An integrative model using flow cytometry identifies nosocomial infection after trauma

Area of Science:

• Immunology
• Critical Care Medicine
• Diagnostic Technology

Background:

• Flow cytometry (FCM) is a valuable tool for monitoring immune cell function.
• Identifying nosocomial infections after trauma is crucial for patient outcomes.
• Standardized FCM assessment of cell phenotypes may offer a novel diagnostic approach.

Purpose of the Study:

• To determine if standardized flow cytometry (FCM) can identify nosocomial infections in trauma patients.
• To develop predictive models for severe sepsis (SS), organ space infection (OSI), and ventilator-associated pneumonia (VAP) using FCM data.
• To evaluate the accuracy of these models in a trauma patient cohort.

Main Methods:

• A prospective study involving trauma patients at a Level I center.
• Collection of clinical and FCM data within 24 hours of admission.
• Development and validation of Random Forest (RF) models to predict SS, OSI, and VAP.

Main Results:

• RF models incorporating clinical and FCM data demonstrated high accuracy in predicting infections.
• The SS model achieved an AUC of 0.89 using variables including lymphocyte and NK cell counts.
• The OSI and VAP models showed AUCs of 0.76 and 0.86, respectively, highlighting the role of specific immune cell populations.

Conclusions:

• Combined clinical and FCM data can aid in the early identification of posttraumatic infections.
• Natural killer (NK) cell presence indicates an innate immune response relevant to inflammation.
• Further research is needed to elucidate the functional role of these innate cell phenotypes in predictive modeling post-injury.