Abstract: 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. 

Key words: multiple traumas, persistent inflammation immuno-suppression catabolism syndrome, influencing factor