臺灣博碩士論文加值系統

研究背景：慢性腎臟病已被公認為全球公共衛生的問題，然而慢性腎臟病分期主要透過蛋白尿與腎絲球過濾率來定義，目前台灣臨床常用的腎絲球過濾率估算公式為simplified MDRD 公式，此一公式主要從西方國家所發展，因此不一定適合台灣。Taiwanese MDRD 與Taiwan-intercept MDRD 公式能夠更為準確的預測真實的腎絲球過濾率，然而目前沒有研究評估不同公式對臨床不良預後的預測性。因此本研究藉由比較在慢性腎臟病人不同公式所估算的腎絲球過濾率對不良預後（心血管住院、透析及死亡）風險的預測力。

研究方法：本研究利用南部某一醫學中心自91 年11 月到97 年5 月加入慢性腎臟病整合型照護計畫的病患，使用simplified MDRD 公式、CKD-EPI 公式、Taiwanese MDRD、Taiwan-intercept MDRD 公式估算病患開始收案時的腎絲球過濾率，追蹤至97 年底，觀察期間因心血管住院、進入透析及發生死亡的風險。Cox proportion hazard 模式用來校正與評估相關的風險。以適合度、區辨能力與重新分類能力來評估不同公式所估算的腎絲球過濾率對風險的預測性。統計以SPSS 19.0 進行分析，p<0.05 視為統計上的顯著差異。

研究結果：總共有2,181 名慢性腎臟病納入研究，排除照護小於90天的454 人，共有1,727 人納入追蹤，期間共有78 人因心血管疾病住院、510 人進入透析、140人發生死亡。整體而言，不論是CKD-EPI 公式、Taiwanese MDRD 或Taiwan-intercept MDRD 公式所估算的腎絲球過濾率在透析風險的預測力上皆顯著的低於simplified MDRD 公式所估算的腎絲球過濾率的預測力。但CKD-EPI公式所估算的腎絲球過濾率在死亡風險模式的重新分類能力顯著優於於simplified MDRD 公式所估算的腎絲球過濾率。但在大於65 歲、女性及糖尿病的患者上發現Taiwanese MDRD、Taiwan-intercept MDRD 公式所估算的腎絲球過濾率在透析的風險預測力上顯著優於simplified MDRD 公式所估算的腎絲球過濾率。

結論：本研究證實不論用哪一公式所估算的腎絲球過濾率對於不良的預後都有顯著預測力。Taiwanese MDRD 與Taiwan-intercept MDRD 公式相較於simplified MDRD 公式， 在大於65 歲、女性及糖尿病族群有較佳透析風險的預測力。本研究的發現，值得進一步的大型研究來證實。

Background: Chronic kidney disease (CKD) has been considered as a global public health problem in the world. The definition of CKD is based on criteria of proteinuria and level of glomerular filtration rate (GFR). Simplified MDRD equation for estimating GFR is most commonly used in clinics. It is not guaranteed that applying the equation to Taiwanese would not result in bias for GFR. Therefore, Taiwanese MDRD equation and Taiwan-intercept equation had been proved more accurate estimation for GFR. However, the performance of predicting risk through estimated GFR (eGFR) by using these equations was not well evaluated. The aim of the study is to compare the performance of models to predict adverse outcomes (cardiovascular disease hospitalization, dialysis and mortality) in different eGFR that were calculated by various equations.

Materials and methods: Patients, who had CKD and joined an integrated care program from Dec. 2002 through May. 2008 in one medical center, southern Taiwan, were included to this study. We used simplified MDRD equation, CKD-EPI equation, Taiwanese MDRD equation and Taiwan-intercept equation to estimate various GFR at baseline. We followed up occurrence of cardiovascular diseases hospitalization, dialysis, and mortality to 2008. Cox proportion hazard model was used to estimate the risk of adverse outcomes. We used the statistical value regarding goodness of fit, calibration, and reclassification of models to identify the performance of risk prediction. All statistical analyses were performed by SPSS 19.0 and p<0.005 is considered as statistically significant.

Results: Totally, 2,181 patients were included into the study. After excluding the patients joining the integrated care program less than 3 months (n=454), 1,727 patients were followed up regularly. In general, eGFR that were calculated by CKD-EPI equation, Taiwanese MDRD equation and Taiwan-intercept equation respectively provide significant worse risk perdition for dialysis than eGFR that was calculated by simplified MDRD equation. On the contrast, eGFR that was calculated by CKD-EPI equation has significant better reclassification of risk perdition for mortality than simplified MDRD equation. Furthermore, in elderly, female, and diabetes mellitus groups, the eGFR that were calculated by Taiwanese MDRD equation and Taiwan-intercept equation had significant better performance of risk predictions for dialysis than simplified MDRD equation.

Conclusion: Taiwanese MDRD equation and Taiwan-intercept equation provide a better risk predicting models for dialysis in old age, female, and diabetes mellitus groups. Our findings need more large-scale studies to further confirm.

中文摘要 ....................................................... I
Abstract ............................................... III
致 謝 ....................................................... V

第一章 緒論........................................................ 1
第一節 研究背景與動機........................................................ 1
第二節 研究目的........................................................ 3
第三節 研究重要性........................................................ 4

第二章 文獻探討 ....................................................... 5
第一節 腎功能的測定方式........................................................ 5
第二節 腎功能公式的發展........................................................ 6
第三節 腎功能對預後之影響........................................................ 9

第三章 材料與方法 ...................................................... 11
第一節 研究設計與資料來源....................................................... 11
第二節 研究變項....................................................... 11
第三節 資料處理與統計方法....................................................... 12

第四章 研究結果 ...................................................... 15
第一節 病人基本資料....................................................... 15
第二節 不同eGFR 公式間的相關性與對CKD 分期的影響....................................................... 15
第三節 不同eGFR 公式對CKD 分期一致性的影響因子....................................................... 16
第四節 不良預後（因心血管疾病住院、透析及死亡）的預測因子....................................................... 17
第五節 不同eGFR 公式對不良預後模式預測力的評估 ...................................................... 18
第六節 不同eGFR 公式對不同族群不良預後模式預測力的評估 ...................................................... 19

第五章 討論....................................................... 22
第一節 論文主要發現....................................................... 22
第二節 eGFR 與不良預後的關係 ...................................................... 23
第三節 不同公式對CKD 期別分類所造成的影響性 ...................................................... 24
第四節 不同eGFR 公式對不良預後風險的預測力 ...................................................... 25
第五節 研究限制....................................................... 26
第六節 結論....................................................... 26

參考文獻....................................................... 53

Cockcroft, D. W. and M. H. Gault (1976). "Prediction of creatinine clearance from serum creatinine." Nephron 16(1): 31‐41.

Cook, N. R. (2007). "Use and misuse of the receiver operating characteristic curve in risk prediction." Circulation 115(7): 928‐935.

Coresh, J., E. Selvin, et al. (2007). "Prevalence of chronic kidney disease in the United States." JAMA 298(17): 2038‐2047.

Deyo, R. A., D. C. Cherkin, et al. (1992). "Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases." J Clin Epidemiol 45 (6): 613‐619.

Frische, S. (2011). "Glomerular filtration rate in early diabetes: ongoing discussions of causes and mechanisms." J Nephrol 24(5): 537‐540.

Go, A. S., G. M. Chertow, et al. (2004). "Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization." N Engl J Med 351(13): 1296‐1305.

Hemmelgarn, B. R., B. J. Manns, et al. (2010). "Relation between kidney function, proteinuria, and adverse outcomes." JAMA 303(5): 423‐429.

Hoefield, R. A., P. A. Kalra, et al. (2011). "The use of eGFR and ACR to predict decline in renal function in people with diabetes." Nephrol Dial Transplant 26(3): 887‐892.

Holzmann, M. J., T. Ivert, et al. (2010). "Renal function assessed by two different formulas and incidence of myocardial infarction and death in middle‐aged men and women." J Intern Med 267(4): 357‐369.

Horio, M., E. Imai, et al. (2010). "Modification of the CKD epidemiology collaboration (CKD‐EPI) equation for Japanese: accuracy and use for population estimates." Am J Kidney Dis 56(1): 32‐38.

Hwang, S. J., M. Y. Lin, et al. (2008). "Increased risk of mortality in the elderly population with late‐stage chronic kidney disease: a cohort study in Taiwan." Nephrol Dial Transplant 23(10): 3192‐3198.

Hwang, S. J., J. C. Tsai, et al. (2010). "Epidemiology, impact and preventive care of chronic kidney disease in Taiwan." Nephrology (Carlton) 15 Suppl 2: 3‐9.

Hwang, S. J., W. C. Yang, et al. (2010). "Impact of the clinical conditions at dialysis initiation on mortality in incident haemodialysis patients: a national cohort study in Taiwan." Nephrol Dial Transplant 25(8): 2616‐2624.

Imai, E., M. Horio, et al. (2007). "Estimation of glomerular filtration rate by the MDRD study equation modified for Japanese patients with chronic kidney disease." Clin Exp Nephrol 11(1): 41‐50.

Imai, E., M. Horio, et al. (2007). "Modification of the Modification of Diet in Renal Disease (MDRD) Study equation for Japan." Am J Kidney Dis 50 (6): 927‐937.

James, M. T., B. R. Hemmelgarn, et al. (2010). "Glomerular filtration rate, proteinuria, and the incidence and consequences of acute kidney injury: a cohort study." Lancet 376(9758): 2096‐2103.

Kitiyakara, C., S. Yamwong, et al. (2012). "The impact of different GFR estimating equations on the prevalence of CKD and risk groups in a Southeast Asian cohort using the new KDIGO guidelines." BMC Nephrol 13: 1.

Lee, M., J. L. Saver, et al. (2010). "Low glomerular filtration rate and risk of stroke: meta‐analysis." BMJ 341: c4249.

Levey, A. S., J. P. Bosch, et al. (1999). "A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group." Ann Intern Med 130(6): 461‐470.

Levey, A. S., J. Coresh, et al. (2006). "Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate." Ann Intern Med 145(4): 247‐254.

Levey, A. S., P. E. de Jong, et al. (2011). "The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report." Kidney Int 80(1): 17‐28.

Levey, A. S., T. Greene, et al. (2000). "A simplified equation to predict glomerular filtration rate from serum creatinine." J Am Soc Nephrol 11: A0828.

Levey, A. S., L. A. Stevens, et al. (2009). "A new equation to estimate glomerular filtration rate." Ann Intern Med 150(9): 604‐612.

Lin, M. Y., S. J. Hwang, et al. (2010). "Impact of late-stage CKD and aging on medical utilization in the elderly population: a closed‐cohort study in Taiwan." Nephrol Dial Transplant 25(10): 3230‐3235.

Ma, Y. C., L. Zuo, et al. (2006). "Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease." J Am Soc Nephrol 17(10): 2937‐2944.

Matsuo, S., E. Imai, et al. (2009). "Revised equations for estimated GFR from serum creatinine in Japan." Am J Kidney Dis 53(6): 982‐992.

Matsushita, K., B. K. Mahmoodi, et al. (2012). "Comparison of risk prediction using the CKD‐EPI equation and the MDRD study equation for estimated glomerular filtration rate." JAMA 307(18): 1941‐1951.

Meguid El Nahas, A. and A. K. Bello (2005). "Chronic kidney disease: the global challenge." Lancet 365(9456): 331‐340.

Myers, G. L., W. G. Miller, et al. (2006). "Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program." Clin Chem 52(1): 5‐18.

Pugliese, G., A. Solini, et al. (2011). "The Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) equation provides a better definition of cardiovascular burden associated with CKD than the Modification of Diet in Renal Disease (MDRD) Study formula in subjects with type 2 diabetes." Atherosclerosis 218(1): 194‐199.

Rothenbacher, D., J. Klenk, et al. (2012). "Prevalence and determinants of chronic kidney disease in community‐dwelling elderly by various estimating equations." BMC Public Health 12(1): 343.

Rule, A. D. and B. W. Teo (2009). "GFR estimation in Japan and China: what accounts for the difference?" Am J Kidney Dis 53(6): 932‐935.

Schold, J. D., S. D. Navaneethan, et al. (2011). "Implications of the CKD‐EPI GFR estimation equation in clinical practice." Clin J Am Soc Nephrol 6(3): 497‐504.

Wen, C. P., T. Y. Cheng, et al. (2008). "All‐cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan." Lancet 371(9631): 2173‐2182.

Zuo, L., Y. C. Ma, et al. (2005). "Application of GFR-estimating equations in Chinese patients with chronic kidney disease." Am J Kidney Dis 45(3): 463‐472.