Predictive value of TIMP-2 combined with IGFBP-7 for acute kidney injury in critically patients
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摘要:
目的总结组织抑制剂金属蛋白酶（TIMP）-2联合胰岛素样生长因子结合蛋白（IGFBP）-7对于急性肾损伤AKI患者的预测效能。 方法在MEDLINE，EMBASE，Web of Science，Clinical Trials.gov，Cochrane Library，Google Scholar进行检索，筛选出13篇相关研究，使用STATA version17.0及R version3.2.2进行统计分析，根据不同的折点计算合并敏感性、合并特异性及95%可信区间。计算合并的曲线下面积及置信区间。 结果[TIMP-2]×[IGFBP-7]对于急性肾损伤预测效能总的合并曲线下面积为0.84（95%CI, 0.78-0.90）；非手术组为0.76（95%CI, 0.68-0.84）；手术组曲线下面积为0.84（95%CI, 0.81-0.88）；德国亚组为（95%CI, 0.79-0.83）；美国亚组为0.82（95%CI, 0.79-0.95）。当折点为0.3，[TIMP-2]×[IGFBP-7]对于急性肾损伤预测的敏感性为86%，特异性为57%；当折点为2.0，[TIMP-2]×[IGFBP-7]预测的敏感性为37%，特异性为91%。 结论对于包括术后患者在内的重症患者，TIMP-2联合IGFBP-7具有较理想的曲线下面积、敏感性及特异性。 -
关键词:
- 急性肾损伤 /
- 组织抑制剂金属蛋白酶-2 /
- 胰岛素样生长因子结合蛋白-7 /
- 心脏手术 /
- 重症患者
Abstract:ObjectiveTo explore the prediction efficiency of TIMP-2 combined with IGFBP-7 for the early prediction ofAKI in critical patients. MethodsA comprehensive systemic search was carried out in MEDLINE, EMBASE, Web of Science, Clinical Trials.gov, Cochrane Library, Google Scholar, and 13 related studies were screened.The bivariate generalized nonlinear mixed-effect model and the hierarchical summary receiver operating characteristic model were used to estimate the pooled sensitivity, specificity, and summary receiver operating characteristic curve of TIMP-2 plus insulin-like growth factor-binding protein(IGFBP)-7. Results The pooled area under the curve (AUC) of [TIMP-2] × [IGFBP-7] for predicting AKI was 0.84(95% CI, 0.78-0.90). A hierarchical bivariategeneralized linear model with a cutoff of 0.3 indicated a sensitivity of 86% and specificity of 57%; with a cutoff of 2.0 indicating sensitivity of 37% and the specificity of 91%. ConclusionTIMP-2 combined with IGFBP-7 have effective AUC, sensitivity and specificity to predict AKI for the postoperative patients and critical patients . -
表 1 纳入研究的一般情况
作者 患者类型 年份 国家 诊断标准 AKI/No-AKI 样本收集时间（h） [TIMP-2] ×
[IGFBP7] AUC敏感性 特异性 Cut off 样本数目（n） 年龄（岁） 性别（男） Max Bell[14] ICU患者 2015 Sweden KDIGO 19/75 66/50 84%/33% <48 0.4（0.24-0.57） NA NA NA Martin Kimmel[15] ICU患者 2016 Germany KDIGO 46/252 65/63 72%/73% <29 0.77（0.72-0.86） 76% 94% 53% 30% 0.32 Michael Heung[16] ICU患者 2016 USA KDIGO 139/992 62/65 59%/53% <36 0.81（0.77-0.85） 100% 55% 0.3 KyleJ.Gunnerson[17] 心脏手术 2016 USA KDIGO 35/340 67/64 66%/64% <36 0.84（0.76-0.9） 89% 40% 49% 94% 0.32 Kevin Pilarczyk[18] 心脏手术 2015 Germany KDIGO 6/54 68.8/76.2 79.6%/83.3% <24 0.817（0.622-1） 89% 81% 0.817 Ivan Gocze[19] 普通手术 2015 Germany KDIGO 45/62 NA NA 0 0.853（0.779-0.926） NA NA NA Melanie Meersch[20] 心脏手术 2014 Germany KDIGO 26/24 70/72 69.2/62.5 <24（0, 4, 12, 24） 0.9（0.79-1） 73% 58% 0.3 AzraBihorac[21] ICU患者 2014 USA KDIGO 71/337 63/62 55%/49% 0 0.82（0.76-0.88） 92% 37% 46% 95% 0.32 Cedrick Zaouter [22] 心脏手术 2018 France KDIGO 37/50 73/71 49%/77% <24（0, 4, 12, 24） 0.78（0.62-0.93） 65% 62% 0.3 Oezkurr [23] 心脏手术 2017 Germany KDIGO 35/150 NA NA <48 0.81（0.67-0.94） 60% 88% 0.3 Fabian Dusse[24] 心脏手术 2016 Germany KDIGO 15/40 81/80 37.5%/40.6% <24 0.869（0.721-1.0） 75.0% 55.2% 0.91 Yimei Wang[25] 心脏手术 2017 China KDIGO 20/57 64/53 95%/71.9% <24 0.80（0.68-0.91） 75% 70% 0.3 Honore[26] ICU患者 2016 Germany KDIGO 40/232 64/62 43%/53% <12 0.84（0.73-0.92） 95% 38% 0.3 
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