Predictive Value of Systemic Immune-Inflammation Index Combined with D-Dimer Levels and Injury Severity Score for the Prognosis of Patients with Multiple Injuries: A Retrospective Analysis.

Background: The prognosis of patients with multiple injuries (MI) remains challenging to predict accurately due to the complexity and heterogeneity of trauma. Traditional scoring systems or single biomarkers often have limitations in terms of sensitivity or specificity. Purpose: The objective of the present study was to investigate the predictive value of combining the systemic immune-inflammation index (SII), D-dimer (D-D) and injury severity score (ISS) for evaluating the prognosis of patients with MI. Patients and Methods: A total of 142 patients with MI admitted to the Emergency Department of Shenzhen Longhua District Central Hospital (Shenzhen, China) from January 2019 to December 2023 were retrospectively analyzed. Patients were then divided into survival (n=102) and non-survival (n=40) groups according to their survival status on postoperative day 28. Logistic regression analysis was performed on indicators with significant differences between the two groups to identify prognostic factors in severe MI. Data were retrospectively analyzed using SPSS 26.0 software. In addition, receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the combined SII, D-D and ISS. Results: The median SII was found to be 340.11 (182.72-578.31) in the survival group and 849.93 (300.83-1034.14) in the non-survival group, yielding a significant difference (P<0.001). Logistic regression indicated that SII, D-D and ISS at admission were independent prognostic factors for mortality. Furthermore, SII, D-D, ISS and their combination showed predictive value [area under the curve (AUC) >0.5] for 28-day mortality, with the combination exhibiting the highest predictive accuracy. Based on the optimal ROC cut-off values (308.8), patients were divided into groups with SII ≤308.8 (n=58) and SII >308.8 (n=84), with significantly lower mortality in the former group (P<0.05). Similarly, patients were categorized into D-D ≤2.35 (n=117) and D-D >2.35 (n=25) groups, with significantly lower mortality in the former group (P<0.05). In conclusion, each indicator demonstrated prognostic significance (AUC >0.5), with the combined model achieving the highest predictive accuracy (AUC =0.969). Conclusion: The integrated indicators offered improved predictive performance compared with individual measures for MI, providing a clinically valuable reference for patient management.