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中华肾病研究电子杂志

中华肾病研究电子杂志 ›› 2023, Vol. 12 ›› Issue (01) : 1 -7. doi: 10.3877/cma.j.issn.2095-3216.2023.01.001

论著

慢性肾衰竭患者发生抗生素脑病的预测模型的建立和评价
余建峰1, 金劲松2,†(), 龙利1, 徐敏1, 金晟1, 张继波1, 刘浩1   
  1. 1. 430030　武汉，江汉大学附属湖北省第三人民医院肾病科
    2. 430070　武汉，湖北省中医院肾病科
  • 收稿日期:2022-05-31 出版日期:2023-02-28
  • 通信作者: 金劲松
  • 基金资助:
    2021年湖北省卫生健康委员会科研项目(ZY2021M071)

Establishment and evaluation of prediction model for antibiotic-associated encephalopathy in patients with chronic renal failure

Jianfeng Yu1, Jinsong Jin2,†(), Li Long1, Min Xu1, Sheng Jin1, Jibo Zhang1, Hao Liu1   

  1. 1. Department of Nephrology, Hubei No.3 People′s Hospital Affiliated to Jianghan University, Wuhan 430030
    2. Department of Nephrology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430070; Hubei Province, China
  • Received:2022-05-31 Published:2023-02-28
  • Corresponding author: Jinsong Jin
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引用本文：

余建峰, 金劲松, 龙利, 徐敏, 金晟, 张继波, 刘浩. 慢性肾衰竭患者发生抗生素脑病的预测模型的建立和评价[J]. 中华肾病研究电子杂志, 2023, 12(01): 1-7.

Jianfeng Yu, Jinsong Jin, Li Long, Min Xu, Sheng Jin, Jibo Zhang, Hao Liu. Establishment and evaluation of prediction model for antibiotic-associated encephalopathy in patients with chronic renal failure[J]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2023, 12(01): 1-7.

目的

探讨慢性肾衰竭(CRF)患者发生抗生素相关性脑病(AAE)的危险因素，并建立预测模型。

方法

回顾性分析784例CRF患者的临床资料，所有患者均使用了抗生素治疗，根据患者有无发生AAE，分为AAE组(103例)和非AAE组(681例)，将两组患者有统计学意义的临床指标纳入Logistic回归分析，得出AAE发生的独立危险因素并建立预测模型，并使用列线图展示模型。采用ROC曲线、校准曲线判定该模型的区分度及校准度，使用Bootstrap法对模型进行内部验证。

结果

多变量Logistic回归分析显示，年龄大(OR 1.033, 95%CI: 1.012～1.054)、低血浆白蛋白(OR 0.852, 95%CI：0.802～0.905)、低GFR(OR 0.963, 95%CI：0.940～0.988)、单用β-内酰胺酶抑制剂复方制剂类抗生素(OR 3.827, 95%CI：1.528～9.584)、两种或两种以上抗生素联用(OR 2.913, 95%CI：1.187～7.149)以及抗生素未减量使用(OR 0.343, 95%CI: 0.212～0.553)均为CRF患者发生AAE的独立影响因素。列线图模型的ROC曲线下面积为0.808(95%CI：0.770～0.846)，内部验证C-统计量0.808。

结论

本研究建立的预测模型可对临床上早期识别CRF患者的AAE风险及实施预防性措施提供一定参考价值。

关键词: 慢性肾衰竭, 抗生素相关性脑病, 危险因素, 预测模型
Objective

To explore the risk factors of the occurrence of antibiotic-associated encephalopathy (AAE) in patients with chronic renal failure (CRF) and establish a prediction model.

Methods

The clinical data of 784 patients with CRF were retrospectively analyzed. All the patients had received treatment of antibiotics. According to whether the patients had AAE or not, they were divided into AAE group (n=103) and non-AAE group (n=681). The clinical indicators with statistical differences between the two groups were included in logistic regression analysis to obtain the independent risk factors of AAE and establish a prediction model which was shown by the nomogram. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the discrimination and calibration of the model, and Bootstrap method was used for internal validation.

Results

The multivariate logistic regression analysis showed that aging(OR 1.033, 95%CI: 1.012-1.054), low plasma albumin(OR 0.852, 95%CI: 0.802-0.905), low GFR(OR 0.963, 95%CI: 0.940-0.988), separate use of β-lactamase inhibitor compound preparation antibiotic (OR 3.827, 95%CI: 1.528-9.584), combination of two or more antibiotics(OR 2.913, 95%CI: 1.187-7.149), and use of antibiotics without dose reduction (OR 343, 95%CI: 0.212-0.553) were all independent influencing factors for the occurrence of AAE in CRF patients. The area under the ROC curve (AUC) of the nomogram model was 0.808(95%CI: 0.770-0.846)with an internal verification C-index of 0.808.

Conclusion

The prediction model established in this study may provide some reference value for the early identification of AAE risk and the implementation of preventive measures in CRF.

Key words: Chronic renal failure, Antibiotic-associated encephalopathy, Risk factor, Prediction model
表1 两组患者一般资料及实验室检测指标比较
临床变量 非抗生素脑病组(n＝681) 抗生素脑病组(n＝103) t/χ2值 P值
年龄(岁) 53.06±13.25 60.63±12.48 －5.445 <0.001
男[例(%)] 392(57.6) 58(56.3) 0.057 0.811
BMI(kg/m2) 23.36±2.56 23.19±3.01 0.612 0.540
维持性透析[例(%)] 251(36.9) 37(35.9) 0.034 0.854
感染类型[例(%)]     2.788 0.425
肺部感染 289(42.4) 47(45.6)    
泌尿系感染 232(34.1) 27(26.2)    
皮肤及皮下组织感染 120(17.6) 21(20.4)    
其他 40(5.9) 8(7.8)    
合并高血压[例(%)] 312(45.8) 40(46.2) 1.762 0.184
合并2型糖尿病[例(%)] 301(44.2) 54(52.4) 2.444 0.118
合并脑血管疾病[例(%)] 229(33.6) 47(45.6) 5.652 0.017
血红蛋白(g/L) 97.08±13.19 95.94±10.48 0.836 0.403
血浆白蛋白(g/L) 33.83±5.35 29.80±3.34 10.395 <0.001
GFR[ml/(min·1.73 m2)] 25.07±14.42 17.73±6.72 8.504 <0.001
血钾(mmol/L) 4.46±0.90 4.54±1.17 －0.637 0.525
血钙(mmol/L) 2.04±0.34 2.07±0.30 －1.009 0.313
PCT(ng/ml) 2.66±1.33 2.86±1.13 －1.639 0.103
CO2-CP(mmol/L) 20.35±3.89 20.99±2.96 －1.954 0.052
抗生素使用时间≥7 d[例(%)] 408(59.9) 69(67.0) 1.882 0.170
抗生素使用类别[例(%)]     14.760 0.011
单用头孢菌素类 118(17.3) 17(16.5)    
单用青霉素类 86(12.6) 4(3.9)    
单用喹诺酮类 84(12.3) 13(12.6)    
单用β-内酰胺酶抑制剂复方制剂类 120(17.6) 29(28.2)    
两种或以上抗生素联用 185(27.2) 33(32.0)    
其他 88(12.9) 7(6.8)    
抗生素减量使用[例(%)] 381(55.9) 32(31.1) 22.215 <0.001
表2 自变量赋值
变量 赋值
因变量  
抗生素脑病 否＝0，是＝1
自变量  
脑血管疾病病史 否＝0，是＝1
抗生素使用类别 单用头孢菌素类(Z0＝1，Z1＝0，Z2＝0，Z3＝0，Z4＝0)；单用青霉素类(Z0＝0，Z1＝1，Z2＝0，Z3＝0，Z4＝0)；单用喹诺酮类(Z0＝0，Z1＝0，Z2＝1，Z3＝0，Z4＝0)；单用β-内酰胺酶抑制剂复方制剂类(Z0＝0，Z1＝0，Z2＝0，Z3＝1，Z4＝0)；两种或以上抗生素联用(Z0＝0，Z1＝0，Z2＝0，Z3＝0，Z4＝1)；其他(Z0＝0，Z1＝0，Z2＝0，Z3＝0，Z4＝0)
抗生素是否减量使用 否＝0，是＝1
表3 Logistic回归分析结果
自变量 回归系数 标准误 Wald χ2值 P值 OR值 95%CI
年龄(岁) 0.032 0.010 9.901 0.002 1.033 1.012～1.054
血浆白蛋白(g/L) －0.160 0.031 26.931 <0.001 0.852 0.802～0.905
肾小球滤过率[ml/(min·1.73 m2)] －0.037 0.013 8.533 0.003 0.963 0.940～0.988
抗生素使用类别            
单用头孢菌素类 0.959 0.496 3.736 0.053 2.610 0.987～6.903
单用青霉素类 0.078 0.679 0.013 0.909 1.081 0.285～4.091
单用喹诺酮类 0.827 0.518 2.553 0.110 2.287 0.829～6.307
单用β-内酰胺酶抑制剂 1.342 0.468 8.207 0.004 3.827 1.528～9.584
复方制剂类
两种或以上抗生素联用 1.069 0.458 5.446 0.020 2.913 1.187～7.149
其他a － － － － － －
抗生素减量使用 －1.071 0.244 19.196 <0.001 0.343 0.212～0.553
常量 1.615 1.260 1.643 0.200 5.029  
图1 慢性肾衰竭患者发生抗生素相关性脑病预测模型的列线图注：Points：得分；Total Points：总得分
图2 慢性肾衰竭患者发生抗生素相关性脑病预测模型的受试者工作特征曲线
图3 慢性肾衰竭患者发生抗生素相关性脑病预测模型的校准曲线注：AAE：抗生素相关性脑病；Apparent：参考线；Bias-corrected：校正曲线；Ideal：理想曲线
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