严重多发伤急救患者并发持续炎症-免疫抑制-分解代谢综合征风险的相关预测模型构建与验证

Construction and validation of a predictive model for the risk of severe multiple injuries in emergency patients complicated with persistent inflammation immuo-suppression catabolism syndrome

目的探究严重多发伤急救患者并发持续炎症-免疫抑制-分解代谢综合征(persistent inflammation immuo-suppression catabolism syndrome,PICS)的影响因素,并构建PICS预测模型。方法选取严重多发伤急救患者200例,统计伤后15~20 d内PICS发生情况。根据是否发生PICS分为PICS组31例和非PICS组169例,比较2组临床资料,采用Logistic多因素模型分析严重多发伤急救患者发生PICS的影响因素,根据影响因素构建PICS发生风险的Nomogram预测模型,并验证Nomogram预测模型的预测价值和临床效用。结果严重多发伤急救患者伤后15~20 d内PICS发生率为15.50%(31/200)。PICS组年龄≥60岁比例、急性呼吸窘迫综合征(acute respiratory distress syndrome,ARDS)发生率、入院时血清白细胞介素6(interleukin-6,IL-6)、白细胞介素10(interleukin-10,IL-10)水平均高于非PICS组,入院时格拉斯哥昏迷量表(Glasgow Coma Scale,GCS)评分及血清CD4^(+)/CD8^(+)水平均低于非PICS组(P<0.05)。Logistic回归分析结果显示,年龄、入院时ARDS、IL-6、IL-10均为严重多发伤急救患者发生PICS的危险因素,入院时GCS评分及血清CD4^(+)/CD8^(+)水平为严重多发伤急救患者发生PICS的保护因素(P<0.05)。绘制森林图显示,年龄≥60岁、并发ARDS、入院时血清IL-6、IL-10水平均为严重多发伤急救患者发生PICS的正相关影响因素,入院时GCS评分及血清CD4^(+)/CD8^(+)水平为负相关影响因素(P<0.05);根据影响因素构建严重多发伤急救患者PICS发生风险的Nomogram预测模型,该模型的一致性指数(concordance index,C-index)为0.856,具有良好的区分度,校准曲线显示该预测模型与实际观测结果有较好的一致性,决策曲线(decision curve analysis,DCA)显示该预测模型具有良好的临床效用。结论严重多发伤急救患者发生PICS的影响因素包括年龄、入院时GCS评分、ARDS、血清IL-6、IL-10、CD4^(+)/CD8^(+)水平,根据影响因素构建PICS发生风险的Nomogram预测模型可为临床筛查并发PICS高危患者提供可靠参考依据。

Objective To explore the influencing factors of emergency patients with severe multiple injuries complicated with persistent inflammation immuno-suppression catabolism syndrome(PICS),and to construct a prediction model of PICS.Methods A total of 200 patients with severe multiple injuries were selected,and the occurrence of PICS within 15-20 d after injury was statistically analyzed.According to presence of PICS,they were divided into PICS group(n=31)and non-PICS group(n=169).The clinical data of the two groups were compared,and a multivariate logistic model was used to analyze the influencing factors of PICS in patients with severe multiple injuries.Based on the influencing factors,a Nomogram prediction model for the risk of PICS occurrence was constructed,and the predictive value and clinical utility of the Nomogram prediction model were validated.Results The incidence of PICS in patients with severe multiple injuries within 15-20 d after injury was 15.50%(31/200).The proportion of patients aged≥60 years,the incidence of acute respiratory distress syndrome(ARDS),and the levels of serum interleukin-6(IL-6)and interleukin-10(IL-10)at admission were higher in the PICS group than in the non-PICS group,while the Glasgow Coma Scale(GCS)score and serum CD4^(+)/CD8^(+)levels at admission were lower in the PICS group than in the non-PICS group(P<0.05).Logistic regression analysis showed that age,ARDS,IL-6,and IL-10 at admission were risk factors for PICS in patients with severe multiple injuries,while GCS score and serum CD4^(+)/CD8^(+)levels at admission were protective factors for PICS in patients with severe multiple injuries(P<0.05).The forest plot showed that age≥60 years,concurrent ARDS,and serum IL-6 and IL-10 levels at admission were positively correlated influencing factors for the occurrence of PICS in patients with severe multiple injuries,while GCS score and serum CD4^(+)/CD8^(+)levels at admission were negatively correlated influencing factors(P<0.05).Based on the influencing factors,a Nomogram prediction model for the risk of PICS in patients with severe multiple injuries was constructed.The concordance index(C-index)of this model was 0.856,indicating good discrimination.The calibration curve showed good consistency between the prediction model and the actual observation results,and the decision curve analysis(DCA)showed that the prediction model had good clinical utility.Conclusion The factors influencing the occurrence of PICS in emergency patients with severe multiple injuries include age,GCS score at admission,ARDS,serum IL-6,IL-10,sPD-L1,and CD4^(+)/CD8^(+)levels,and the construction of a Nomogram prediction model for the risk of PICS according to the influencing factors can provide a reliable reference basis for clinical screening of patients at high risk of concurrent PICS.