尿血管紧张素原（AGT）在急性失代偿性心力衰竭病人AKI中的预测作用和预后价值

发布时间：2018-02-24 06:45

  本文关键词： 急性失代偿心力衰竭 急性肾损伤 尿 血管紧张素原(AGT)　出处：《南方医科大学》2013年博士论文　论文类型：学位论文

【摘要】：研究背景和目的 急性失代偿性心力衰竭(ADHF)病人容易并发急性肾损伤(acute renal injury, AKI),即心肾综合征(Ⅰ型)。心衰病人并发肾损伤不但增加病情复杂性,还增加了病人死亡率,延长住院时间及增高医疗费用。新英格兰医学杂志2012年刊发的研究表明,发生心肾综合征的患者,其60天内再住院和死亡率超过33%。广东省器官衰竭防治重点实验室在省内心力衰竭病人群的研究资料显示,心肾综合征发生率达44%,死亡率达23-37%,超过30%病人肾功能障碍不能完全恢复。由此可见,对于这一高死亡率、高后遗症率、涉及心、肾两个重要脏器的危重疾病,临床医生迫切需要能早期诊断的可靠方法,通过及时干预,降低病人的致残率和死亡率。 目前临床上诊断AKI指标是血肌酐,但肾功能要持续损伤一大半时才会出现血肌酐升高,故血肌酐不是肾损伤的早期敏感指标。2005年权威医学杂志《柳叶刀》发表了通过检测尿明胶酶相关脂质运载蛋白(NGAL)预测心脏手术病人术后急性肾损伤的文章,证实了尿生物学标志物可以早期诊断肾功能损伤。此后,因尿液检测的无创性和简便性,研究尿生物学标志物预测疾病成为医学热点。NGAL目前已被公认为早期诊断肾损伤的生物学标志物,但单一的生物学标志物在临床上应用是不足够的,寻找其他潜在的生物学标志物,通过比较和联合预测,将进一步提高早期诊断疾病的水平。 目前心肾综合征发病机制尚不完全清楚,肾素-血管紧张素系统异常激活被公认为是重要机制之一。尿AGT在动物模型、人类慢性肾脏病等研究中被认为可以直接反应肾内RAS水平,是肾内RAS的标志物。I型心肾综合征中,肾脏局部RAS活性是否与AKI发生、发展相关值得深入研究 本研究目的是探明尿AGT能否作为急性失代偿性心力衰竭病人发生AKI的早期生物学标志物,并与目前公认的生物学标志物NGAL相比,分析其预测作用；其次探明尿AGT能否预示AKI进展和临床预后。 方法 1、研究设计：前瞻性,多中心,观察性研究,时间2010年2月～2012年12月。 2、研究人群：因急性失代偿性心力衰竭住南方医院和广东省人民医院的病人,诊断标准：2005年欧洲心脏病学会急性失代偿性心力衰竭诊断和治疗指南。 3、纳入排除标准：纳入标准：具有心衰的症状(呼吸困难)和体征(如肺部湿罗音、低血压、组织灌注不足；颈静脉压增高、外周性水肿、肝肿大、肠淤血等右心衰体征),X线胸片示肺淤血；排除标准：院前及入院后使用万古霉素、氨基糖甙抗生素或造影剂,慢性肾脏病(eGFR小于60ml/min或接受慢性肾脏透析治疗),尿路梗阻、恶性肿瘤和感染者,入院后接受心脏外科手术、造影术,肾动脉狭窄,多器官功能衰竭,入院时间不足24小时,年龄小于18岁,外院经治疗后转入我院。 4、研究时间：整个住院期间。 5、尿血管紧张素原(urine angiotensinogen, UAGT),尿中性粒细胞明胶酶相关脂质运载蛋白(urine neutrophil gelatinase-associated lipocalin, UNGAL)。入院1周内：每天留取晨尿标本10ml,低温离心(4℃,3000rpm*15min)后分装储于-80℃冰箱待检；血AGT,每两天留血1次,4ml (EDTA抗凝管),低温离心(4℃,3000rpm*15min)分离血浆后分装储于-80℃冰箱待检。入院1周后,每2-3天留取一次血、尿标本,方法同前。标本留取时间点见表1。 6、其他临床指标：人口学,既往病史,临床指标。 7、研究终点 a)主要终点：AKI; KDIGO2012标准(本文采用血肌酐标准)分级见表2KDIGO2012标准(满足一下任何一条即可诊断AKI)：ⅰ.48小时内血肌酐上升≥26.5umol/L (0.3mg/dl)ⅱ.7天内血肌酐升至1.5倍基线值以上ⅲ.尿量0.5ml/kg/h超过6小时 b)次要终点：出院时AKI不恢复(血肌酐未降至基线水平)；1年内死亡；1年内再住院；1年内进展至CKD (KDIGO2012标准) 统计学处理(二级标题) 1.分组：按病人住院期间是否发生AKI,分为AKI组(发生组,n=50)和no-AKI组(未发生组,n=95)。 2.两组基线资料包括人口学、病史及临床指标。分类变量(性别、并存病、病因及心衰分级)的组间比较采用Fisher精确检验或Pearson卡方检验；正态分布连续变量(年龄、收缩压、舒张压、血Na、血K、血肌酐、Hb)的组间比较采用independent-samples T Test;非正态分布连续变量(NT-pro-BNP、UPro/cr、UAlb/cr)的组间比较采用Mann-Whitney U Test。两组间比较见结果章节Table1。 3.两组间第1-7天尿AGT和尿NGAL水平比较采用Mann-Whitney U Test,见Table2和Table3;原始值对数10转换后作7天内趋势图,见Figure1, Figure2。两组间第1、3、5、7天血AGT水平比较采用independent-samples T Test,原始值作7天内趋势图,Table4, Figure3。 4.建立二分类Logistic回归模型分析AKI预测因子(predictor),因变量为AKI(1、0)。先单因素分析与AKI有关的自变量,选择P0.10和临床重要的自变量,纳入模型后进行多因素Logistic回归分析。自变量的选择原则：研究目的(预测AKI的发生,故选择住院第1天的尿AGT和NGAL);专业判断(临床已公认与AKI有关的预测因素,如age、DM、Hypertension、尿白蛋白、原发病严重程度等)。住院第1天尿AGT按不同高低水平分级后作为分类自变量进入Logistic回归模型。见结果章节Table5。 5.受试者工作特性曲线(ROC)分析：尿AGT, NGAL单独预测AKI的曲线下面积。多变量联合预测时建立Logistic回归模型计算预测概率值,以该预测值作为检验变量进行预测AKI的ROC分析。多变量预测AKI的模型有：1、临床模型,自变量包括年龄、性别、糖尿病、高血压、原发病严重程度和尿白蛋白肌酐比值；2、临床模型+尿NGAL,3、临床模型+尿NGAL+尿AGT,见Figure4、Figure5、Figure6。 6.尿指标(AGT. NGAL)+临床模型预测AKI与单纯临床模型预测AKI的R0C比较：见Table6 7.尿指标(AGT)预测严重的AKI (severe AKI):先比较severe AKI, AKI (stage1)和未发生AKI (no-AKI)三组的尿AGT水平(对数转换值的直方图),后计算尿AGT预测severe AKI的AUC和95%CI。见Figure7, Figure8。 8.按病人住院前有无规律使用RASI药物将研究病人群分为2个亚组：使用组和未使用组；比较亚组内发生AKI者和未发生者尿AGT水平(对数转换值的直方图),ROC分析：亚组内住院第1天尿AGT预测AKI的曲线下面积。见Figure10,Figure11 9.50例AKI病人,按出院时肾功能是否恢复(血肌酐降至基线水平)分布恢复组和未恢复组,比较两组的临床指标和AKI确诊第1天尿AGT水平,见Table7; ROC分析：尿AGT预测AKI不恢复的曲线下面积,见Figure9 10. Kaplan-meier法分析住院第1天高低尿AGT水平组(ROC分析中约登指数最大时的cutoff为界)入院1周内AKI累积发生率,1年内的累积死亡率和再住院率,Cox regression法计算高尿AGT水平组比较低尿AGT水平组的HR和95%CI。Figure12, Figure13, Figure14。 11.按发病1年内是否进入慢性肾脏病(CKD),将AKI病人分为进展至CKD组和未进展至CKD组,比较2组间临床指标和不同时间点的尿AGT水平(对数转换值),见Figure15, Table8, Table9; ROC分析：尿AGT水平和升幅预测AKI后进展至CKD曲线下面积,见Figure16。 结果 1.两个中心共收治ADHF病人181例,排除36例,纳入145例。 2.145例病人中,50例住院期间发生AKI,发生占比34.5%。 3.AKI发生时间点为入院第2-7天,中位数是第3天。 4.按是否发生AKI将病人分为AKI(n=50)和no-AKI(n=95)两组,基线资料见Table1。AKI组较no-AKI组年龄大,糖尿病和高血压发生率高,心衰程度重(NT-pro-BNP高)；AKI组尿蛋白和尿微量白蛋白水平显著高于no-AKI组;AKI组和no-AKI组在原发病、性别、基线血肌酐、既往心衰、心梗史、血压、左室射血分数、NYHA分级上均无统计学差别。 5.入院7天内AKI组和no-AKI组尿AGT/cr和NGAL/cr趋势图见Figure1、 Figure2,原始数据见Table2、Table3.图表上可见AKI组各天尿AGT/cr和NGAL/cr水平均显著高于no-AKI组,其中以第1-2天更为显著；AKI组和no-AKI组血AGT趋势图见Figure3,原始数据见Table4,两组间比较无统计学差别。 6.在校正其他临床预测因素后,住院第1天尿AGT能独立预测AKI发生,见Table5。 7.ROC分析显示：尿AGT能增加尿NGAL预测AKI的AUC,见Figure4、;尿AGT、NGAL联合临床模型显著提高单纯临床模型预测AKI的AUC和NRI,见Table6, Figure5, Figure6。 8.尿AGT水平能预测AKI的严重程度。尿AGT水平越高,AKI越严重,见Figure7、Figure8。 9.按既往有无使用RASI药物分亚组后,未使用RASI组和使用RASI组尿AGT均可独立预测AKI见Figure10、Figure11。 10.高尿AGT水平能独立预测AKI不恢复。不恢复组和恢复组临床指标和尿AGT水平见Table7、ROC分析见Figure9。 11.入院时高尿AGT水平预示1周内AKI发生,高尿AGT水平组发生AKI风险是低尿AGT水平组的4.3倍,见Figure12 12.入院时高尿AGT水平能预示1年内死亡和再住院,高尿AGT水平组1年内死亡风险是低尿水平组的20倍,1年内再住院风险是低尿AGT水平组的3倍,见Figure13、Figure14 13.高尿AGT水平和升幅能预测AKI后进展至CKD, AKI后进展至CKD与未进展者临床指标和各时间点尿AGT见Table8、Table9,趋势图和ROC分析见Figure15、Figure16 结论 尿AGT是急性失代偿性心力衰竭病人发生AKI的早期生物学标志物,联合尿NGAL和临床指标将进一步提高预测能力,高尿AGT水平预示ADHF病人AKI进展和临床不良预后。
[Abstract]:Background and purpose of research
Acute decompensated heart failure (ADHF) patients complicated with acute kidney injury (acute renal, injury, AKI), namely the cardiorenal syndrome (type I). Patients with acute renal injury of heart failure will not only increase the complexity of the disease, but also increased the patient mortality, prolonged hospitalization and increased medical expenses. The new England Journal of Medicine published in 2012 show that the occurrence of cardiorenal syndrome patients, the 60 days of rehospitalization and mortality is more than 33%. of Guangdong Province Key Laboratory of organ failure prevention study showed failure data of the patients with heart, force group, heart and kidney syndrome rate was 44%, the mortality rate of 23-37%, more than 30% of patients with renal dysfunction could not recover completely. Thus, for such a high mortality rate, high rate of sequelae, involving heart, kidney two important organs of the critically ill, clinicians urgently need a reliable method for early diagnosis, through timely intervention, reduce disease The rate of disability and mortality.
The current clinical diagnosis index of AKI blood creatinine, but renal function lasts more than half damage occurs when serum creatinine increased, so the early sensitive index.2005 authoritative medical journal "Lancet" creatinine renal injury is not published by detecting urine gelatinase associated protein lipid carrier (NGAL) prediction of acute kidney injury in cardiac surgery the patient after the article, confirmed urine biomarkers can diagnose early renal function injury. Since then, noninvasive and convenient for detection of urine, urine of biological markers to predict disease become medical hot.NGAL has now been recognized as a biological marker for early diagnosis of renal damage, but a single biomarker application it is not enough in the clinic, looking for other potential biomarkers, and by comparing the joint prediction, will further improve the early diagnosis level of the disease.
The cardiorenal syndrome pathogenesis is not completely clear, renin angiotensin system activation is recognized as one of the important mechanisms. Urinary AGT in animal models of human chronic kidney disease is thought to react directly the level of renal RAS,.I is a marker of renal RAS in the heart and kidney syndrome whether in local kidney RAS activity and AKI occurrence, development is worthy of further study
The purpose of this study is to find out whether urine AGT can be used as an early biomarker for AKI in patients with acute decompensated heart failure, and analyze its predictive role compared with the currently recognized biomarker NGAL. Secondly, we need to find out whether urine AGT can predict AKI progression and clinical prognosis.
Method
1, research design: prospective, multicenter, observational studies, from February 2010 to December 2012.
2, the research population: the diagnosis of acute decompensated heart failure due to acute decompensated heart failure in the southern hospital and Guangdong General Hospital. Diagnostic criteria: 2005 European Heart Association acute decompensated heart failure diagnosis and treatment guidelines.
3, the inclusion and exclusion criteria: with heart failure symptoms (dyspnea) and symptoms (such as pulmonary rales, hypotension, inadequate tissue perfusion; jugular venous pressure, peripheral edema, hepatomegaly, intestinal congestion and other symptoms of right heart failure), chest X-ray showed pulmonary congestion; exclusion criteria: the use of vancomycin in pre hospital and after admission, aminoglycoside antibiotics or contrast agent, chronic kidney disease (eGFR 60ml/min or less than the treatment of chronic kidney dialysis), urinary tract obstruction, malignant tumor and infection, admitted to hospital after undergoing cardiac surgery, angiography, renal artery stenosis, multiple organ failure, admission time less than 24 hours, age less than 18 old, outside the hospital transferred to our hospital after treatment.
4, study time: throughout the period of hospitalization.
5, urinary angiotensinogen (urine angiotensinogen, UAGT), urinary neutrophil gelatinase associated lipocalin (urine neutrophil gelatinase-associated lipocalin, UNGAL). In the 1 week of hospitalization: every morning urine samples was 10ml, centrifuged at a low temperature (4 DEG, 3000rpm* 15min) after filling in -80 storage C refrigerator for inspection; the blood AGT, every two days to leave the blood 1 times, 4ml (EDTA tubes), centrifugation (at 4 3000rpm*15min) after separating the plasma stored in -80 packaging C refrigerator for inspection. 1 weeks after admission, every 2-3 days to take a blood and urine sample, with the former method. Specimens of time see table 1.
6, other clinical indicators: demography, previous medical history, and clinical indicators.
7, the end of the study
The main end point: AKI; a) KDIGO2012 standard (the standard creatinine) classification table 2KDIGO2012 standard (satisfy any one can diagnose AKI): I.48 hours creatinine rise more than 26.5umol/L (0.3mg/dl),.7 days, up to 1.5 times the baseline serum creatinine values above. Urine volume 0.5ml/kg/h for more than 6 hours
B) secondary end point: AKI did not recover at discharge (blood creatinine was not reduced to baseline); death within 1 years; rehospitalization within 1 years; and progression to CKD (KDIGO2012 standard) within 1 years.
Statistical processing (two level headlines)
1. group: whether AKI occurred during patients' hospitalization, divided into group AKI (group, n=50) and group no-AKI (no group, n=95).
2. two groups of baseline data including demographic, medical history and clinical indicators. Categorical variables (gender classification, comorbidities, and etiology of heart failure) were compared using Fisher's exact test or chi square test Pearson; normal distribution of continuous variables (age, systolic blood pressure, diastolic blood pressure, blood Na, blood K, serum creatinine, Hb comparison between group independent-samples) by T Test; non normal distribution of continuous variables (NT-pro-BNP, UPro/cr, UAlb/cr) between the two groups by Mann-Whitney U Test. of the two groups see results section Table1.
3. among the two groups in 1-7 days of urinary AGT and NGAL levels were compared by Mann-Whitney U Test, Table2 and Table3; the original value was 10 within 7 days of logarithmic conversion trend, see Figure1, Figure2. between the two groups at day 1,3,5,7 blood levels of AGT compared with independent-samples T Test, the original value 7 days trend chart Table4, Figure3.
4. to establish two classification Logistic regression analysis AKI predictor (predictor), the dependent variable is AKI (1,0). The first single factor analysis and AKI related variables, the variable selection of P0.10 and clinical importance, included in the Logistic regression analysis model. The selection principles of independent variables: the research is to predict the occurrence of AKI (so, choose the hospital first days of urinary AGT and NGAL); professional judgment (accepted clinical predictive factors associated with AKI, such as age, DM, Hypertension, urinary albumin, primary disease severity). First days of hospitalization of urinary AGT according to the classification of different high and low level as classification variables in the Logistic regression model. The results of section Table5.
The 5. receiver operating characteristic curve (ROC) analysis: urinary AGT, NGAL alone to predict the area under the AKI curve of joint multivariate prediction. Logistic regression model was established to calculate the predicted probabilities to value, the prediction value as the test variables for prediction and analysis of AKI ROC. Multivariate prediction model of AKI are: 1, clinical the model variables, including age, gender, diabetes, hypertension, primary disease severity and urinary albumin creatinine ratio; 2, clinical model + urinary NGAL, 3 clinical model + NGAL+ urinary AGT, Figure4, Figure5, Figure6.
6. urine index (AGT. NGAL) + clinical model prediction of AKI compared with simple clinical model for predicting R0C of AKI: Table6
7. urine index (AGT) predicts severe AKI (severe AKI): first, compare severe AKI, AKI (stage1) and urine AKI level (logarithmic transformation histogram) of the three groups without AKI (no-AKI), then calculate the urine and predict the prognosis.
8. according to the patient before without regular use of RASI drugs will study the patient group was divided into 2 subgroups: group and non group; AKI subgroup were compared within and without the occurrence of urinary AGT levels (log transformed values of the histogram), ROC analysis: subgroups within first days of hospitalization urine AGT forecast area under the curve of AKI. Figure10, Figure11
9.50 cases of AKI patients were divided into recovery group and non recovery group according to the recovery of renal function at discharge (CR level to baseline level). The clinical index and AKI level of first groups were compared between the two groups, Table7 and ROC analysis: urine AGT predicted the area under the curve of AKI which did not recover, and Figure9 was observed in the two groups.
Analysis of hospitalization first days of low levels of urinary AGT group 10. Kaplan-meier method (Youden index analysis of ROC maximum cutoff is bounded) within 1 weeks after admission AKI cumulative incidence and cumulative mortality within 1 years and re hospitalization rate, Cox regression method to calculate the high levels of urinary AGT group lower urinary AGT levels in group HR and 95%CI.Figure12 Figure13, Figure14..
11. according to the onset of the disease within 1 years into chronic kidney disease (CKD), AKI patients were divided into CKD group and no progress to progress to the CKD group, the levels of urinary AGT were compared between the 2 groups of clinical indicators and different time points (log transformed values), Figure15, Table8, Table9; ROC analysis: area of urinary AGT the level and increase forecast after AKI progress to CKD curve, see Figure16.
Result
1. two ADHF patients were treated with 181 cases, excluding 36 cases, and 145 cases were included.
Of the 2.145 patients, 50 were hospitalized with AKI, which accounted for 34.5%.
The time point for 3.AKI was the 2-7 day of admission and the median was third days.
4. according to whether the occurrence of AKI patients were divided into AKI (n=50) and no-AKI (n=95) two groups, the baseline data see Table1.AKI group than in no-AKI group in age, diabetes and hypertension incidence rate, heart failure severity (NT-pro-BNP high); micro albumin urine protein and urine level of AKI group was significantly higher than that of no-AKI group and AKI group; in group no-AKI, primary disease, gender, baseline serum creatinine, previous history of heart failure, myocardial infarction, blood pressure, left ventricular ejection fraction, NYHA grading showed no statistical difference.
5. within 7 days after admission of AKI group and no-AKI group of urine AGT/cr and NGAL/cr trends in Figure1, Figure2, data Table2, Table3. chart is visible on the AKI group each day of urinary AGT/cr and NGAL/cr levels were significantly higher than no-AKI group, the 1-2 day is more significant; AKI group and no-AKI group blood AGT trend chart see Figure3, the original data Table4, there is no statistically significant difference between the two groups.
6. after the other clinical predictors were corrected, the first day hospitalization of AGT could independently predict the occurrence of AKI, and Table5.
7.ROC analysis showed that urinary AGT can increase urine NGAL, predict AKI AUC, see Figure4, urine AGT and NGAL combined with clinical model significantly improve the prediction of AKI AUC and NRI by the simple clinical model, and see "AKI".
The level of 8. urine AGT could predict the severity of AKI. The higher the level of urine AGT, the more severe the AKI, and Figure7, Figure8..
9. according to the previous subgroup of RASI or not, the unused RASI group and the RASI group urine AGT could be independently predicted for AKI Figure10, Figure11.
10. the level of AGT in high urine could be independently predicted by AKI. The clinical indexes and urinary AGT levels in the non recovery group and the recovery group were Table7, and the ROC analysis was found to be Figure9..
11. at admission, the level of high urinary AGT indicated that AKI occurred within 1 weeks. The risk of AKI in the high urinary AGT level group was 4.3 times as high as that of the low urine AGT group, and Figure12 was seen.
12., high urinary AGT level at admission can predict death and rehospitalization in 1 years. The risk of death in 1 years in high AGT level group is 20 times that in low urinary level group. The risk of rehospitalization in 3 years is 3 times higher than that in low urinary AGT level group. See Figure13, Figure14.
13., high urinary AGT level and increase can predict AKI progressing to CKD. After AKI, CKD and progression progresses to clinical indicators and AGT at different time points. Table8, Table9, trend map and ROC analysis show Figure15, Figure16.
conclusion
Urinary AGT is an early biomarker of AKI in patients with acute decompensated heart failure. Combined urinary NGAL and clinical indicators will further improve the predictive ability. High urinary AGT level indicates AKI progression and clinical adverse prognosis in ADHF patients.

【学位授予单位】：南方医科大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：R541.6

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