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Background: While the incidence and aspects of pneumonia in ICU patients has been extensively discussed in the literature, studies on the occurrence of pneumonia in severely injured patients are rare. The aim of the present study is to elucidate factors associated with the occurrence of pneumonia in severely injured patients with thoracic trauma.
Setting: Level-I University Trauma Centres associated with the TraumaRegister DGU®.
Methods: A total of 1162 severely injured adult patients with thoracic trauma documented in the TraumaRegister DGU® (TR-DGU) were included in this study. Demographic data, injury severity, duration of mechanical ventilation (MV), duration of ICU stay, occurrence of pneumonia, bronchoalveolar lavage, aspiration, pathogen details, and incidences of mortality were evaluated. Statistical evaluation was performed using SPSS (Version 25.0, SPSS, Inc.) software.
Results: The overall incidence of pneumonia was 27.5%. Compared to patients without pneumonia, patients with pneumonia had sustained more severe injuries (mean ISS: 32.6 vs. 25.4), were older (mean age: 51.3 vs. 47.5) and spent longer periods under MV (mean: 368.9 h vs. 114.9 h). Age, sex (male), aspiration, and duration of MV were all independent predictors for pneumonia occurrence in a multivariate analysis. The cut-off point for duration of MV that best discriminated between patients who would and would not develop pneumonia during their hospital stay was 102 h. The extent of thoracic trauma (AISthorax), ISS, and presence of pulmonary comorbidities did not show significant associations to pneumonia incidence in our multivariate analysis. No significant difference in mortality between patients with and without pneumonia was observed.
Conclusions: Likelihood of pneumonia increases with age, aspiration, and duration of MV. These parameters were not found to be associated with differences in outcomes between patients with and without pneumonia. Future studies should focus on independent parameters to more clearly identify severely injured subgroups with a high risk of developing pneumonia.
Level of evidence: Level II - Retrospective medical record review.
Objective: Nationwide data on the epidemiology, treatment characteristics, and long-term outcome of severe traumatic brain injury (TBI) in Germany is not yet existing. Neurosurgeons from the German Neurosurgery Society (DGNC) and traumatologists from the German Trauma Society (DGU), therefore, joined forces in 2016 to conceptualize a TBI module for the well-established Trauma Register of the DGU (TR-DGU). Here, we report how this “German National TBI registry (GNTR)” has been developed, implemented, and tested in a recently completed pilot period.
Methods: The conception and implementation process of the GNTR from August 2016 to February 2019 is described, and results of its 23-months long pilot period from February 2019 to December 2020 are presented. For the pilot period, TBI patients were prospectively enrolled at nine neurosurgical and traumatological hospitals across Germany. Inclusion criteria were treatment on the ICU ≥ 24h, or an ISS score ≥ 16. A variety of clinical, imaging, and laboratory parameters were collected, and the GOSE score was used to assess the outcome at discharge and 6- and 12 months follow-up.
Results: Details on the structure and dataset of the GNTR as well as milestones and pitfalls during its conception and implementation, are outlined. During the pilot period, a total of 264 TBI patients were enrolled. Their demographic characteristics, clinical, imaging, and radiological findings, and their early mortality and functional outcome are described. Furthermore, factors associated with an unfavorable outcome (GOSE 1-4) are assessed using uni- and multivariate regression analyses. Finally, problems and future directions of the GNTR are discussed.
Conclusion: The pilot period of the GNTR offers a first glance at the current epidemiology and treatment characteristics of TBI patients in Germany. More importantly, they show how a national TBI registry yielding high-quality prospective data can be developed, implemented, and tested within four years