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Abstract The goal of hemodynamic resuscitation is to optimize the microcirculation of organs to meet their oxygen and metabolic needs. Clinicians are currently blind to what is happening in the microcirculation of organs, which prevents them from achieving an additional degree of individualization of the hemodynamic resuscitation at tissue level. Indeed, clinicians never know whether optimization of the microcirculation and tissue oxygenation is actually achieved after macrovascular hemodynamic optimization. The challenge for the future is to have noninvasive, easy-to-use equipment that allows reliable assessment and immediate quantitative analysis of the microcirculation at the bedside. There are different methods for assessing the microcirculation at the bedside; all have strengths and challenges. The use of automated analysis and the future possibility of introducing artificial intelligence into analysis software could eliminate observer bias and provide guidance on microvascular-targeted treatment options. In addition, to gain caregiver confidence and support for the need to monitor the microcirculation, it is necessary to demonstrate that incorporating microcirculation analysis into the reasoning guiding hemodynamic resuscitation prevents organ dysfunction and improves the outcome of critically ill patients.

Key messages Immediate detection and management of traumatic coagulopathy improves outcomes of severely injured patients. This guideline follows management of the severe trauma patient in chronological order, with a focus on prevention of possible exsanguination. These structured recommendations support measures that prioritise the optimisation of resources for the benefit of bleeding control based on scientific evidence. Empirical management should not be implemented unless no method of monitoring bleeding and coagulation is available. Optimal organisation of the resuscitation team for the bleeding trauma patient includes implementation of these guidelines.

BACKGROUND:Organ failure, including acute kidney injury (AKI), is the third leading cause of death after bleeding and brain injury in trauma patients. We sought to assess the prevalence, the risk factors and the impact of AKI on outcome after trauma. METHODS:We performed a retrospective analysis of prospectively collected data from a multicenter trauma registry. AKI was defined according to the risk, injury, failure, loss of kidney function and end-stage kidney disease (RIFLE) classification from serum creatinine only. Prehospital and early hospital risk factors for AKI were identified using logistic regression analysis. The predictive models were internally validated using bootstrapping resampling technique. RESULTS:We included 3111 patients in the analysis. The incidence of AKI was 13% including 7% stage R, 3.7% stage I and 2.3% stage F. AKI incidence rose to 42.5% in patients presenting with hemorrhagic shock; 96% of AKI occurred within the 5 first days after trauma. In multivariate analysis, prehospital variables including minimum prehospital mean arterial pressure, maximum prehospital heart rate, secondary transfer to the trauma center and data early collected after hospital admission including injury severity score, renal trauma, blood lactate and hemorrhagic shock were independent risk factors in the models predicting AKI. The model had good discrimination with area under the receiver operating characteristic curve of 0.85 (0.82-0.88) to predict AKI stage I or F and 0.80 (0.77-0.83) to predict AKI of all stages. Rhabdomyolysis severity, assessed by the creatine kinase peak, was an additional independent risk factor for AKI when it was forced into the model (OR 1.041 (1.015-1.069) per step of 1000 U/mL, p

BackgroundHaemorrhagic shock is the leading cause of early preventable death in severe trauma. Delayed treatment is a recognized prognostic factor that can be prevented by efficient organization of care. This study aimed to develop and validate Red Flag, a binary alert identifying blunt trauma patients with high risk of severe haemorrhage (SH), to be used by the pre-hospital trauma team in order to trigger an adequate intra-hospital standardized haemorrhage control response: massive transfusion protocol and/or immediate haemostatic procedures.MethodsA multicentre retrospective study of prospectively collected data from a trauma registry (Traumabase®) was performed. SH was defined as: packed red blood cell (RBC) transfusion in the trauma room, or transfusion ≥ 4 RBC in the first 6 h, or lactate ≥ 5 mmol/L, or immediate haemostatic surgery, or interventional radiology and/or death of haemorrhagic shock. Pre-hospital characteristics were selected using a multiple logistic regression model in a derivation cohort to develop a Red Flag binary alert whose performances were confirmed in a validation cohort.ResultsAmong the 3675 patients of the derivation cohort, 672 (18%) had SH. The final prediction model included five pre-hospital variables: Shock Index ≥ 1, mean arterial blood pressure ≤ 70 mmHg, point of care haemoglobin ≤ 13 g/dl, unstable pelvis and pre-hospital intubation. The Red Flag alert was triggered by the presence of any combination of at least two criteria. Its predictive performances were sensitivity 75% (72-79%), specificity 79% (77-80%) and area under the receiver operating characteristic curve 0.83 (0.81-0.84) in the derivation cohort, and were not significantly different in the independent validation cohort of 2999 patients.ConclusionThe Red Flag alert developed and validated in this study has high performance to accurately predict or exclude SH.

Abstract Background Organ failure, including acute kidney injury (AKI), is the third leading cause of death after bleeding and brain injury in trauma patients. We sought to assess the prevalence, the risk factors and the impact of AKI on outcome after trauma. Methods We performed a retrospective analysis of prospectively collected data from a multicenter trauma registry. AKI was defined according to the risk, injury, failure, loss of kidney function and end-stage kidney disease (RIFLE) classification from serum creatinine only. Prehospital and early hospital risk factors for AKI were identified using logistic regression analysis. The predictive models were internally validated using bootstrapping resampling technique. Results We included 3111 patients in the analysis. The incidence of AKI was 13% including 7% stage R, 3.7% stage I and 2.3% stage F. AKI incidence rose to 42.5% in patients presenting with hemorrhagic shock; 96% of AKI occurred within the 5 first days after trauma. In multivariate analysis, prehospital variables including minimum prehospital mean arterial pressure, maximum prehospital heart rate, secondary transfer to the trauma center and data early collected after hospital admission including injury severity score, renal trauma, blood lactate and hemorrhagic shock were independent risk factors in the models predicting AKI. The model had good discrimination with area under the receiver operating characteristic curve of 0.85 (0.82–0.88) to predict AKI stage I or F and 0.80 (0.77–0.83) to predict AKI of all stages. Rhabdomyolysis severity, assessed by the creatine kinase peak, was an additional independent risk factor for AKI when it was forced into the model (OR 1.041 (1.015–1.069) per step of 1000 U/mL, p

Abstract Background Renal perfusion status remains poorly studied at the bedside during septic shock. We sought to measure cortical renal perfusion in patients with septic shock during their first 3 days of care using renal contrast enhanced ultrasound (CEUS). Methods We prospectively included 20 ICU patients with septic shock and 10 control patients (CL) without septic shock admitted to a surgical ICU. Cortical renal perfusion was evaluated with CEUS during continuous infusion of Sonovue (Milan, Italy) within the first 24 h (day 0), between 24 and 48 h (day 1) and after 72 h (day 3) of care. Each measurement consisted of three destruction replenishment sequences that were recorded for delayed analysis with dedicated software (Vuebox). Renal perfusion was quantified by measuring the mean transit time (mTT) and the perfusion index (PI), which is the ratio of renal blood volume (rBV) to mTT. Results Cortical renal perfusion was decreased in septic shock as attested by a lower PI and a higher mTT in patients with septic shock than in patients of the CL group (p = 0.005 and p = 0.03). PI values had wider range in patients with septic shock (median (min-max) of 74 arbitrary units (a.u.) (3–736)) than in patients of the CL group 228 a.u. (67–440)). Renal perfusion improved over the first 3 days with a PI at day 3 higher than the PI at day 0 (74 (22–120) versus 160 (88–245) p = 0.02). mTT was significantly higher in patients with severe acute kidney injury (AKI) (n = 13) compared with patients with no AKI (n = 7) over time (p = 0.005). The PI was not different between patients with septic shock with severe AKI and those with no AKI (p = 0.29). Conclusions Although hemodynamic macrovascular parameters were restored, the cortical renal perfusion can be decreased, normal or even increased during septic shock. We observed an average decrease in cortical renal perfusion during septic shock compared to patients without septic shock. The decrease in cortical renal perfusion was associated with severe AKI occurrence. The use of renal CEUS to guide renal perfusion resuscitation needs further investigation.

Abstract Background Haemorrhagic shock is the leading cause of early preventable death in severe trauma. Delayed treatment is a recognized prognostic factor that can be prevented by efficient organization of care. This study aimed to develop and validate Red Flag, a binary alert identifying blunt trauma patients with high risk of severe haemorrhage (SH), to be used by the pre-hospital trauma team in order to trigger an adequate intra-hospital standardized haemorrhage control response: massive transfusion protocol and/or immediate haemostatic procedures. Methods A multicentre retrospective study of prospectively collected data from a trauma registry (Traumabase®) was performed. SH was defined as: packed red blood cell (RBC) transfusion in the trauma room, or transfusion ≥ 4 RBC in the first 6 h, or lactate ≥ 5 mmol/L, or immediate haemostatic surgery, or interventional radiology and/or death of haemorrhagic shock. Pre-hospital characteristics were selected using a multiple logistic regression model in a derivation cohort to develop a Red Flag binary alert whose performances were confirmed in a validation cohort. Results Among the 3675 patients of the derivation cohort, 672 (18%) had SH. The final prediction model included five pre-hospital variables: Shock Index ≥ 1, mean arterial blood pressure ≤ 70 mmHg, point of care haemoglobin ≤ 13 g/dl, unstable pelvis and pre-hospital intubation. The Red Flag alert was triggered by the presence of any combination of at least two criteria. Its predictive performances were sensitivity 75% (72–79%), specificity 79% (77–80%) and area under the receiver operating characteristic curve 0.83 (0.81–0.84) in the derivation cohort, and were not significantly different in the independent validation cohort of 2999 patients. Conclusion The Red Flag alert developed and validated in this study has high performance to accurately predict or exclude SH.

Background: Trauma-induced coagulopathy includes thrombocytopenia and platelet dysfunction that impact patient outcome. Nevertheless, the role of platelet transfusion remains poorly defined. The aim of the study was 1/ to evaluate the impact of early platelet transfusion on 24-h all-cause mortality and 2/ to describe platelet count at admission (PCA) and its relationship with trauma severity and outcome.Methods: Observational study carried out on a multicentre prospective trauma registry. All adult trauma patients directly admitted in participating trauma centres between May 2011 and June 2019 were included. Severe haemorrhage was defined as ≥ 4 red blood cell units within 6 h and/or death from exsanguination. The impact of PCA and early platelet transfusion (i.e. within the first 6 h) on 24-h all-cause mortality was assessed using uni- and multivariate logistic regression.Results: Among the 19,596 included patients, PCA (229 G/L [189,271]) was associated with coagulopathy, traumatic burden, shock and bleeding severity. In a logistic regression model, 24-h all-cause mortality increased by 37% for every 50 G/L decrease in platelet count (OR 0.63 95% CI 0.57-0.70; p < 0.001). Regarding patients with severe hemorrhage, platelets were transfused early for 36% of patients. Early platelet transfusion was associated with a decrease in 24-h all-cause mortality (versus no or late platelets): OR 0.52 (95% CI 0.34-0.79; p < 0.05).Conclusions: PCA, although mainly in normal range, was associated with trauma severity and coagulopathy and was predictive of bleeding intensity and outcome. Early platelet transfusion within 6 h was associated with a decrease in mortality in patients with severe hemorrhage. Future studies are needed to determine which doses of platelet transfusion will improve outcomes after major trauma. [ABSTRACT FROM AUTHOR]

Background: Hyperoxemia has been associated with increased mortality in critically ill patients, but little is known about its effect in trauma patients. The objective of this study was to assess the association between early hyperoxemia and in-hospital mortality after severe trauma. We hypothesized that a PaO2 ≥ 150 mmHg on admission was associated with increased in-hospital mortality.Methods: Using data issued from a multicenter prospective trauma registry in France, we included trauma patients managed by the emergency medical services between May 2016 and March 2019 and admitted to a level I trauma center. Early hyperoxemia was defined as an arterial oxygen tension (PaO2) above 150 mmHg measured on hospital admission. In-hospital mortality was compared between normoxemic (150 > PaO2 ≥ 60 mmHg) and hyperoxemic patients using a propensity-score model with predetermined variables (gender, age, prehospital heart rate and systolic blood pressure, temperature, hemoglobin and arterial lactate, use of mechanical ventilation, presence of traumatic brain injury (TBI), initial Glasgow Coma Scale score, Injury Severity Score (ISS), American Society of Anesthesiologists physical health class > I, and presence of hemorrhagic shock).Results: A total of 5912 patients were analyzed. The median age was 39 [26-55] years and 78% were male. More than half (53%) of the patients had an ISS above 15, and 32% had traumatic brain injury. On univariate analysis, the in-hospital mortality was higher in hyperoxemic patients compared to normoxemic patients (12% versus 9%, p < 0.0001). However, after propensity score matching, we found a significantly lower in-hospital mortality in hyperoxemic patients compared to normoxemic patients (OR 0.59 [0.50-0.70], p < 0.0001).Conclusion: In this large observational study, early hyperoxemia in trauma patients was associated with reduced adjusted in-hospital mortality. This result contrasts the unadjusted in-hospital mortality as well as numerous other findings reported in acutely and critically ill patients. The study calls for a randomized clinical trial to further investigate this association. [ABSTRACT FROM AUTHOR]