本文關(guān)鍵詞： 急性失代償心力衰竭 急性腎損傷 尿 血管緊張素原(AGT)　出處：《南方醫(yī)科大學(xué)》2013年博士論文　論文類型：學(xué)位論文

【摘要】：研究背景和目的 急性失代償性心力衰竭(ADHF)病人容易并發(fā)急性腎損傷(acute renal injury, AKI),即心腎綜合征(Ⅰ型)。心衰病人并發(fā)腎損傷不但增加病情復(fù)雜性,還增加了病人死亡率,延長(zhǎng)住院時(shí)間及增高醫(yī)療費(fèi)用。新英格蘭醫(yī)學(xué)雜志2012年刊發(fā)的研究表明,發(fā)生心腎綜合征的患者,其60天內(nèi)再住院和死亡率超過33%。廣東省器官衰竭防治重點(diǎn)實(shí)驗(yàn)室在省內(nèi)心力衰竭病人群的研究資料顯示,心腎綜合征發(fā)生率達(dá)44%,死亡率達(dá)23-37%,超過30%病人腎功能障礙不能完全恢復(fù)。由此可見,對(duì)于這一高死亡率、高后遺癥率、涉及心、腎兩個(gè)重要臟器的危重疾病,臨床醫(yī)生迫切需要能早期診斷的可靠方法,通過及時(shí)干預(yù),降低病人的致殘率和死亡率。 目前臨床上診斷AKI指標(biāo)是血肌酐,但腎功能要持續(xù)損傷一大半時(shí)才會(huì)出現(xiàn)血肌酐升高,故血肌酐不是腎損傷的早期敏感指標(biāo)。2005年權(quán)威醫(yī)學(xué)雜志《柳葉刀》發(fā)表了通過檢測(cè)尿明膠酶相關(guān)脂質(zhì)運(yùn)載蛋白(NGAL)預(yù)測(cè)心臟手術(shù)病人術(shù)后急性腎損傷的文章,證實(shí)了尿生物學(xué)標(biāo)志物可以早期診斷腎功能損傷。此后,因尿液檢測(cè)的無創(chuàng)性和簡(jiǎn)便性,研究尿生物學(xué)標(biāo)志物預(yù)測(cè)疾病成為醫(yī)學(xué)熱點(diǎn)。NGAL目前已被公認(rèn)為早期診斷腎損傷的生物學(xué)標(biāo)志物,但單一的生物學(xué)標(biāo)志物在臨床上應(yīng)用是不足夠的,尋找其他潛在的生物學(xué)標(biāo)志物,通過比較和聯(lián)合預(yù)測(cè),將進(jìn)一步提高早期診斷疾病的水平。 目前心腎綜合征發(fā)病機(jī)制尚不完全清楚,腎素-血管緊張素系統(tǒng)異常激活被公認(rèn)為是重要機(jī)制之一。尿AGT在動(dòng)物模型、人類慢性腎臟病等研究中被認(rèn)為可以直接反應(yīng)腎內(nèi)RAS水平,是腎內(nèi)RAS的標(biāo)志物。I型心腎綜合征中,腎臟局部RAS活性是否與AKI發(fā)生、發(fā)展相關(guān)值得深入研究 本研究目的是探明尿AGT能否作為急性失代償性心力衰竭病人發(fā)生AKI的早期生物學(xué)標(biāo)志物,并與目前公認(rèn)的生物學(xué)標(biāo)志物NGAL相比,分析其預(yù)測(cè)作用；其次探明尿AGT能否預(yù)示AKI進(jìn)展和臨床預(yù)后。 方法 1、研究設(shè)計(jì)：前瞻性,多中心,觀察性研究,時(shí)間2010年2月～2012年12月。 2、研究人群：因急性失代償性心力衰竭住南方醫(yī)院和廣東省人民醫(yī)院的病人,診斷標(biāo)準(zhǔn)：2005年歐洲心臟病學(xué)會(huì)急性失代償性心力衰竭診斷和治療指南。 3、納入排除標(biāo)準(zhǔn)：納入標(biāo)準(zhǔn)：具有心衰的癥狀(呼吸困難)和體征(如肺部濕羅音、低血壓、組織灌注不足；頸靜脈壓增高、外周性水腫、肝腫大、腸淤血等右心衰體征),X線胸片示肺淤血；排除標(biāo)準(zhǔn)：院前及入院后使用萬古霉素、氨基糖甙抗生素或造影劑,慢性腎臟病(eGFR小于60ml/min或接受慢性腎臟透析治療),尿路梗阻、惡性腫瘤和感染者,入院后接受心臟外科手術(shù)、造影術(shù),腎動(dòng)脈狹窄,多器官功能衰竭,入院時(shí)間不足24小時(shí),年齡小于18歲,外院經(jīng)治療后轉(zhuǎn)入我院。 4、研究時(shí)間：整個(gè)住院期間。 5、尿血管緊張素原(urine angiotensinogen, UAGT),尿中性粒細(xì)胞明膠酶相關(guān)脂質(zhì)運(yùn)載蛋白(urine neutrophil gelatinase-associated lipocalin, UNGAL)。入院1周內(nèi)：每天留取晨尿標(biāo)本10ml,低溫離心(4℃,3000rpm*15min)后分裝儲(chǔ)于-80℃冰箱待檢；血AGT,每?jī)商炝粞?次,4ml (EDTA抗凝管),低溫離心(4℃,3000rpm*15min)分離血漿后分裝儲(chǔ)于-80℃冰箱待檢。入院1周后,每2-3天留取一次血、尿標(biāo)本,方法同前。標(biāo)本留取時(shí)間點(diǎn)見表1。 6、其他臨床指標(biāo)：人口學(xué),既往病史,臨床指標(biāo)。 7、研究終點(diǎn) a)主要終點(diǎn)：AKI; KDIGO2012標(biāo)準(zhǔn)(本文采用血肌酐標(biāo)準(zhǔn))分級(jí)見表2KDIGO2012標(biāo)準(zhǔn)(滿足一下任何一條即可診斷AKI)：ⅰ.48小時(shí)內(nèi)血肌酐上升≥26.5umol/L (0.3mg/dl)ⅱ.7天內(nèi)血肌酐升至1.5倍基線值以上ⅲ.尿量0.5ml/kg/h超過6小時(shí) b)次要終點(diǎn)：出院時(shí)AKI不恢復(fù)(血肌酐未降至基線水平)；1年內(nèi)死亡；1年內(nèi)再住院；1年內(nèi)進(jìn)展至CKD (KDIGO2012標(biāo)準(zhǔn)) 統(tǒng)計(jì)學(xué)處理(二級(jí)標(biāo)題) 1.分組：按病人住院期間是否發(fā)生AKI,分為AKI組(發(fā)生組,n=50)和no-AKI組(未發(fā)生組,n=95)。 2.兩組基線資料包括人口學(xué)、病史及臨床指標(biāo)。分類變量(性別、并存病、病因及心衰分級(jí))的組間比較采用Fisher精確檢驗(yàn)或Pearson卡方檢驗(yàn)；正態(tài)分布連續(xù)變量(年齡、收縮壓、舒張壓、血Na、血K、血肌酐、Hb)的組間比較采用independent-samples T Test;非正態(tài)分布連續(xù)變量(NT-pro-BNP、UPro/cr、UAlb/cr)的組間比較采用Mann-Whitney U Test。兩組間比較見結(jié)果章節(jié)Table1。 3.兩組間第1-7天尿AGT和尿NGAL水平比較采用Mann-Whitney U Test,見Table2和Table3;原始值對(duì)數(shù)10轉(zhuǎn)換后作7天內(nèi)趨勢(shì)圖,見Figure1, Figure2。兩組間第1、3、5、7天血AGT水平比較采用independent-samples T Test,原始值作7天內(nèi)趨勢(shì)圖,Table4, Figure3。 4.建立二分類Logistic回歸模型分析AKI預(yù)測(cè)因子(predictor),因變量為AKI(1、0)。先單因素分析與AKI有關(guān)的自變量,選擇P0.10和臨床重要的自變量,納入模型后進(jìn)行多因素Logistic回歸分析。自變量的選擇原則：研究目的(預(yù)測(cè)AKI的發(fā)生,故選擇住院第1天的尿AGT和NGAL);專業(yè)判斷(臨床已公認(rèn)與AKI有關(guān)的預(yù)測(cè)因素,如age、DM、Hypertension、尿白蛋白、原發(fā)病嚴(yán)重程度等)。住院第1天尿AGT按不同高低水平分級(jí)后作為分類自變量進(jìn)入Logistic回歸模型。見結(jié)果章節(jié)Table5。 5.受試者工作特性曲線(ROC)分析：尿AGT, NGAL單獨(dú)預(yù)測(cè)AKI的曲線下面積。多變量聯(lián)合預(yù)測(cè)時(shí)建立Logistic回歸模型計(jì)算預(yù)測(cè)概率值,以該預(yù)測(cè)值作為檢驗(yàn)變量進(jìn)行預(yù)測(cè)AKI的ROC分析。多變量預(yù)測(cè)AKI的模型有：1、臨床模型,自變量包括年齡、性別、糖尿病、高血壓、原發(fā)病嚴(yán)重程度和尿白蛋白肌酐比值；2、臨床模型+尿NGAL,3、臨床模型+尿NGAL+尿AGT,見Figure4、Figure5、Figure6。 6.尿指標(biāo)(AGT. NGAL)+臨床模型預(yù)測(cè)AKI與單純臨床模型預(yù)測(cè)AKI的R0C比較：見Table6 7.尿指標(biāo)(AGT)預(yù)測(cè)嚴(yán)重的AKI (severe AKI):先比較severe AKI, AKI (stage1)和未發(fā)生AKI (no-AKI)三組的尿AGT水平(對(duì)數(shù)轉(zhuǎn)換值的直方圖),后計(jì)算尿AGT預(yù)測(cè)severe AKI的AUC和95%CI。見Figure7, Figure8。 8.按病人住院前有無規(guī)律使用RASI藥物將研究病人群分為2個(gè)亞組：使用組和未使用組；比較亞組內(nèi)發(fā)生AKI者和未發(fā)生者尿AGT水平(對(duì)數(shù)轉(zhuǎn)換值的直方圖),ROC分析：亞組內(nèi)住院第1天尿AGT預(yù)測(cè)AKI的曲線下面積。見Figure10,Figure11 9.50例AKI病人,按出院時(shí)腎功能是否恢復(fù)(血肌酐降至基線水平)分布恢復(fù)組和未恢復(fù)組,比較兩組的臨床指標(biāo)和AKI確診第1天尿AGT水平,見Table7; ROC分析：尿AGT預(yù)測(cè)AKI不恢復(fù)的曲線下面積,見Figure9 10. Kaplan-meier法分析住院第1天高低尿AGT水平組(ROC分析中約登指數(shù)最大時(shí)的cutoff為界)入院1周內(nèi)AKI累積發(fā)生率,1年內(nèi)的累積死亡率和再住院率,Cox regression法計(jì)算高尿AGT水平組比較低尿AGT水平組的HR和95%CI。Figure12, Figure13, Figure14。 11.按發(fā)病1年內(nèi)是否進(jìn)入慢性腎臟病(CKD),將AKI病人分為進(jìn)展至CKD組和未進(jìn)展至CKD組,比較2組間臨床指標(biāo)和不同時(shí)間點(diǎn)的尿AGT水平(對(duì)數(shù)轉(zhuǎn)換值),見Figure15, Table8, Table9; ROC分析：尿AGT水平和升幅預(yù)測(cè)AKI后進(jìn)展至CKD曲線下面積,見Figure16。 結(jié)果 1.兩個(gè)中心共收治ADHF病人181例,排除36例,納入145例。 2.145例病人中,50例住院期間發(fā)生AKI,發(fā)生占比34.5%。 3.AKI發(fā)生時(shí)間點(diǎn)為入院第2-7天,中位數(shù)是第3天。 4.按是否發(fā)生AKI將病人分為AKI(n=50)和no-AKI(n=95)兩組,基線資料見Table1。AKI組較no-AKI組年齡大,糖尿病和高血壓發(fā)生率高,心衰程度重(NT-pro-BNP高)；AKI組尿蛋白和尿微量白蛋白水平顯著高于no-AKI組;AKI組和no-AKI組在原發(fā)病、性別、基線血肌酐、既往心衰、心梗史、血壓、左室射血分?jǐn)?shù)、NYHA分級(jí)上均無統(tǒng)計(jì)學(xué)差別。 5.入院7天內(nèi)AKI組和no-AKI組尿AGT/cr和NGAL/cr趨勢(shì)圖見Figure1、 Figure2,原始數(shù)據(jù)見Table2、Table3.圖表上可見AKI組各天尿AGT/cr和NGAL/cr水平均顯著高于no-AKI組,其中以第1-2天更為顯著；AKI組和no-AKI組血AGT趨勢(shì)圖見Figure3,原始數(shù)據(jù)見Table4,兩組間比較無統(tǒng)計(jì)學(xué)差別。 6.在校正其他臨床預(yù)測(cè)因素后,住院第1天尿AGT能獨(dú)立預(yù)測(cè)AKI發(fā)生,見Table5。 7.ROC分析顯示：尿AGT能增加尿NGAL預(yù)測(cè)AKI的AUC,見Figure4、;尿AGT、NGAL聯(lián)合臨床模型顯著提高單純臨床模型預(yù)測(cè)AKI的AUC和NRI,見Table6, Figure5, Figure6。 8.尿AGT水平能預(yù)測(cè)AKI的嚴(yán)重程度。尿AGT水平越高,AKI越嚴(yán)重,見Figure7、Figure8。 9.按既往有無使用RASI藥物分亞組后,未使用RASI組和使用RASI組尿AGT均可獨(dú)立預(yù)測(cè)AKI見Figure10、Figure11。 10.高尿AGT水平能獨(dú)立預(yù)測(cè)AKI不恢復(fù)。不恢復(fù)組和恢復(fù)組臨床指標(biāo)和尿AGT水平見Table7、ROC分析見Figure9。 11.入院時(shí)高尿AGT水平預(yù)示1周內(nèi)AKI發(fā)生,高尿AGT水平組發(fā)生AKI風(fēng)險(xiǎn)是低尿AGT水平組的4.3倍,見Figure12 12.入院時(shí)高尿AGT水平能預(yù)示1年內(nèi)死亡和再住院,高尿AGT水平組1年內(nèi)死亡風(fēng)險(xiǎn)是低尿水平組的20倍,1年內(nèi)再住院風(fēng)險(xiǎn)是低尿AGT水平組的3倍,見Figure13、Figure14 13.高尿AGT水平和升幅能預(yù)測(cè)AKI后進(jìn)展至CKD, AKI后進(jìn)展至CKD與未進(jìn)展者臨床指標(biāo)和各時(shí)間點(diǎn)尿AGT見Table8、Table9,趨勢(shì)圖和ROC分析見Figure15、Figure16 結(jié)論 尿AGT是急性失代償性心力衰竭病人發(fā)生AKI的早期生物學(xué)標(biāo)志物,聯(lián)合尿NGAL和臨床指標(biāo)將進(jìn)一步提高預(yù)測(cè)能力,高尿AGT水平預(yù)示ADHF病人AKI進(jìn)展和臨床不良預(yù)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.

【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：R541.6

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