重型新型冠状病毒肺炎患者发生急性肾损伤的危险因素及其预测价值

    Risk factors of acute kidney injury in severe COVID-19 patients and their predictive values

      摘要
    • 摘要:
      目的  筛选重型新型冠状病毒肺炎（coronavirus disease 2019，COVID-19）患者发生急性肾损伤（acute kidney injury，AKI）的危险因素，构建预测模型，并探讨其预测价值。
      方法  回顾性分析2022年12月1日至2023年7月1日在陕西省人民医院重症医学科确诊为重型COVID-19 的150例患者的临床资料，根据是否发生AKI分为AKI组（54例）和非AKI组（96例），比较两组患者的临床资料，采用多因素Logistic回归分析筛选重型COVID-19患者发生AKI的危险因素，构建预测模型，并采用受试者工作特征（receiver operating chatacteristic，ROC）曲线分析该模型的预测价值。
      结果  AKI组患者的年龄为74.50（63.00，81.50）岁，明显低于非AKI组75.00（70.00，83.00）岁；AKI组合并慢性阻塞性肺疾病比例（1.85%）和接受经鼻高流量氧疗治疗比例（38.89%），均明显低于非AKI组（13.54%和58.33%）（均P＜0.05）。AKI组急性生理与慢性健康评分Ⅱ20.50（15.00，25.00）和序贯器官衰竭评分（sequential organ failure assessment，SOFA）8.00（6.00，10.00）均明显高于非AKI组16.00（13.00，18.25）和5.00（4.00，6.00）；AKI组患者合并细菌感染比例（61.11%）和接受有创通气治疗比例（57.41%）均显著高于非AKI组（40.62%和25.00%）（均P＜0.05）。AKI组患者氧合指数109.55（75.99，149.50）mmHg，1 mm Hg=0.133 kPa和血红蛋白（109.61 ± 24.46）g/L均显著低于非AKI组134.50（105.36，156.57）mmHg和（121.24 ± 21.77）g/L；AKI组的N端脑利钠肽3148.28（446.42，13572.58）ng/L、降钙素原1480（560，8420）ng/L、部分活化凝血活酶时间39.80（34.42，45.32）s和天冬氨酸氨基转移酶38.00（21.00，66.50）U/L均显著高于非AKI组502.41（171.50，1703.00）ng/L、0.37（110，1040）ng/L、36.95（32.70，42.20）s、34.00（20.50，53.00）U/L，并且其血尿素氮13.86（8.68，18.70）mmol/L、血肌酐177.00（90.30，375.00）μmol/L和胱抑素C2.67（1.77，4.08）mg/L均显著高于非AKI组7.31（5.51，9.67）mmol/L、62.95（51.75，78.10）μmol/L、1.34（1.10，1.61）mg/L（均P＜0.05）。多因素Logistic回归分析结果显示，SOFA评分高、合并细菌感染和血肌酐高均是重型COVID-19患者发生AKI的独立危险因素（均P＜0.05）。以上述危险因素构建预测模型，并进行ROC曲线分析，结果显示该模型预测AKI发生的ROC曲线下面积为0.916（95%CI：0.8687～0.9633），敏感度为77.36%，特异度为92.63%。
      结论  血肌酐高、SOFA评分高、合并细菌感染是重型COVID-19发生AKI的独立危险因素。由上述危险因素构建的预测模型对重型COVID-19发生AKI有一定的预测价值。

       

      Abstract:
      Objective  To screen the risk factors of acute kidney injury （AKI）in severe COVID-19 patients, construct a predictive model and explore its predictive value.
      Methods  From December 1,2022 to July 1, 2023, the relevant clinical data were retrospectively reviewed for 150 patients of severe COVID-19. Based upon the presence or absence of AKI, they were assigned into two groups of AKI（n=54）and non-AKI （n=96）. Multivariate Logistic regression analysis was utilized for screening the risk factors for AKI in severe COVID-19 patients. A prediction model was constructed. And predictive value of model was verified by receiver operating characteristic curve（ROC）.
      Results  Age was significantly lower in AKI group than that in non-AKI group74.50（63.00, 81.50） vs 75.00（70.00, 83.00） year. Proportion of AKI plus chronic obstructive pulmonary disease（1.85%）and proportion on high-flow nasal cannula（HFNC）（38.89%）were significantly lower than those in non-AKI group（13.54%, 58.33%） （both P＜0.05）. Scores of APACHE Ⅱ20.50（15.00, 25.00） and sequential organ failure assessment（SOFA）8.00（6.00, 10.00） were significantly higher in AKI group than those in non-AKI group16.00（13.00, 18.25）, 5.00（4.00, 6.00）. Proportion of patients with bacterial infection（61.11%） and receiving invasive ventilation therapy（57.41%）were significantly higher in AKI group than those in non-AKI group（40.62%, 25.00%） （both P＜0.05）. Oxygenation index109.55（75.99, 149.50）mmHg (1 mm Hg=0.133 kPa) and hemoglobin（109.61±24.46）g/L were significantly lower in AKI group than those in non-AKI group134.50（105.36, 156.57）mmHg and （121.24±21.77）g/L. N-terminal brain natriuretic peptide 3148.28（446.42, 13572.58）ng/L, procalcitonin1.48（0.56, 8.42）ng/L, partially activated thromboplastin time39.80（34.42, 45.32）second and aspartate aminotransferase38.00（21.00, 66.50）U/L were significantly higher in AKI group than those in non-AKI group502.41（171.50, 1703.00）ng/L, 370（110, 1040）ng/L, 36.95（32.70, 42.20）second, 34.00（20.50, 53.00）U/L. Blood urea nitrogen13.86（8.68, 18.70）mmol/L, serum creatinine177.00（90.30, 375.00）μmol/L and cystatin C2.67（1.77, 4.08）mg/L were significantly higher than those in non-AKI group7.31（5.51, 9.67）mmol/L, 62.95（51.75, 78.10）μmol/L and 1.34（1.10, 1.61）mg/L（all P＜0.05）. Multivariate Logistic regression analysis revealed that high SOFA score, concurrent bacterial infection and elevated serum creatinine were independent risk factors for AKI（all P＜0.05）. A prediction model was constructed with the above risk factors and ROC curve analysis performed. The results indicated that area under ROC curve（AUC） of model for predicting AKI was 0.916（95%CI:0.8687-0.9633） with a sensitivity of 77.36% and a specificity of 92.63%.
      Conclusion  Elevated serum creatinine, high SOFA score and concurrent bacterial infection are independent risk factors for severe COVID-19 AKI. The prediction model constructed by the above risk factors has some value in predicting the occurrence of AKI.

       

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