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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 流行病學與預防醫學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46077
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳秀熙
dc.contributor.authorJu-Yeh Yangen
dc.contributor.author楊如燁zh_TW
dc.date.accessioned2021-06-15T04:53:13Z-
dc.date.available2010-09-09
dc.date.copyright2010-09-09
dc.date.issued2010
dc.date.submitted2010-07-30
dc.identifier.citation1. Wen CP, Cheng TY, Tsai MK, et al. All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. Lancet 2008;371:2173-82.
2. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007;298:2038-47.
3. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002;39:S1-266.
4. Jones CA, McQuillan GM, Kusek JW, et al. Plasma creatinine levels in the US population: third National Health and Nutrition Examination Survey. Am J Kidney Dis 1998;32:992-9.
5. United States Renal Data System (USRDS). USRDS 1998 Annual Data Report. In: National Institutes of Health, National Institiute of Diabetes and Digestive and Kidney Disease, Bethesda, MD.; 1998.
6. Lee PH, Chang HY, Tung CW, et al. Hypertriglyceridemia: an independent risk factor of chronic kidney disease in Taiwanese adults. Am J Med Sci 2009;338:185-9.
7. Iseki K, Iseki C, Ikemiya Y, Fukiyama K. Risk of developing end-stage renal disease in a cohort of mass screening. Kidney Int 1996;49:800-5.
8. Yoshida T, Takei T, Shirota S, et al. Risk factors for progression in patients with early-stage chronic kidney disease in the Japanese population. Intern Med 2008;47:1859-64.
9. Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol 2009;20:1069-77.
10. Ruggenenti P, Perna A, Mosconi L, Pisoni R, Remuzzi G. Urinary protein excretion rate is the best independent predictor of ESRF in non-diabetic proteinuric chronic nephropathies. 'Gruppo Italiano di Studi Epidemiologici in Nefrologia' (GISEN). Kidney Int 1998;53:1209-16.
11. Levin A, Djurdjev O, Beaulieu M, Er L. Variability and risk factors for kidney disease progression and death following attainment of stage 4 CKD in a referred cohort. Am J Kidney Dis 2008;52:661-71.
12. Jones C, Roderick P, Harris S, Rogerson M. Decline in kidney function before and after nephrology referral and the effect on survival in moderate to advanced chronic kidney disease. Nephrol Dial Transplant 2006;21:2133-43.
13. Lorenzo V, Saracho R, Zamora J, Rufino M, Torres A. Similar renal decline in diabetic and non-diabetic patients with comparable levels of albuminuria. Nephrol Dial Transplant 2010;25:835-41.
14. Schumock GT, Andress DL, Marx SE, Sterz R, Joyce AT, Kalantar-Zadeh K. Association of secondary hyperparathyroidism with CKD progression, health care costs and survival in diabetic predialysis CKD patients. Nephron Clin Pract 2009;113:c54-61.
15. Othman M, Kawar B, El Nahas AM. Influence of obesity on progression of non-diabetic chronic kidney disease: a retrospective cohort study. Nephron Clin Pract 2009;113:c16-23.
16. Bomback AS, Katz R, He K, Shoham DA, Burke GL, Klemmer PJ. Sugar-sweetened beverage consumption and the progression of chronic kidney disease in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2009;90:1172-8.
17. Chiu YL, Chien KL, Lin SL, Chen YM, Tsai TJ, Wu KD. Outcomes of stage 3-5 chronic kidney disease before end-stage renal disease at a single center in Taiwan. Nephron Clin Pract 2008;109:c109-18.
18. Tsai SY, Tseng HF, Tan HF, Chien YS, Chang CC. End-stage renal disease in Taiwan: a case-control study. J Epidemiol 2009;19:169-76.
19. Keane WF, Zhang Z, Lyle PA, et al. Risk scores for predicting outcomes in patients with type 2 diabetes and nephropathy: the RENAAL study. Clin J Am Soc Nephrol 2006;1:761-7.
20. Johnson ES, Thorp ML, Yang X, Charansonney OL, Smith DH. Predicting renal replacement therapy and mortality in CKD. Am J Kidney Dis 2007;50:559-65.
21. Johnson ES, Thorp ML, Platt RW, Smith DH. Predicting the risk of dialysis and transplant among patients with CKD: a retrospective cohort study. Am J Kidney Dis 2008;52:653-60.
22. Keith DS, Nichols GA, Gullion CM, Brown JB, Smith DH. Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization. Arch Intern Med 2004;164:659-63.
23. Moranne O, Froissart M, Rossert J, et al. Timing of onset of CKD-related metabolic complications. J Am Soc Nephrol 2009;20:164-71.
24. Taal MW, Brenner BM. Predicting initiation and progression of chronic kidney disease: Developing renal risk scores. Kidney Int 2006;70:1694-705.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46077-
dc.description.abstractAlthough there are numerous studies addressing the clinical parameters governing the evolution of chronic kidney disease (CKD) into end-stage renal disease (ESRD), the results are inconsistent. We examined the relationship between clinical characteristics and chronic kidney disease progression with different perspectives using a cohort in Taiwan.
We enrolled 801 patients with chronic kidney disease stage 3 to 5 in one single medical center. We examined the different characteristics between CKD stages with application of cumulative and multinomial logit models. For longitudinal followed data, generalized estimating equation and generalized linear mixed model were adopted. We investigated the associations between covariates and time to ESRD by Cox regression models and checked the interaction between these covariates and CKD stages. We also applied time dependent Cox regression model for longitudinal followed data.
We found changing relationships between certain covariates and CKD stages in different models. Most biomarkers showed consistently progressive nature with advance of CKD stages, while some differences exist only between certain stages. There was significantly higher hazard for ESRD among those patients with more advanced CKD stages. Plasma levels of albumin, calcium, phosphorous, hematocrit and amount of proteinuria are independent and consistent risk factors of ESRD in multivariate regression model. However, there were significant interactions between several factors and CKD stages. Most of the associations tend to be stronger in CKD 3-4 and weaker or even lack of significance in CKD stage 5. The estimates of hazard ratios for ESRD differed between time-independent and time-dependent models.
This study provides solid evidence from domestic data about clinically pertinent risk factors in progression of CKD and clinical significance concerning CKD staging. These may form a basis for early intervention of modifiable factors to delay the progression of CKD.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T04:53:13Z (GMT). No. of bitstreams: 1
ntu-99-P97842004-1.pdf: 632666 bytes, checksum: b46b9fda48fa8ae6406cdcdc3a8d7272 (MD5)
Previous issue date: 2010
en
dc.description.tableofcontents第一章 背景……………………………………………………………………………1
第二章 文獻回顧………………………………………………………………………4
2.1 慢性腎病的定義與分期……………………………………………………4
2.2 慢性腎病盛行率……………………………………………………………4
2.3影響慢性腎病進展的因子……………………………………………………5
2.3.1 影響早期慢性腎病患者或一般族群腎病進展的因子………………5
2.3.2 影響晚期慢性腎病患者腎病進展的因子……………………………5
2.3.3 末期腎衰竭的預測模式………………………………………………7
2.3.4 總結影響慢性腎病的因子 …………………………………………8
2.4慢性腎病的併發症與預後……………………………………………………8
第三章 研究方法……………………………………………………………………10
3.1研究族群與設計……………………………………………………………10
3.2變項與預後測量……………………………………………………………10
3.3 統計分析……………………………………………………………………11
3.3.1統計分析架構………………………………………………………11
3.3.2 多項式回歸模式……………………………………………………12
3.3.3 廣義估計式…………………………………………………………13
3.3.4 廣義線性混合模式…………………………………………………13
3.3.5 Cox比例風險回歸模式……………………………………………14
3.3.6 檢測比例風險假設…………………………………………………15
3.3.7 交互作用……………………………………………………………16
3.3.8 時間相依 Cox 模式………………………………………………17
第四章 結果…………………………………………………………………………19
4.1 描述性分析…………………………………………………………………19
4.2慢性腎病分期之間不同的特徵 ……………………………………………19
4.2.1多項式邏吉司模式…………………………………………………19
4.2.2累積邏吉司模式……………………………………………………20
4.2.3 廣義估計式…………………………………………………………20
4.2.4 廣義線性混合模式…………………………………………………21
4.3 透析前存活分析 …………………………………………………………21
4.3.1 檢測比例風險假設 ………………………………………………21
4.3.2 慢性腎病分期與末期腎衰竭時間…………………………………21
4.3.3 危險因子與末期腎衰竭時間………………………………………21
4.3.4共變量與慢性腎病分期之間的交互作用…………………………22
4.3.5校正慢性腎病分期後共變量與末期腎衰竭時間之間關係…………23
4.3.6 時間相依Cox模式…………………………………………………23
第五章 討論…………………………………………………………………………24
5.1 主要結果……………………………………………………………………24
5.2慢性腎病分期之間不同的特徵……………………………………………25
5.2.1 實驗室生物指標……………………………………………………25
5.2.2 心理暨社會因子……………………………………………………25
5.3 透析前存活分析……………………………………………………………26
5.3.1 糖尿病與末期腎衰竭風險…………………………………………26
5.3.2 共變量與慢性腎病分期之間的交互作用…………………………26
5.3.3 時間相依 Cox 模式………………………………………………27
5.4 研究限制……………………………………………………………………28
5.5 結論…………………………………………………………………………29
參考資料………………………………………………………………………………30
dc.language.isozh-TW
dc.title影響慢性腎病進展之相關因子探討zh_TW
dc.titleFactors affecting progression of chronic kidney diseaseen
dc.typeThesis
dc.date.schoolyear98-2
dc.description.degree碩士
dc.contributor.oralexamcommittee戴政,陳立昇,劉宏輝,張淑惠
dc.subject.keyword慢性腎病,末期腎病變,多項式邏吉司模型,交互作用,重複測量,時間相依模式,zh_TW
dc.subject.keywordchronic kidney disease,end-stage renal disease,polynomial logit model,interaction,repeated measurement,time-dependent model,en
dc.relation.page61
dc.rights.note有償授權
dc.date.accepted2010-07-30
dc.contributor.author-college公共衛生學院zh_TW
dc.contributor.author-dept流行病學研究所zh_TW
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