PurposeThe present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma... Show morePurposeThe present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma patients.MethodsA retrospective cohort study was conducted on adult patients admitted to the University Medical Center Utrecht following a trauma between January 1 and December 31, 2017. Trauma patients aged 16 years or older without severe neurological injuries, who underwent a CT that included the abdomen within 7 days of admission, were included. An artificial intelligence (AI) algorithm was used to retrieve muscle areas to calculate the psoas muscle index and to retrieve psoas muscle radiation attenuation and visceral fat (VF) area from axial CT images. Multivariable logistic and linear regression analyses were performed to assess associations between body composition parameters and outcomes.ResultsA total of 404 patients were included for analysis. The median age was 49 years (interquartile range [IQR] 30-64), and 66.6% were male. Severe comorbidities (ASA 3-4) were seen in 10.9%, and the median ISS was 9 (IQR 5-14). Psoas muscle index was not independently associated with complications, but it was associated with ICU admission (odds ratio [OR] 0.79, 95% confidence interval [CI] 0.65-0.95), and an unfavorable Glasgow Outcome Scale (GOS) score at discharge (OR 0.62, 95% CI 0.45-0.85). Psoas muscle radiation attenuation was independently associated with the development of any complication (OR 0.60, 95% CI 0.42-0.85), pneumonia (OR 0.63, 95% CI 0.41-0.96), and delirium (OR 0.49, 95% CI 0.28-0.87). VF was associated with developing a delirium (OR 1.95, 95% CI 1.12-3.41).ConclusionIn level-1 trauma patients without severe neurological injuries, automatically derived body composition parameters are able to independently predict an increased risk of specific complications and other poor outcomes. Show less
IntroductionNosocomial pneumonia has poor prognosis in hospitalized trauma patients. Croce et al. published a model to predict post-traumatic ventilator-associated pneumonia, which achieved high... Show moreIntroductionNosocomial 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 methodsThis retrospective study included all trauma patients (>= 16y) admitted for > 24 h to our level-1 trauma center in 2017. Exclusion criteria were pneumonia or antibiotic treatment upon hospital admission, treatment elsewhere > 24 h, or death < 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.Results809 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.ConclusionThe 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. Show less
