[Construction of a risk predictive model of acute kidney injury based on urinary tissue inhibitor of metalloproteinase 2 and insulin-like growth factor-binding protein 7 and its early predictive value in critically ill patients].

Q3 Medicine
Haixia Wang, Hongbin Mou, Xiaolan Xu, Ruiqiang Zheng
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引用次数: 0

Abstract

Objective: To establish a risk predictive model nomogram of acute kidney injury (AKI) in critically ill patients by combining urinary tissue inhibitor of metalloproteinase 2 (TIMP2) and insulin-like growth factor-binding protein 7 (IGFBP7), and to verify the predictive value of the model.

Methods: A prospective observational study was conducted. The patients with acute respiratory failure or circulatory disorder admitted to the intensive care unit (ICU) of Northern Jiangsu People's Hospital from November 2017 to April 2020 were enrolled. The patients were enrolled within 24 hours of ICU admission, and their general conditions and relevant laboratory test indicators were collected. At the same time, urine was collected to determine the levels of biomarkers TIMP2 and IGFBP7, and TIMP2×IGFBP7 was calculated. Patients were divided into non-AKI and AKI groups according to whether grade 2 or 3 AKI occurred within 12 hours after enrollment. The general clinical data and urinary TIMP2×IGFBP7 levels of patients between the two groups were compared. The indicators with P < 0.1 in univariate analysis were included in the multivariate Logistic regression analysis to obtain the independent risk factors for grade 2 or 3 AKI within 12 hours in critical patients. An AKI risk predictive model nomogram was established, and the application value of the model was evaluated.

Results: A total of 206 patients were finally enrolled, of whom 54 (26.2%) developed grade 2 or 3 AKI within 12 hours of enrollment, and 152 (73.8%) did not. Compared with the non-AKI group, the patients in the AKI group had higher body mass index (BMI), pre-enrollment serum creatinine (SCr), urinary TIMP2×IGFBP7 and proportion of using vasoactive drugs, and additional exposure to AKI (use of nephrotoxic drugs before enrollment) was more common. Multivariate Logistic regression analysis showed that BMI [odds ratio (OR) = 1.23, 95% confidence interval (95%CI) was 1.10-1.37, P = 0.000], pre-enrollment SCr (OR = 1.01, 95%CI was 1.00-1.02, P = 0.042), use of nephrotoxic drugs (OR = 2.84, 95%CI was 1.34-6.03, P = 0.007) and urinary TIMP2×IGFBP7 (OR = 2.19, 95%CI was 1.56-3.08, P = 0.000) was an independent risk factor for the occurrence of grade 2 or 3 AKI in critical patients. An AKI risk predictive model nomogram was constructed based on the independent risk factors of AKI. Bootstrap validation results showed that the model had good discrimination and calibration in internal validation. Receiver operator characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) of urinary TIMP2×IGFBP7 alone in predicting grade 2 or 3 AKI within 12 hours in critical patients was 0.74 (95%CI was 0.66-0.83), the optimal cut-off value was 1.40 (μg/L) 2/1 000 (sensitivity was 66.7%, specificity was 85.0%), and the predictive performance of the model incorporating urinary TIMP2×IGFBP7 was significantly better than that of the model without urinary TIMP2×IGFBP7 [AUC (95%CI): 0.85 (0.79-0.91) vs. 0.77 (0.70-0.84), P = 0.005], net reclassification index (NRI) was 0.29 (95%CI was 0.08-0.50, P = 0.008), integrated discrimination improvement (IDI) was 0.13 (95%CI was 0.07-0.19, P < 0.001).

Conclusions: The AKI risk predictive model based on urinary TIMP2×IGFBP7 has high clinical value and is expected to be used to early predict the occurrence of AKI in critically ill patients.

[基于尿液组织金属蛋白酶抑制剂 2 和胰岛素样生长因子结合蛋白 7 的急性肾损伤风险预测模型的构建及其在重症患者中的早期预测价值]。
目的结合尿液组织金属蛋白酶抑制剂2(TIMP2)和胰岛素样生长因子结合蛋白7(IGFBP7),建立重症患者急性肾损伤(AKI)风险预测模型提名图,并验证该模型的预测价值:进行了一项前瞻性观察研究。研究对象为2017年11月至2020年4月入住苏北人民医院重症监护室(ICU)的急性呼吸衰竭或循环障碍患者。入选患者均在入住ICU后24小时内入院,并收集其一般情况及相关实验室检查指标。同时,收集尿液测定生物标志物TIMP2和IGFBP7的水平,计算TIMP2×IGFBP7。根据入院后 12 小时内是否发生 2 级或 3 级 AKI,将患者分为非 AKI 组和 AKI 组。比较两组患者的一般临床数据和尿液 TIMP2×IGFBP7 水平。将单变量分析中P<0.1的指标纳入多变量Logistic回归分析,以获得危重患者12小时内发生2级或3级AKI的独立风险因素。建立了 AKI 风险预测模型提名图,并评估了该模型的应用价值:最终共有206名患者入选,其中54人(26.2%)在入选后12小时内出现2级或3级AKI,152人(73.8%)未出现AKI。与未发生 AKI 组相比,发生 AKI 组患者的体重指数(BMI)、入组前血清肌酐(SCr)、尿液 TIMP2×IGFBP7 和使用血管活性药物的比例均较高,且发生 AKI 的额外暴露(入组前使用肾毒性药物)更为常见。多变量逻辑回归分析显示,体重指数(BMI)[几率比(OR)= 1.23,95% 置信区间(95%CI)为 1.10-1.37,P = 0.000]、入组前 SCr(OR = 1.01,95%CI 为 1.00-1.02,P = 0.042)、使用肾毒性药物(OR = 2.84,95%CI 为 1.34-6.03,P = 0.007)和尿液 TIMP2×IGFBP7 (OR = 2.19,95%CI 为 1.56-3.08,P = 0.000)是危重患者发生 2 级或 3 级 AKI 的独立危险因素。根据 AKI 的独立风险因素构建了 AKI 风险预测模型提名图。Bootstrap 验证结果表明,该模型在内部验证中具有良好的区分度和校准性。接收者操作特征曲线(ROC 曲线)分析表明,尿 TIMP2×IGFBP7 单独预测危重患者 12 小时内 2 级或 3 级 AKI 的 ROC 曲线下面积(AUC)为 0.74(95%CI 为 0.66-0.83),最佳临界值为 1.40(μg/L)2/1 000(灵敏度为 66.7%,特异性为 85.0%),包含尿TIMP2×IGFBP7的模型的预测性能明显优于不包含尿TIMP2×IGFBP7的模型[AUC (95%CI): 0.85 (0.79-0.91) vs. 0.77(0.70-0.84),P = 0.005],净重分类指数(NRI)为 0.29(95%CI 为 0.08-0.50,P = 0.008),综合分辨改进(IDI)为 0.13(95%CI 为 0.07-0.19,P <0.001):基于尿TIMP2×IGFBP7的AKI风险预测模型具有较高的临床价值,有望用于早期预测危重症患者AKI的发生。
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来源期刊
Zhonghua wei zhong bing ji jiu yi xue
Zhonghua wei zhong bing ji jiu yi xue Medicine-Critical Care and Intensive Care Medicine
CiteScore
1.00
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