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

Demographic patterns and outcomes of patients in level I trauma centers in three international trauma systems

Introduction: Trauma systems were developed to improve the care for the injured. The designation and elements comprising these systems vary across countries. In this study, we have compared the demographic patterns and patient outcomes of Level I trauma centers in three international trauma systems. Methods: International multicenter prospective trauma registry-based study, performed in the University Medical Center Utrecht (UMCU), Utrecht, the Netherlands, John Hunter Hospital (JHH), Newcastle, Australia, and Harborview Medical Center (HMC), Seattle, the United States. Inclusion: patients =18 years, admitted in 2012, registered in the institutional trauma registry. Results: In UMCU, JHH, and HMC, respectively, 955, 1146, and 4049 patients met the inclusion criteria of which 300, 412, and 1375 patients with Injury Severity Score (ISS) > 15. Mean ISS was higher in JHH (13.5; p < 0.001) and HMC (13.4; p < 0.001) compared to UMCU (11.7). Unadjusted mortality: UMCU = 6.5 %, JHH = 3.6 %, and HMC = 4.8 %. Adjusted odds of death: JHH = 0.498 [95 % confidence interval (CI) 0.303-0.818] and HMC = 0.473 (95 % CI 0.325-0.690) compared to UMCU. HMC compared to JHH was 1.002 (95 % CI 0.664-1.514). Odds of death patients ISS > 15: JHH = 0.507 (95 % CI 0.300-0.857) and HMC = 0.451 (95 % CI 0.297-0.683) compared to UMCU. HMC = 0.931 (95 % CI 0.608-1.425) compared to JHH. TRISS analysis: UMCU: Ws = 0.787, Z = 1.31, M = 0.87; JHH, Ws = 3.583, Z = 6.7, M = 0.89; HMC, Ws = 3.902, Z = 14.6, M = 0.84. Conclusion: This study demonstrated substantial differences across centers in patient characteristics and mortality, mainly of neurological cause. Future research must investigate whether the outcome differences remain with nonfatal and long-term outcomes. Furthermore, we must focus on the development of a more valid method to compare systems

Longitudinal assessment of the inflammatory response: The next step in personalized medicine after severe trauma

Infections in trauma patients are an increasing and substantial cause of morbidity, contributing to a mortality rate of 5-8% after trauma. With increased early survival rates, up to 30-50% of multitrauma patients develop an infectious complication. Trauma leads to a complex inflammatory cascade, in which neutrophils play a key role. Understanding the functions and characteristics of these cells is important for the understanding of their involvement in the development of infectious complications. Recently, analysis of neutrophil phenotype and function as complex biomarkers, has become accessible for point-of-care decision making after trauma. There is an intriguing relation between the neutrophil functional phenotype on admission, and the clinical course (e.g., infectious complications) of trauma patients. Potential neutrophil based cellular diagnostics include subsets based on neutrophil receptor expression, responsiveness of neutrophils to formyl-peptides and FcγRI (CD64) expression representing the infectious state of a patient. It is now possible to recognize patients at risk for infectious complications when presented at the trauma bay. These patients display increased numbers of neutrophil subsets, decreased responsiveness to fMLF and/or increased CD64 expression. The next step is to measure these biomarkers over time in trauma patients at risk for infectious complications, to guide decision making regarding timing and extent of surgery and administration of (preventive) antibiotics

A multi-disciplinary perspective on climate model evaluation for Antarctica

A workshop was organized by Antarctic Climate 21 (AntClim21), with the topic 'evaluation of climate models' representation of Antarctic climate from the perspective of long-term twenty-first-century climate change.' The suggested approach for evaluating whether climate models over- or underestimate the effects of ozone depletion is to diagnose simulated historical trends in lower-stratospheric temperature and compare these to observational estimates. With regard to more regional changes over Antarctica, such as West Antarctic warming, the simulation of teleconnection patterns to the tropical Pacific was highlighted. To improve the evaluation of low-frequency variability and trends in climate models, the use and development of approaches to emulate ice-core proxies in models was recommended. It is recommended that effort be put into improving datasets of ice thickness, motion, and composition to allow for a more complete evaluation of sea ice in climate models. One process that was highlighted in particular is the representation of Antarctic clouds and resulting precipitation. It is recommended that increased effort be put into observations of clouds over Antarctica, such as the use of instruments that can detect cloud-base height or the use of remote sensing resources

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 > 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. 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

Future sea level change from Antarctica's Lambert-Amery glacial system

Future global mean sea level (GMSL) change is dependent on the complex response of the Antarctic ice sheet to ongoing changes and feedbacks in the climate system. The Lambert-Amery glacial system has been observed to be stable over the recent period yet is potentially at risk of rapid grounding line retreat and ice discharge given that a significant volume of its ice is grounded below sea level, making its future contribution to GMSL uncertain. Using a regional ice sheet model of the Lambert-Amery system, we find that under a range of future warming and extreme scenarios, the simulated grounding line remains stable and does not trigger rapid mass loss from grounding line retreat. This allows for increased future accumulation to exceed the mass loss from ice dynamical changes. We suggest that the Lambert-Amery glacial system will remain stable or gain ice mass and mitigate a portion of potential future sea level rise over the next 500 years, with a range of +3.6 to −117.5 mm GMSL equivalent

Mass balance of the Greenland Ice Sheet from 1992 to 2018

In recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today’s imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52%) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48%) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the IPCC’s predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate