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儿童多发伤继发急性肾损伤危险因素分析

Risk factors of acute kidney injury secondary to multiple injuries in the children
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摘要 目的探讨儿童多发伤患者继发急性肾损伤(AKI)的危险因素。方法回顾性分析厦门大学附属东南医院2019年1月至2023年12月收治的95例儿童多发伤患者临床资料。根据入院1周内是否发生AKI分为AKI组(n=26)和非AKI组(n=69),对比两组患者性别、年龄、病因、合并损伤部位、受伤至住院时间、损伤严重度评分(ISS)、格拉斯哥昏迷评分(GCS)、手术、造影剂使用、是否休克、白细胞、血红蛋白、C反应蛋白、降钙素原、血乳酸、血糖、血钙、血肌酐、尿素氮及胱抑素C等指标。采用单因素和多因素Logistic分析儿童多发伤患者继发AKI危险因素,根据多因素分析结果建立风险预测模型,绘制受试者工作特征(ROC)曲线评估风险模型对继发AKI的预测价值。结果合并头胸腹部损伤、使用造影剂、GCS评分≤8分、血红蛋白<80 g/L、高ISS评分、高血乳酸及高胱抑素C儿童多发伤患者AKI发生率较高(χ^(2)=5.317、11.494、5.235、16.104,t=7.525、11.846、8.783,P<0.05)。多因素Logistic分析发现,头胸腹部损伤(OR=3.718,95%CI 1.103~12.532)、使用造影剂(OR=4.094,95%CI 1.338~13.525)、ISS评分(OR=7.790,95%CI 2.644~24.238)及血红蛋白<80 g/L(OR=6.019,95%CI 2.003~18.088)是儿童多发伤继发AKI的独立危险因素。根据多因素分析结果建立风险预测模型,模型F=-53.642+1.313×(合并头胸腹部损伤=1,无合并头胸腹部损伤=0)+1.410×(使用造影剂=1,无使用造影剂=0)+2.053×(ISS评分)+0.681×(GCS评分≤8分=1,GCS评分>8分=0)+1.795×(血红蛋白<80 g/L=1,血红蛋白≥80 g/L=0)+0.968×(血乳酸)+1.027×(胱抑素C)。绘制ROC曲线分析发现,该模型对儿童多发伤继发AKI具有预测价值,曲线下面积(AUC)为0.823,95%CI为0.717~0.928,P<0.001。结论头胸腹部损伤、使用造影剂、ISS评分及血红蛋白<80 g/L是儿童多发伤继发AKI的独立危险因素,基于以上4个指标建立的风险预测模型具有较好的预测效能,可用于儿童多发伤患者AKI早期筛查。 Objective To investigate the risk factors of acute kidney injury(AKI)secondary to multiple injuries in the children.Methods The clinical data of 95 children with multiple injuries admitted to Dongnan Hospital of Xiamen University from January 2019 to December 2023 were retrospectively analyzed.According to the occurrence of AKI within the first week of admission,they were divided into AKI group(n=26)and non-AKI group(n=69).Gender,age,etiology,site of combined injury,length of hospital stay,injury severity score(ISS),Glasgow coma scale(GCS),operation,use of contrast agent,shock,white blood cells,hemoglobin,C-reactive protein,procalcitonin,blood lactic acid,blood glucose,blood calcium,blood creatinine,urea nitrogen,cystatin C and other indicators were compared between the two groups.The risk factors of secondary AKI in the children with multiple injuries were analyzed by univariate and Logistic multivariate analysis.The risk prediction model was established according to the multivariate analysis,and the ROC curve was drawn to evaluate the predictive value of the risk prediction model for AKI.Results The incidence of AKI was higher in the multiple injury children with head,chest and abdominal injury,use of contrast agent,GCS score≤8 score,hemoglobin<80 g/L,high ISS score,high blood lactic acid and high cystatin C(χ^(2)=5.317,11.494,5.235,16.104,t=7.525,11.846,8.783,P<0.05).Multivariate analysis revealed head,chest and abdominal injury(OR=3.718,95%CI 1.103-12.532),use of contrast agent(OR=4.094,95%CI 1.338-13.525),ISS score(OR=7.790,95%CI 2.644-24.238)and hemoglobin<80 g/L(OR=6.019,95%CI 2.003-18.088)were independent risk factors for secondary AKI in the children with multiple injuries.According to the results of multivariate analysis,a risk prediction model was established F=-53.642+1.313×(head,chest and abdominal injury=1,no head,chest and abdominal injury=0)+1.410×(use of contrast agent=1,no use of contrast agent=0)+2.053×(ISS)+0.681×(GCS≤8 scores=1,GCS>8 scores=0)+1.795×(hemoglobin<80 g/L=1,hemoglobin≥80 g/L=0)+0.968×(blood lactic acid)+1.027×(cystatin C),and ROC curve analysis showed that the model had predictive value for secondary AKI in the children with multiple injuries.The area under the curve(AUC)was 0.823,95%CI ranged from 0.717 to 0.928,P<0.001.Conclusions Head,chest and abdominal injuries,use of contrast agent,ISS score and hemoglobin<80 g/L are independent risk factors for secondary AKI in the children with multiple injuries.The risk prediction model established based on above-mentioned four indicators has good predictive efficacy and can be used for early screening of AKI children.
作者 林庆斌 王健 黄泽滨 庄佳毅 Lin Qingbin;Wang Jian;Huang Zebin;Zhuang Jiayi(Emergency Department,the 909th Hospital(Dongnan Hospital of Xiamen University),Zhangzhou 363000,China)
出处 《中国急救医学》 CAS CSCD 2024年第9期774-778,共5页 Chinese Journal of Critical Care Medicine
关键词 儿童 多发伤 急性肾损伤 并发症 危险因素 预测模型 Children Multiple injuries Acute kidney injury Complications Risk factor Prediction model
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