A recalibrated prediction model can identify level-1 trauma patients at risk of nosocomial pneumonia

Introduction: Nosocomial pneumonia has poor prognosis in hospitalized trauma patients. Croce et al. published a model to predict post-traumatic ventilator-associated pneumonia, which achieved high discrimination 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. Materials and methods: This retrospective study included all trauma patients (≥ 16y) admitted for &gt; 24 h to our level-1 trauma center in 2017. Exclusion criteria were pneumonia or antibiotic treatment upon hospital admission, treatment elsewhere &gt; 24 h, or death &lt; 48 h. Croce’s model used eight clinical variables—on trauma severity and treatment, available in the emergency department—to predict nosocomial pneumonia risk. The model’s predictive performance was assessed through discrimination and calibration before and after re-estimating the model’s coefficients. In sensitivity analysis, the model was updated using Ridge regression. Results: 809 Patients were included (median age 51y, 67% male, 97% blunt trauma), of whom 86 (11%) developed nosocomial 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 (calibration slope 0.63). After full model 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 the models were updated using Ridge regression. Conclusion: The externally validated and intercept-recalibrated models show good discrimination and have the potential to predict nosocomial pneumonia. At this time, clinicians could apply these models to identify high-risk patients, increase patient monitoring, and initiate preventative measures. Recalibration of Croce’s model improved the predictive performance (discrimination and calibration). The recalibrated model provides a further basis for nosocomial pneumonia prediction in level-1 trauma patients. Several models are accessible via an online tool. Level of evidence: Level III, Prognostic/Epidemiological Study.</p

Increased reduction in exsanguination rates leaves brain injury as the only major cause of death in blunt trauma

Introduction: Central nervous system (CNS) related injuries and exsanguination have been the most common causes of death in trauma for decades. Despite improvements in haemorrhage control in recent years exsanguination is still a major cause of death. We conducted a prospective database study to investigate the current incidence of haemorrhage related mortality. Materials and methods: A prospective database study of all trauma patients admitted to an urban major trauma centre between January 2007 and December 2016 was conducted. All in-hospital trauma deaths were included. Cause of death was reviewed by a panel of trauma surgeons. Patients who were dead on arrival were excluded. Trends in demographics and outcome were analysed per year. Further, 2 time periods (2007–2012 and 2013–2016) were selected representing periods before and after implementation of haemostatic resuscitation and damage control procedures in our hospital to analyse cause of death into detail. Results: 11,553 trauma patients were admitted, 596 patients (5.2%) died. Mean age of deceased patients was 61 years and 61% were male. Mechanism of injury (MOI) was blunt in 98% of cases. Mean ISS was 28 with head injury the most predominant injury (mean AIS head 3.4). There was no statistically significant difference in sex and MOI over time. Even though deceased patients were older in 2016 compared to 2007 (67 vs. 46 years, p < 0.001), mortality was lower in later years (p = 0.02). CNS related injury was the main cause of death in the whole decade; 58% of patients died of CNS in 2007–2012 compared to 76% of patients in 2013–2016 (p = 0.001). In 2007–2012 9% died of exsanguination compared to 3% in 2013–2016 (p = 0.001). Discussion: In this cohort in a major trauma centre death by exsanguination has decreased to 3% of trauma deaths. The proportion of traumatic brain injury has increased over time and has become the most common cause of death in blunt trauma. Besides on-going prevention of brain injury future studies should focus on treatment strategies preventing secondary damage of the brain once the injury has occurre

A recalibrated prediction model can identify level-1 trauma patients at risk of nosocomial pneumonia

Introduction: Nosocomial pneumonia has poor prognosis in hospitalized trauma patients. Croce et al. published a model to predict post-traumatic ventilator-associated pneumonia, which achieved high discrimination 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. Materials and methods: This retrospective study included all trauma patients (≥ 16y) admitted for &gt; 24 h to our level-1 trauma center in 2017. Exclusion criteria were pneumonia or antibiotic treatment upon hospital admission, treatment elsewhere &gt; 24 h, or death &lt; 48 h. Croce’s model used eight clinical variables—on trauma severity and treatment, available in the emergency department—to predict nosocomial pneumonia risk. The model’s predictive performance was assessed through discrimination and calibration before and after re-estimating the model’s coefficients. In sensitivity analysis, the model was updated using Ridge regression. Results: 809 Patients were included (median age 51y, 67% male, 97% blunt trauma), of whom 86 (11%) developed nosocomial 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 (calibration slope 0.63). After full model 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 the models were updated using Ridge regression. Conclusion: The externally validated and intercept-recalibrated models show good discrimination and have the potential to predict nosocomial pneumonia. At this time, clinicians could apply these models to identify high-risk patients, increase patient monitoring, and initiate preventative measures. Recalibration of Croce’s model improved the predictive performance (discrimination and calibration). The recalibrated model provides a further basis for nosocomial pneumonia prediction in level-1 trauma patients. Several models are accessible via an online tool. Level of evidence: Level III, Prognostic/Epidemiological Study.</p