Tim Kobes (Utrecht / NL), Anniek Terpstra (Utrecht / NL), Frank F.A. IJpma (Groningen / NL), Luke P.H. Leenen (Utrecht / NL), Marijn Houwert (Utrecht / NL), Karlijn van Wessem (Utrecht / NL), Rolf Groenwold (Leiden / NL), Mark van Baal (Utrecht / NL)
Abstract text (incl. references and figure legends)
Introduction: Nosocomial pneumonia has a poor prognosis in hospitalized trauma patients. Croce et al. published a model to predict post-traumatic VAP, which achieved high discriminatory capacity and reasonable sensitivity. We aimed to externally validate Croce"s model to predict nosocomial pneumonia in patients admitted to a Dutch level-1 trauma center.
Material & Methods: This retrospective study included all trauma patients (≥16y) admitted for >24h to our level-1 trauma center in 2017. Excluded were pneumonia or antibiotic treatment upon hospital admission, treatment elsewhere >24h, or death <48h. Croce"s model used 8 clinical variables—on trauma severity and treatment, available in the ED—to predict nosocomial pneumonia risk. The predictive performance was assessed through discrimination and calibration before and after re-estimating the model"s coefficients. In sensitivity analysis, models were updated with Ridge regression.
Results: 809 Patients were included (median age 51y, 67% male), 86 (11%) developed pneumonia. Pneumonia patients were older, more severely injured, and underwent more emergent interventions. Croce"s model showed good discrimination (AUC 0.83, 95%CI 0.79–0.87), yet predicted probabilities were too low (mean predicted risk 6.4%), and calibration was suboptimal. After full recalibration, discrimination (AUC 0.84, 95%CI 0.80–0.88) and calibration improved. Adding age to the model increased the AUC to 0.87 (95%CI 0.84–0.91). Prediction parameters were similar after Ridge regression.
Conclusions: The externally validated and intercept-recalibrated models show good discrimination and have the potential to predict nosocomial pneumonia. Recalibration of Croce"s model improved predictive performance. The recalibrated model provides further basis for pneumonia prediction in level-1 trauma patients. At this time, clinicians could apply the models to identify high-risk patients, increase monitoring, and initiate preventative measures.
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