Pneumonia in trauma patients: Building a basis for patient-tailored prediction

Pneumonia is the most common in-hospital complication among severely injured trauma patients and is associated with increased short-term mortality, prolonged hospital and ICU stay, and reduced long-term quality of life. Predicting which patients are at high risk of developing pneumonia could enable timely, targeted preventive measures — improving outcomes and reducing healthcare costs. This thesis explores the potential of patient-tailored prediction models and investigates novel prognostic markers to support risk stratification in trauma care.

The first part of the thesis centers around prediction modeling. The ventilator-associated pneumonia (VAP) prediction model developed by Croce et al. served as a starting point. Through external validation and model recalibration in two large trauma cohorts — one Dutch, one American — the model demonstrated consistent discriminatory ability, though calibration and overall accuracy were limited by low outcome incidence and possible underreporting. Additional risk factors identified in the Dutch cohort, including specific thoracic injuries and chronic comorbidities, informed an updated model and highlighted the importance of patient-specific characteristics in risk prediction. A systematic review further demonstrated the wide variability in pneumonia definitions across trauma studies, underscoring the urgent need for a standardized outcome definition to support model development and reproducibility.

The second part of this thesis introduces opportunistic screening of routinely acquired CT images as a promising approach to assess patient frailty — a known risk factor for poor outcomes. Bone mineral density, coronary artery calcifications, and signs of pulmonary disease were assessed as surrogate markers of frailty in trauma patients. Lower bone density and coronary calcifications were associated with increased pneumonia risk and in-hospital complications, suggesting that pre-existing physiological vulnerability plays a role in post-trauma prognosis. These markers could contribute to future prediction models, particularly as automated image analysis continues to advance.

Together, the studies in this thesis demonstrate that while perfect prediction is not feasible, risk stratification for nosocomial pneumonia in trauma patients is achievable. The results emphasize the value of early predictors, consistent data, and standardized definitions, as well as the potential of integrating frailty assessment into trauma care. Future prediction models should be prospectively developed, externally validated per center, and optimized for clinical implementation. Ultimately, such models could support more personalized, proactive trauma care — guiding decision-making, reducing complications, and improving long-term outcomes for trauma patients.