基于PNI、SII、CAR的列线图模型对肝移植术后腹腔感染的预测价值

Predictive value of nomogram model based on PNI, SII and CAR for abdominal infection after liver transplantation

  • 摘要:
      目的  探讨原位肝移植术后发生腹腔感染的危险因素。
      方法  回顾性分析284例原位肝移植受者的临床资料,根据术后是否发生腹腔感染分为感染组(51例)和非感染组(233例)。采用单因素和多因素logistic回归分析腹腔感染的危险因素,构建列线图预测模型并评估模型预测效果,分析连续性变量对腹腔感染的预测价值。
      结果  284例受者中,51例发生腹腔感染,发生率为18.0%。术前有糖尿病比值比(OR)2.66,95%可信区间(CI)1.13~6.14,P=0.013、手术时间长(OR 1.98,95%CI 1.03~3.57,P=0.038)、预后营养指数(PNI)低(OR 2.18,95%CI 1.06~4.44,P=0.023)、全身免疫炎症指数(SII)高(OR 2.21,95%CI 1.06~4.78,P=0.012)、C-反应蛋白/白蛋白比值(CAR)高(OR 1.90,95%CI 1.05~3.49,P=0.029)是肝移植术后腹腔感染的独立危险因素。列线图模型预测肝移植术后腹腔感染的曲线下面积(AUC)为0.761,且模型标准,一致性较好。CAR、PNI、SII均为肝移植术后腹腔感染的预测因子(均为P < 0.05),AUC分别为0.648、0.611和0.648,临界值分别为2.75、43.15和564.50。
      结论  CAR、SII、PNI是肝移植术后腹腔感染的预测因子,基于PNI、SII、CAR构建的列线图模型可有效预测肝移植术后腹腔感染的发生。

     

    Abstract:
      Objective  To investigate the risk factors of abdominal infection after orthotopic liver transplantation.
      Methods  Clinical data of 284 recipients undergoing orthotopic liver transplantation were retrospectively analyzed. All recipients were divided into the infection group (n=51) and non-infection group (n=233) according to the incidence of postoperative abdominal infection. Univariate and multivariate logistic regression analyses were used to identify the risk factors of abdominal infection. Nomogram prediction models were constructed and the prediction efficiency of these models was evaluated. The predictive value of continuous variables for abdominal infection was assessed.
      Results  Among 284 recipients, 51 developed abdominal infection with an incidence of 18.0%. Diabetes mellitus before surgeryodds ratio (OR) 2.66, 95% confidence interval (CI) 1.13-6.14, P=0.013, long operation time (OR 1.98, 95%CI 1.03-3.57, P=0.038), low prognostic nutritional index (PNI) (OR 2.18, 95%CI 1.06-4.44, P=0.023), high systemic immune-inflammation index (SII) (OR 2.21, 95%CI 1.06-4.78, P=0.012) and high C-reactive protein/albumin ratio (CAR) (OR 1.90, 95%CI 1.05-3.49, P=0.029) were independent risk factors for abdominal infection after liver transplantation. The area under curve (AUC) of nomogram model for predicting abdominal infection after liver transplantation was 0.761. The standard model yielded high consistency. CAR, PNI and SII were all predictors of abdominal infection after liver transplantation (all P < 0.05), with AUC of 0.648, 0.611 and 0.648, and cut-off values of 2.75, 43.15 and 564.50, respectively.
      Conclusions  CAR, SII and PNI are predictors of abdominal infection after liver transplantation. The nomogram model based on PNI, SII and CAR may effectively predict the incidence of abdominal infection after liver transplantation.

     

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