Background: In recent years, invasive fungal infections (IFI) have complicated the clinical course of patients with combat-related injuries. Commonalities in injury patterns and characteristics among patients with IFI led to the development of a Joint Trauma System (JTS) clinical practice guideline (CPG) for IFI management. We performed a case-control study to confirm and further delineate risk factors associated with IFI development in combat casualties with the objective of generating data to refine the CPG and promote timelier initiation of treatment.
Methods: Data were collected retrospectively for United States (U.S.) military personnel injured during deployment in Afghanistan from June 2009 through August 2011. Cases were identified as IFI based upon wound cultures with fungal growth and/or fungal elements seen on histology, in addition to the presence of recurrent wound necrosis. Controls were matched using date of injury (±3 mo) and injury severity score (±10). Risk factor parameters analyzed included injury circumstances, blood transfusion requirements, amputations after first operative intervention, and associated injuries. Data are expressed as multivariate odds ratios (OR; 95% confidence interval [CI]).
Results: Seventy-six IFI cases were identified from 1,133 U.S. military personnel wounded in Afghanistan and matched to 150 controls. Parameters associated significantly with the development of IFI multivariate analysis were blast injuries (OR: 5.7; CI: 1.1-29.6), dismounted at time of injury (OR: 8.5; CI: 1.2-59.8); above the knee amputations (OR: 4.1; CI: 1.3-12.7), and large-volume packed red blood cell (PRBC; >20 U) transfusions within first 24 h (OR: 7.0; CI: 2.5-19.7).
Conclusions: Our analysis indicates that dismounted blast injuries, resulting in above the knee amputations, and requirement of large volume PRBC transfusions are independent predictors of IFI development. These data confirm all the preliminary risk factors, except for genitalia/perineal injuries, utilized by JTS in their IFI CPG. Model validation is necessary for further risk factor specification.