Explore the vast range of titles available.

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.

Background Severe trauma represents a major global public health burden and the management of post-traumatic bleeding continues to challenge healthcare systems around the world. Post-traumatic bleeding and associated traumatic coagulopathy remain leading causes of potentially preventable multiorgan failure and death if not diagnosed and managed in an appropriate and timely manner. This sixth edition of the European guideline on the management of major bleeding and coagulopathy following traumatic injury aims to advise clinicians who care for the bleeding trauma patient during the initial diagnostic and therapeutic phases of patient management. Methods The pan-European, multidisciplinary Task Force for Advanced Bleeding Care in Trauma included representatives from six European professional societies and convened to assess and update the previous version of this guideline using a structured, evidence-based consensus approach. Structured literature searches covered the period since the last edition of the guideline, but considered evidence cited previously. The format of this edition has been adjusted to reflect the trend towards concise guideline documents that cite only the highest-quality studies and most relevant literature rather than attempting to provide a comprehensive literature review to accompany each recommendation. Results This guideline comprises 39 clinical practice recommendations that follow an approximate temporal path for management of the bleeding trauma patient, with recommendations grouped behind key decision points. While approximately one-third of patients who have experienced severe trauma arrive in hospital in a coagulopathic state, a systematic diagnostic and therapeutic approach has been shown to reduce the number of preventable deaths attributable to traumatic injury. Conclusion A multidisciplinary approach and adherence to evidence-based guidelines are pillars of best practice in the management of severely injured trauma patients. Further improvement in outcomes will be achieved by optimising and standardising trauma care in line with the available evidence across Europe and beyond. 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  < 0.001). AKI was independently associated with a twofold increase in ICU mortality. Conclusions AKI has an early onset and is independently associated with mortality in trauma patients. Its prevalence varies by a factor 3 according to the severity of injuries and hemorrhage. Prehospital and early hospital risk factors can provide good performance for early prediction of AKI after trauma. Hence, studies aiming to prevent AKI should target patients at high risk of AKI and investigate therapies early in the course of trauma care.

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.

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.

We evaluated the performance of a new device to control the administration of fluid alone or co-administration of fluid and norepinephrine in a pig model of haemorrhagic shock in two sets of experiments. In the first one, resuscitation was guided using continuous arterial pressure measurements (three groups: resuscitation with fluid by a physician, CL resuscitation with fluid, and CL resuscitation with fluid and norepinephrine). In the second one, resuscitation was guided using discontinuous arterial pressure measurements (three groups: CL resuscitation with fluid alone, CL resuscitation with fluid and moderate dose norepinephrine, and CL resuscitation with fluid and a high dose of norepinephrine). Pigs were resuscitated for 1 h. In the first set of experiments, proportion of time spent in the target area of 78–88 mmHg of systolic arterial pressure was not statistically different between the three groups: manual, 71.2% (39.1–80.1); CL with fluid, 87.8% (68.3–97.4); and CL with fluid and norepinephrine, 78.1% (59.2–83.6), p = 0.151. In the second set of experiments, performance of CL resuscitation with fluid or with combination of fluid and high or moderate dose of norepinephrine was not significantly different (p = 0.543 for time in target). Pigs resuscitated with norepinephrine required less fluid and had less haemodilution than pigs resuscitated with fluid alone. Performance of CL resuscitation using continuous arterial pressure measurement was not significantly different than optimised manual treatment by a dedicated physician. Performance of CL resuscitation was reduced with discontinuous arterial pressure measurements in comparison with continuous arterial pressure measurements.

Background Rapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI. Methods Retrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction. Results 2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate. Conclusion Machine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment.

Background Veno-arterial carbon dioxide tension difference (ΔPCO 2 ) and mixed venous oxygen saturation (SvO 2 ) have been shown to be markers of the adequacy between cardiac output and metabolic needs in critical care patients. However, they have hardly been assessed in trauma patients. We hypothesized that femoral ΔPCO 2 (ΔPCO 2 fem ) and SvO 2 (SvO 2 fem ) could predict the need for red blood cell (RBC) transfusion following severe trauma. Methods We conducted a prospective and observational study in a French level I trauma center. Patients admitted to the trauma room following severe trauma with an Injury Severity Score (ISS) > 15, who had arterial and venous femoral catheters inserted were included. ΔPCO 2 fem, SvO 2 fem and arterial blood lactate were measured over the first 24 h of admission. Their abilities to predict the transfusion of at least one pack of RBC (pRBC H6 ) or hemostatic procedure during the first six hours of admission were assessed using receiver operating characteristics curve. Results 59 trauma patients were included in the study. Median ISS was 26 (22–32). 28 patients (47%) received at least one pRBC H6 and 21 patients (35,6%) had a hemostatic procedure performed during the first six hours of admission. At admission, ΔPCO 2 fem was 9.1 ± 6.0 mmHg, SvO 2 fem 61.5 ± 21.6% and blood lactate was 2.7 ± 1.9 mmol/l. ΔPCO 2 fem was significantly higher (11.6 ± 7.1 mmHg vs. 6.8 ± 3.7 mmHg, P  = 0.003) and SvO 2 fem was significantly lower (50 ± 23 mmHg vs. 71.8 ± 14.1 mmHg, P  < 0.001) in patients who were transfused than in those who were not transfused. Best thresholds to predict pRBC H6 were 8.1 mmHg for ΔPCO 2 fem and 63% for SvO 2 fem . Best thresholds to predict the need for a hemostatic procedure were 5.9 mmHg for ΔPCO 2 fem and 63% for SvO 2 fem . Blood lactate was not predictive of pRBC H6 or the need for a hemostatic procedure. Conclusion In severe trauma patients, ΔPCO 2 fem and SvO 2 fem at admission were predictive for the need of RBC transfusion and hemostatic procedures during the first six hours of management while admission lactate was not. ΔPCO 2 fem and SvO 2 fem appear thus to be more sensitive to blood loss than blood lactate in trauma patients, which might be of importance to early assess the adequation of tissue blood flow with metabolic needs.

Importance Decision-making in trauma patients remains challenging and often results in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could improve hemorrhage resuscitation. Aim To develop a ML enhanced decision support tool to predict Need for Hemorrhage Resuscitation (NHR) (part I) and test the collection of the predictor variables in real time in a smartphone app (part II). Design, setting, and participants Development of a ML model from a registry to predict NHR relying exclusively on prehospital predictors. Several models and imputation techniques were tested. Assess the feasibility to collect the predictors of the model in a customized smartphone app during prealert and generate a prediction in four level-1 trauma centers to compare the predictions to the gestalt of the trauma leader. Main outcomes and measures Part 1: Model output was NHR defined by 1) at least one RBC transfusion in resuscitation, 2) transfusion ≥ 4 RBC within 6 h, 3) any hemorrhage control procedure within 6 h or 4) death from hemorrhage within 24 h. The performance metric was the F4-score and compared to reference scores ( RED FLAG, ABC ). In part 2, the model and clinician prediction were compared with Likelihood Ratios (LR). Results From 36,325 eligible patients in the registry (Nov 2010—May 2022), 28,614 were included in the model development (Part 1). Median age was 36 [25–52], median ISS 13 [5–22], 3249/28614 (11%) corresponded to the definition of NHR . A XGBoost model with nine prehospital variables generated the best predictive performance for NHR according to the F4-score with a score of 0.76 [0.73–0.78]. Over a 3-month period (Aug—Oct 2022), 139 of 391 eligible patients were included in part II (38.5%), 22/139 with NHR . Clinician satisfaction was high, no workflow disruption observed and LRs comparable between the model and the clinicians. Conclusions and relevance The ShockMatrix pilot study developed a simple ML-enhanced NHR prediction tool demonstrating a comparable performance to clinical reference scores and clinicians. Collecting the predictor variables in real-time on prealert was feasible and caused no workflow disruption.