本文關鍵詞： 慢性腎臟病 健康管理隊列 Cox回歸 預測模型 弗羅明翰評分　出處：《山東大學》2017年碩士論文　論文類型：學位論文

【摘要】：研究背景慢性腎臟病(chronic kidney disease,CKD)是指任何原因引起的腎臟結構和功能障礙超過3個月,包括腎小球濾過率(glomerular filtration rate,GFR)正常和不正常的病理損傷、血液或尿液成分異常及影像學檢查異常,或不明原因GFR[mL/(minx1.73m2)]超過3個月。近年來,慢性腎臟病患病率持續(xù)上升,全球CKD平均患病率約為13.4%,已成為全球重要的公共衛(wèi)生問題。我國成人CKD患病率約為10.8%,現(xiàn)患病例近1.2億人,且隨著我國人口快速老齡化的趨勢,高血壓、糖尿病等患病率的增高,未來CKD患病人數(shù)勢必會繼續(xù)上升。然而,由于CKD在早期常無臨床癥狀,CKD患者早期知曉率低,而發(fā)展到后期則預后差,通常會并發(fā)多種嚴重疾病,例如高血壓、糖尿病、心血管疾病等,部分CKD患者可最終進展為終末期腎病(end-stage renal disease,ESRD),需要進行復雜且昂貴的腎臟替代治療,帶來嚴重的疾病負擔。因此,明確CKD的危險因素及其效應,通過建立數(shù)學模型將各種危險因素組合起來,綜合評估個體CKD的發(fā)病風險和預測發(fā)生概率,可以及早識別高風險個體并采取相應的健康管理措施,對早期預防、延緩甚至避免疾病發(fā)生具有重要意義。研究目的1、以"山東多中心健康管理縱向觀察隊列"為依托,采用多因素Cox比例風險回歸模型,分性別構建CKD風險評估模型,并對模型進行合理的驗證;2、將復雜的數(shù)學模型轉化為CKD評分系統(tǒng),為健康管理提供可直接應用于實踐的工具。資料與方法利用"山東多中心健康管理縱向觀察隊列",建立CKD隨訪隊列,采用多因素Cox比例風險回歸建立CKD預測模型并采用ROC曲線、AUC、靈敏度、特異度等指標對其預測效果進行合理的評價,采用十折交叉驗證法驗證其預測效果,最終,使用弗羅明翰評分法構建風險評分矩陣。研究結果1、隨訪過程中發(fā)現(xiàn),男性CKD的發(fā)病密度為30.96/1000人年,高于女性的13.92/1000人年,差異有統(tǒng)計學意義。2、在CKD發(fā)病組和CKD未發(fā)病組兩組之間,多數(shù)體檢指標存在統(tǒng)計學差異。使用單因素Cox回歸分析初步篩選,并結合臨床專業(yè)知識,確定男性預測模型的預測因子包括:年齡、體質量指數(shù)、對數(shù)轉換的γ-谷胺酰轉肽酶、血肌酐、甘油三酯、總膽固醇、血紅蛋白、白細胞計數(shù)、血清白蛋白、血清球蛋白、糖尿病、高血壓、腎囊腫、CVD;女性包括:年齡、體質量指數(shù)、對數(shù)轉換的7-谷胺酰轉肽酶、血肌酐、甘油三酯、總膽固醇、白細胞計數(shù)、血清白蛋白、血清球蛋白、血紅蛋白、糖尿病、高血壓、腎囊腫、CVD、睡眠狀況。3、將以上變量進行多因素Cox回歸分析,最終構建的預測模型包括的預測因子如下:男性:年齡、體質量指數(shù)、對數(shù)轉換的γ-谷胺酰轉肽酶、血肌酐、甘油三酯、白細胞計數(shù)、血清白蛋白、糖尿病、高血壓、CVD;女性:年齡、血肌酐、白細胞計數(shù)、甘油三酯、血清白蛋白、高血壓。4、使用受試者工作曲線(receiver operating characteristic curve,ROC)下面積AUC(area under curve)、靈敏度、特異度等指標評價上述模型的預測效果,男性CKD預測模型1-4年預測效果的AUC分別為0.669(95%可信區(qū)間(confidence interval,CI)為 0.661-0.676)、0.698(95%CI:0.690-0.707),0.687(95%CI:0.676-0.698),0.630(95%CI:0.615-0.644),靈敏度分別為 60.1%、55.9%、59.1%、58.6%,特異度分別為64.6%、77.0%、69.4%、63.6%;女性CKD預測模型的1-4年預測效果的 AUC 分別為 0.742(95%CI:0.732-0.752),0.793(95%CI:0.782-0.803),0.702(95%CI:0.687-0.717),0.621(95%CI:0.601-0.640),靈敏度分別為 61.1%、63.6%、57.7%、56.5%,特異度分別為 88.2%、89.6%、78.0%、62.9%。5、使用十折交叉驗證對模型預測效果及穩(wěn)定性進行驗證,結果顯示,經(jīng)十折交叉驗證,男性CKD預測模型1-4年預測效果的AUC分別為0.659(95%CI:0.651-0.666),0.692(95%CI:0.684-0.701),0.683(95%CI:0.672-0.694),0.620(95%CI:0.605-0.634);女性CKD預測模型1-4年預測效果的AUC分別為0.730(95%CI:0.719-0.740),0.789(95%CI:0.778-0.800),0.697C 95%CI:0.682-0.712),0.613 C95%CI:0.593-0.632)，，6、將預測模型轉換為弗羅明翰評分模型后,男性總分范圍為-2分至29分,-2分所對應的1-4年發(fā)病風險分別為0.08%,0.20%,0.33%,0.48%,29分所對應的1-4年發(fā)病風險分別為4.56%,10.78%,17.12%,23.74%;女性得分范圍為-3至20分,-3分所對應的1-4年發(fā)病風險分別為0.03%,0.08%,0.12%,0.22%,20分所對應的1-4年發(fā)病風險分別為3.13%,7.24%,11.43%,20.00%。研究結論1、CKD發(fā)病密度存在性別差異,男性高于女性;2、本研究基于健康體檢人群分性別構建了慢性腎臟病預測模型,男性模型預測因子為:年齡、體質量指數(shù)、對數(shù)轉換的γ-谷胺酰轉肽酶、血肌酐、甘油三酯、白細胞計數(shù)、血清白蛋白、糖尿病、高血壓、CVD,女性模型預測因子為:年齡、血肌酐、白細胞計數(shù)、甘油三酯、血清白蛋白、高血壓;3、模型用于預測1、2、3年的發(fā)病風險效果較好,且具有穩(wěn)健性;4、本研究將預測模型轉換為弗羅明翰風險評分,用于人群健康管理實踐。本研究探索了針對健康管理人群進行疾病風險評估和建立預測模型的方法,建立了 CKD預測模型,并應用弗羅明翰風險評分法將預測模型進行轉換,便于成果轉化和實際應用。但因受資料的限制,建模時未能包含所有與CKD相關的指標,且現(xiàn)有健康管理隊列可能存在一定的選擇性偏倚,隨訪時間也較短,因此模型的穩(wěn)定性及預測能力尚有待繼續(xù)觀察和進一步研究的驗證。
[Abstract]:On the background of chronic kidney disease (chronic kidney, disease, CKD) refers to the renal structure and dysfunction of any cause for more than 3 months, including glomerular filtration rate (glomerular, filtration rate, GFR) of normal and abnormal pathological damage, blood or urine and abnormal imaging abnormalities, GFR[mL/ or unexplained (minx1.73m2)] for more than 3 months. In recent years, the prevalence of chronic kidney disease continues to rise, the global average CKD prevalence rate is about 13.4%, has become an important public health problem in the world. The prevalence rate is about 10.8% of China's adult CKD, the case of nearly 120 million people, with China's rapid population aging trend. Increased prevalence of hypertension, diabetes and so on, the future CKD the number of patients will continue to rise. However, due to CKD in early CKD patients are usually asymptomatic, early low awareness, and the development of late prognosis, usually associated with A variety of serious diseases, such as hypertension, diabetes, cardiovascular disease, CKD patients may eventually progress to end-stage renal disease (end-stage renal, disease, ESRD), the need for complex and expensive renal replacement therapy, brings the serious burden of disease. Therefore, the risk factors of CKD and its effect, through the establishment of mathematical models of various risk the risk factors in combination, and comprehensive evaluation of individual CKD prediction probability, early identification of high risk individuals and take corresponding measures for health management, early prevention, delay or even avoid is important diseases. Objective: 1, to "Shandong health management center longitudinal observational cohort" based on the multi factors Cox proportional hazards regression model, CKD risk assessment model of gender construction, and make reasonable verification of the model; 2, the complex mathematical model into CKD score The system can be directly applied to practice, to provide tools for health management. Materials and methods using the "Shandong Center for health management, establish the CKD longitudinal study cohort follow-up cohort, regression to establish CKD prediction model and the ROC curve, using multivariate Cox proportional hazard AUC, sensitivity, specificity and other indexes were used to evaluate the prediction results using ten fold cross validation method to verify the prediction effect, finally, using Freund mingsh scoring method to construct risk rating matrix. The results of the 1, was found during the follow-up, the incidence density of male CKD was 30.96/1000 person years, higher than the female 13.92/1000 years, there was significant difference in the incidence of CKD group.2 and CKD onset group between the two groups, there were significant differences in most physical indicators. Using single factor Cox regression analysis screening, combined with clinical expertise to identify predictors of male package prediction model Including: age, body mass index, log transformed gamma glutamyl transpeptidase, serum creatinine, triglyceride, total cholesterol, hemoglobin, white blood cell count, serum albumin, serum globulin, diabetes, hypertension, renal cyst, CVD; women included: age, body mass index, the log transformed 7- Valley enzyme, creatinine, triglyceride, total cholesterol, white blood cell count, serum albumin, serum globulin, hemoglobin, diabetes, hypertension, renal cyst, CVD, sleep.3, the variable Cox regression analysis, the following factors including prediction prediction model is constructed finally: male age:, body mass index, log transformed gamma glutamyl transpeptidase, serum creatinine, triglyceride, leukocyte count, serum albumin, diabetes, hypertension, CVD; female: age, serum creatinine, white blood cell count, serum albumin, triglyceride, hypertension,.4, use The receiver operating curve (receiver operating characteristic curve ROC AUC (area) and area under the under curve), the sensitivity, specificity and other indexes to evaluate the prediction effect of the model, the male CKD prediction model prediction effect of 1-4 years of AUC were 0.669 (95% confidence interval (confidence, interval, CI), 0.698 (0.661-0.676) 95%CI:0.690-0.707), 0.687 (95%CI:0.676-0.698), 0.630 (95%CI:0.615-0.644), the sensitivity was 60.1%, 55.9%, 59.1%, 58.6%, the specificity was 64.6%, respectively, 77%, 69.4%, 63.6%; female CKD prediction model of the prediction effect of 1-4 years of AUC were 0.742 (95%CI:0.732-0.752), 0.793 (95%CI:0.782-0.803), 0.702 (95%CI:0.687-0.717). 0.621 (95%CI:0.601-0.640), the sensitivity was 61.1%, 63.6%, 57.7%, 56.5%, the specificity was 88.2%, 89.6%, 78%, 62.9%.5, using ten fold cross validation test of predictive effect and stability of the model C, results show that by ten fold cross validation, male CKD prediction model prediction effect of 1-4 years of AUC were 0.659 (95%CI:0.651-0.666), 0.692 (95%CI:0.684-0.701), 0.683 (95%CI:0.672-0.694), 0.620 (95%CI:0.605-0.634); female CKD prediction model for 1-4 years the results of prediction AUC were 0.730 (95%CI:0.719-0.740), 0.789 (95%CI:0.778-0.800 0.697C), 95%CI:0.682-0.712), 0.613 C95%CI:0.593-0.632, 6), the prediction model is converted to galantine mingsh scoring model, male scores range from -2 to 29, -2 branch of the corresponding risk 1-4 years were 0.08%, 0.20%, 0.33%, 0.48%, 29 branch of the corresponding risk 1-4 years were 4.56%, 10.78%, 17.12%, 23.74%; female scores ranged from -3 to 20, -3 branch of the corresponding risk 1-4 years were 0.03%, 0.08%, 0.12%, 0.22%, 20 branch of the corresponding risk 1-4 years were 3.13%, 7.24%, 11.43%, 20.00%. The conclusion of the study 1, there are gender differences in the incidence of CKD density was higher in male than in female; 2, based on the healthy population gender construct prediction model of chronic kidney disease, male model predictive factors: age, body mass index, log transformed gamma glutamyl transpeptidase, serum creatinine, triglyceride, white blood cell count, serum albumin, diabetes, hypertension, CVD, female model predictive factors: age, serum creatinine, white blood cell count, serum albumin, triglyceride, hypertension; 3, risk prediction model for the effect of 1,2,3 is better, and has robustness; 4, the prediction model is converted to galantine mingsh risk score for the practice of population health management. This study explores the health management of disease risk assessment and prediction model method, we establish CKD prediction model and application of Freund mingsh risk score method forecast 妯″瀷榪涜杞崲,渚夸簬鎴愭灉杞寲鍜屽疄闄呭簲鐢

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