预测肝衰竭患者并发感染列线图模型的建立

摘要/Abstract

摘要： 目的建立预测肝衰竭患者并发感染概率的可视化列线图。方法 2014年1月至2019年12月南京市第二医院ICU收治的肝衰竭患者252例。根据感染情况分为感染组及非感染组,分析两组患者的临床资料,logistic单因素及多因素回归分析独立危险因素,利用R语言软件可视化处理逻辑回归(LR)模型获得列线图,并通过内部验证对模型的区分能力和一致性能力进行评估。结果感染组150例,年龄为(44±13)岁,非感染组102例,年龄为(49±14)岁(t=-3.273,P=0.001);肝硬化患者感染组59例(39.3%),非感染组18例(17.6%),χ²=13.46,P<0.01);感染组合并肝性脑病患者105例(70%),非感染组49例(48%),(χ²=4.341,P=0.037);感染组合并腹水患者87例(58%),非感染组20例(19.6%),(χ²=36.63,P<0.01);感染组合并肝肾综合征患者39例(26%),非感染组4例(3.92%),(χ²=20.91,P<0.01);感染组患者APACHEII评分(19.8±5.30)分,非感染组(16.6±5.24)分,(t=-4,8,P<0.01);感染组患者白细胞计数9.56(6.37,14.6)×10⁹/L,非感染组7.25(5.77,10.7)×10⁹/L,(Z=-2.807,P<0.01);感染组丙氨酸氨基转移酶309(64,773)U/L,非感染组554(182,1039)U/L,(Z=-2.577,P=0.01);感染组白蛋白(31.3±5.2)g/L,非感染组(33.1±5.0)g/L,(t=2.657,P=0.008);感染组胆碱酯酶2916(2196,4123)U/L,非感染组3966(2840,4883)U/L,(Z=-3.767,P<0.01);感染组血清钾离子3.98(3.51,4.34)mmol/L,非感染组4.27(3.72,4.54)mmol/L,(t=-2.675,P=0.007);感染组血清铁离子例(26.9±11.2)mmol/L,非感染组例(31.7±12.3)mmol/L,(t=-3.196,P=0.002);感染组肌酐71(52,108)μmol/L,非感染组57.5(45.8,75.3)μmol/L,(Z=-3.676,P<0.01)。多因素Logistic 回归分析显示,腹水(OR=4.90,95%CI:2.51~9.56)、肝性脑病4期(OR=8.41,95%CI:2.18~32.4)、肝肾综合征(OR=3.77,95%CI:1.18~12.1)及 APACHEII评分(OR=1.08,95%CI:1.02~1.55)为肝衰竭患者并发感染的独立危险因素(P<0.05)。列线图模型预测肝衰竭患者发生感染的一致性指数(C-index)为0.803,校准曲线显示列线图模型预测肝衰竭患者并发感染的风险与实际发生风险的平均绝对误差为0.038,ROC 曲线显示其预测肝衰竭患者合并感染的曲线下面积为 0.812(95%CI:0.760~0.864)。结论合并腹水、肝性脑病、肝肾综合征以及较高的APACHEII评分是肝衰竭合并感染的独立危险因素。基于这4个危险因素建立的列线图模型可准确预测肝衰竭患者并发感染的概率。

关键词: 肝衰竭, 感染, 列线图

Abstract: Objective To establish a visual nomogram with predicting value for infection in patients with liver failure. Methods The patients with liver failure admitted to our hospital from January 2014 to December 2019 were collected and they were divided into infected group and non-infected group. The clinical data of patients were collected and analyzed by univariate and multivariate logistic regression analysis. Based on the screened independent risk factors, R language software was used to visualize the logistic regression (LR) model to obtain the nomogram, and the distinguishing ability and consistency ability of the model was evaluated through internal verification. Results Univariate analysis showed that the average ages of infected group and non-infected group were (44±13) and (49±14) respectively, (t=-3.273, P=0.001). There were 59 and 19 patients with liver cirrhosis in infected group (39.3%) and non-infected group (17.6%) respectively, χ²=13.46, P<0.001; There were 105 (70%) and 49 (48%) patients with hepatic encephalopathy in infected group and non-infected group respectively, χ²=4.341, P=0.037; There were 87 (58%) and 20 (19.6%) patients with ascites in infected group and non-infected group, χ²=36.63, P<0.001. There were 39 (26%) and 4 (3.92%) patients with hepatorenal syndrome in infected group and non-infected group, χ²=20.91, P<0.001; APACHEII score in infected group and non-infected group were (19.8±5.30) and (16.6±5.24) respectively, t=-4.8, P<0.001. The white blood cell (WBC) count in infected group and non-infected group were 9.56 (6.37-14.6) ×10⁹/L and 7.25 (5.77-10.7) ×10⁹/L, Z=-2.807, P<0.001. The alanine aminotransferase level in infected group and non-infected group were 309 (64-773) U/L and 554 (182-1039) U/L, Z=-2.577, P=0.01. The albumin level in infected group and non-infected group were (31.3±5.2) g/L and (33.1±5.0) g/L, t=2.657, P=0.008. The cholinesterase level in infected group and non-infected group were 2916 (2196-4123) U/L and 3966 (2840-4883) U/L, Z=-3.767, P<0.001. The serum potassium of the infected group and non-infected group were 3.98 (3.51-4.34) mmol/Land 4.27 (3.72-4.54) mmol/L, t=-2.675, P=0.007. The serum ions of infected group and non-infected group were (26.9±11.2) mmol/L and (31.7±12.3) mmol/L, t=-3.196, P=0.002. The creatinine of infected group and non-infected group were 71 (52-108) μmol/L and 57.5 (45.8-75.3) μmol/L, Z=-3.676, P<0.001. There were statistically significant differences in above indexes between the 2 groups. Multivariate logistic regression analysis showed that ascites (OR=4.90, 95%CI: 2.51~9.56), hepatic encephalopathy stage 4 (OR=8.41, 95%CI: 2.18 ~ 32.4), hepatorenal syndrome (OR=3.77, 95%CI: 1.18-12.1) and APACHEII score (OR=1.08, 95%CI: 1.02-1.15) were independent risk factors for infection in patients with liver failure (P<0.05). The consistency index (C-index) of the nomogram model predicting infection in patients with liver failure was 0.803. The calibration curve showed that the average absolute error between the predicted result and the actual result of the nomogram model was 0.038, and the receiver operator characteristic (ROC) curve shows that the area under the curve was 0.812 (95%CI: 0.760 ~ 0.864). Conclusion Ascites, hepatic encephalopathy, hepatorenal syndrome and higher APACHEII scores are independent risk factors for infection in patients with liver failure. The nomogram model based on these 4 risk factors can accurately predict the probability of infection in patients with liver failure.

Key words: Liver failure, Infection, Nomogram

史东阳, 伏倩倩, 肖玲燕, 杨凯, 郑以山. 预测肝衰竭患者并发感染列线图模型的建立[J]. 肝脏, 2022, 27(5): 521-525.

SHI Dong-yang, FU Qian-qian, XIAO Ling-yan, YANG Kai, ZHENG Yi-shan. Establishment of a nomogram for predicting infection in patients with liver failure[J]. Chinese Hepatolgy, 2022, 27(5): 521-525.

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