34. Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: A collaborative meta-analysis. Lancet 2010; 375: 2073-2081.
35. Mogensen CE. Twelve shifting paradigms in diabetic renal disease and hypertension. Diabetes Res Clin Pract 2008; 82(Suppl 1): S2-S9.
36. James MT, Quan H, Tonelli M, Manns BJ, Faris P, Laupland KB, et al. CKD and risk of hospitalization and death with pneumonia. Am J Kidney Dis 2009; 54: 24-32.
37. Hostetter TH, Olson JL, Rennke HG, Venkatachalam MA, Brenner BM. Hyperfiltration in remnant nephrons: A potentially adverse response to renal ablation. Am J Physiol 1981; 241: F85-F93.
38. Vedel P, Obel J, Nielsen FS, Bang LE, Svendsen TL, Pedersen OB, et al. Glomerular hyperfiltration in microalbuminuric NIDDM patients. Diabetologia 1996; 39: 1584-1589.
39. Chiarelli F, Verrotti A, Morgese G. Glomerular hyperfiltration increases the risk of developing microalbuminuria in diabetic children. Pediatr Nephrol 1995; 9: 154-158.
40. Kitamura A, Sato S, Kiyama M, Imano H, Iso H, Okada T, et al. Trends in the incidence of coronary heart disease and stroke and their risk factors in Japan, 1964 to 2003: The Akita-Osaka study. J Am Coll Cardiol 2008; 52: 71-79.