Study on the pathogenic distribution and risk factors of pulmonary fungal infections in severely traumatized patients

ZHANG Weina; LI Ruining; AI Meimei

doi:10.3969/j.issn.1673-3827.2025.06.005

Abstract：Objective Examine the risk factors and pathogenic distribution of lung fungal infections in patients who have experienced severe trauma.Methods A retrospective study was conducted on patients with severe trauma complicated with pulmonary fungal infection admitted to the Emergency Department of the First Affiliated Hospital of the Air Force Medical University from January 2022 and December 2024,as well as patients with severe trauma without infection during the same period.Gathered clinical information from two patient groups,analyzed risk factors for pulmonary fungal infections in patients who had experienced severe trauma using a multiple logistic regression model,assessed predictive value using receiver operating characteristic(ROC)curves,and used the CHAID algorithm to build a decision tree model based on risk factors.Results A total of 73 cases were collected from the infected group and 219 cases were collected from the uninfected group.Among the 73 patients in the infection group,a total of 89 fungal strains were detected,with Candida albicans making up the largest percentage(30.34%);multivariate analysis revealed that the following factors were independent risk factors for severe trauma patients with concurrent pulmonary fungal infections:age(OR=1.092,95%CI 1.018-1.171),diabetes(OR=3.591,95%CI 1.352-9.541),acute kidney injury(AKI)(OR=2.960,95%CI 1.004-8.722),APACHE Ⅱ score(OR=1.405,95%CI 1.220-1.619),ICU stay time(OR=1.409,95%CI 1.254-1.583),and tracheotomy assisted ventilation time(OR=2.397,95%CI 1.720-3.341).ROC analysis revealed that the aforementioned indicators and combined prediction were statistically significant(P＜0.05)for severe trauma patients with concurrent pulmonary fungal infections.The combined detection diagnostic efficacy study showed that the sensitivity was 84.9%and the specificity was 91.8%(AUC=0.945,95%CI 0.915-0.976).With a 90.4%classification accuracy,the decision tree model demonstrated that the ICU stay length at the first level was the most significant predictor.Conclusion For severe trauma patients with pulmonary fungal infection,independent risk factors included age,diabetes,AKI,APACHE Ⅱ score,length of stay in intensive care unit,and tracheotomy assisted ventilation time.A more user-friendly presentation format can be offered by the ROC curve and decision tree model,which can also early on detect the potential of severe trauma patients who are complicated by pulmonary fungal infection.The risk of infection can be decreased by using efficient prevention and control strategies.