Development and validation of a prehospital prediction model for acute traumatic coagulopathy

A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients

BACKGROUND

Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusion (MT) and mortality. We describe a large-data machine learning-based Coagulation Risk Index (CRI) to test the early prediction of ATC in bleeding trauma patients.

METHODS

Coagulation Risk Index was developed using continuous vital signs (VSs) available during the first 15 minutes after admission at a single trauma center over 4 years. Data to compute the CRI were derived from continuous features of photoplethymographic and electrocardiographic waveforms, oximetry values, and blood pressure trends. Two groups of patients at risk for ATC were evaluated: critical administration threshold and patients who received an MT. Acute traumatic coagulopathy was evaluated in separate models and defined as an international normalized ratio (INR) >1.2 and >1.5 upon arrival. The CRI was developed using 2 years of cases for training and 2 years for testing. The accuracy of the models is described by area under the receiver operator curve with 95% confidence intervals.

RESULTS

A total of 17,567 patients were available for analysis with continuous VS data, 52.8% sustained blunt injury, 30.2% were female, and the mean age was 44.6 years. The ability of CRI to predict ATC in critical administration threshold patients was excellent. The true positive and true negative rates were 95.6% and 88.3%, and 94.9% and 89.2% for INR >1.2 and INR >1.5, respectively. The CRI also demonstrated excellent accuracy in patients receiving MT; true positive and true negative rates were 92.8% and 91.3%, and 100% and 88.1% for INR >1.2 and INR >1.5, respectively.

CONCLUSION

Using continuous VSs and large-data machine learning capabilities, the CRI accurately predicts early ATC in bleeding patients. Clinical application may guide early hemostatic resuscitation. Extension of this technology into the prehospital setting could provide earlier treatment of ATC.

LEVEL OF EVIDENCE

Retrospective, Prognostic Study; Level III.

Development and validation of the tic score for early detection of traumatic coagulopathy upon hospital admission: a cohort study

BACKGROUND

Critically injured patients need rapid and appropriate hemostatic treatment, which requires prompt identification of trauma-induced coagulopathy (TIC) upon hospital admission. We developed and validated the performance of a clinical score based on prehospital resuscitation parameters and vital signs at hospital admission for early diagnosis of TIC.

METHODS

The score was derived from a level-1 trauma center registry (training set). It was then validated on data from two other level-1 trauma centers: first on a trauma registry (retrospective validation set), and then on a prospective cohort (prospective validation set). TIC was defined as a PTratio > 1.2 at hospital admission. Prehospital (vital signs and resuscitation care) and admission data (vital signs and laboratory parameters) were collected. We considered parameters independently associated with TIC in the score (binomial logistic regression). We estimated the score's performance for the prediction of TIC.

RESULTS

A total of 3489 patients were included, and among these a TIC was observed in 22% (95% CI 21-24%) of cases. Five criteria were identified and included in the TIC Score: Glasgow coma scale < 9, Shock Index > 0.9, hemoglobin < 11 g.dL-1, prehospital fluid volume > 1000 ml, and prehospital use of norepinephrine (yes/no). The score, ranging from 0 and 9 points, had good performance for the identification of TIC (AUC: 0.82, 95% CI: 0.81-0.84) without differences between the three sets used. A score value < 2 had a negative predictive value of 93% and was selected to rule-out TIC. Conversely, a score value ≥ 6 had a positive predictive value of 92% and was selected to indicate TIC.

CONCLUSION

The TIC Score is quick and easy to calculate and can accurately identify patients with TIC upon hospital admission.

A cost-aware framework for the development of AI models for healthcare applications

Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads. However, when time or financial resources for gathering input data are limited, as in emergency and critical-care medicine, developing accurate AI models, which typically require inputs for many clinical variables, may be impractical. Here we report a model-agnostic cost-aware AI (CoAI) framework for the development of predictive models that optimize the trade-off between prediction performance and feature cost. By using three datasets, each including thousands of patients, we show that relative to clinical risk scores, CoAI substantially reduces the cost and improves the accuracy of predicting acute traumatic coagulopathy in a pre-hospital setting, mortality in intensive-care patients and mortality in outpatient settings. We also show that CoAI outperforms state-of-the-art cost-aware prediction strategies in terms of predictive performance, model cost, training time and robustness to feature-cost perturbations. CoAI uses axiomatic feature-attribution methods for the estimation of feature importance and decouples feature selection from model training, thus allowing for a faster and more flexible adaptation of AI models to new feature costs and prediction budgets.

Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches

BACKGROUND

Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes. Prehospital data are among the earliest information collected by health care systems. Using these untapped sources of data in machine learning (ML)-based approaches can identify patients with sepsis earlier in emergency department (ED).

OBJECTIVES

This integrative literature review aims to discuss the importance of utilizing prehospital data elements in ED, summarize their current use in developing ML-based prediction models, and specifically identify those data elements that can potentially contribute to early identification of sepsis in ED when used in ML-based approaches.

METHOD

Literature search strategy includes following two separate searches: (1) use of prehospital data in ML models in ED; and (2) ML models that are developed specifically to predict/detect sepsis in ED. In total, 24 articles are used in this review.

RESULTS

A summary of prehospital data used to identify time-sensitive conditions earlier in ED is provided. Literature related to use of ML models for early identification of sepsis in ED is limited and no studies were found related to ML models using prehospital data in prediction/early identification of sepsis in ED. Among those using ED data, ML models outperform traditional statistical models. In addition, the use of the free-text elements and natural language processing (NLP) methods could result in better prediction of sepsis in ED.

CONCLUSION

This study reviews the use of prehospital data in early decision-making in ED and suggests that researchers utilize such data elements for prediction/early identification of sepsis in ML-based approaches.

A Machine Learning Approach for the Prediction of Traumatic Brain Injury Induced Coagulopathy

Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate.

Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population.

Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.

Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902–0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)—over other models across a range of threshold probability values.

Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).

Prehospital coagulation measurement by a portable blood analyzer in a helicopter emergency medical service (HEMS)

In helicopter emergency medical services, HEMS, coagulopathy presents both in trauma (e.g. consumption of coagulation factors) and non-trauma cases (e.g. anticoagulant use). Therefore, in HEMS coagulation measurements appear promising and Prothrombin Time (PT) and derived INR are attractive variables herein. We tested the feasibility of prehospital PT/INR coagulation measurements in HEMS. This study was performed at the Dutch HEMS, using a portable blood analyzer (i-Stat®1, Abbott). PT/INR measurements were performed on (hemodiluted) author's blood, and both trauma- and non-trauma HEMS patients. Device-related benefits of the i-Stat PT/INR system were portability, speed and ease of handling. Limitations included a rather narrow operational temperature range (16-30 °C). PT/INR measurements (n = 15) were performed on hemodiluted blood, and both trauma and non-trauma patients. The PT/INR results confirmed effects of hemodilution and anticoagulation, however, most measurement results were in the normal INR-range (0.9-1.2). We conclude that prehospital PT/INR measurements, although with limitations, are feasible in HEMS operations.

Tools to predict acute traumatic coagulopathy in the pre-hospital setting: a review of the literature

Introduction:

Recognising acute traumatic coagulopathy (ATC) poses a significant challenge to improving survival in emergency care. Paramedics are in a prime position to identify ATC in pre-hospital major trauma and initiate appropriate coagulopathy management.

Method:

A database literature review was conducted using Scopus, CINAHL and MEDLINE.

Results:

Two themes were identified from four studies: prediction tools, and point-of-care testing. Prediction tools identified key common ATC markers in the pre-hospital setting, including: systolic blood pressure, reduced Glasgow Coma Score and trauma to the chest, abdomen and pelvis. Point-of-care testing was found to have limited value.

Conclusion:

Future research needs to explore paramedics using prediction tools in identifying ATC, which could alert hospitals to prepare for blood products for damage control resuscitation.

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Background

Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.

Objective

We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.

Methods

Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.

Results

Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.

Conclusions

For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization

Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30% of severely injured patients who are admitted to emergency departments (EDs). Given that conventional coagulation indicators usually require more than 1 hour after admission to yield results—a limitation that frequently prevents the ability for clinicians to make appropriate interventions during the optimal therapeutic window—it is clearly of vital importance to develop prediction models that can rapidly identify ATC; such models would also facilitate ancillary resource management and clinical decision support. Using the critical care Emergency Rescue Database and further collected data in ED, a total of 1385 patients were analyzed and cases with initial international normalized ratio (INR) values >1.5 upon admission to the ED met the defined diagnostic criteria for ATC; nontraumatic conditions with potentially disordered coagulation systems were excluded. A total of 818 individuals were collected from Emergency Rescue Database as derivation cohorts, then were split 7:3 into training and test data sets. A Pearson correlation matrix was used to initially identify likely key clinical features associated with ATC, and analysis of data distributions was undertaken prior to the selection of suitable modeling tools. Both machine learning (random forest) and traditional logistic regression were deployed for prediction modeling of ATC. After the model was built, another 587 patients were further collected in ED as validation cohorts. The ATC prediction models incorporated red blood cell count, Shock Index, base excess, lactate, diastolic blood pressure, and potential of hydrogen. Of 818 trauma patients filtered from the database, 747 (91.3%) patients did not present ATC (INR ≤ 1.5) and 71 (8.7%) patients had ATC (INR > 1.5) upon admission to the ED. Compared to the logistic regression model, the model based on the random forest algorithm showed better accuracy (94.0%, 95% confidence interval [CI]: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95), precision (93.3%, 95% CI: 0.914-0.948 to 93.1%, 95% CI: 0.912-0.946), F1 score (93.4%, 95% CI: 0.915-0.949 to 92%, 95% CI: 0.9-0.937), and recall score (94.0%, 95% CI: 0.922-0.954 to 93.5%, 95% CI: 0.916-0.95) but yielded lower area under the receiver operating characteristic curve (AU-ROC) (0.810, 95% CI: 0.673-0.918 to 0.849, 95% CI: 0.732-0.944) for predicting ATC in the trauma patients. The result is similar in the validation cohort. The values for classification accuracy, precision, F1 score, and recall score of random forest model were 0.916, 0.907, 0.901, and 0.917, while the AU-ROC was 0.830. The values for classification accuracy, precision, F1 score, and recall score of logistic regression model were 0.905, 0.887, 0.883, and 0.905, while the AU-ROC was 0.858. We developed and validated a prediction model based on objective and rapidly accessible clinical data that very confidently identify trauma patients at risk for ATC upon their arrival to the ED. Beyond highlighting the value of ED initial laboratory tests and vital signs when used in combination with data analysis and modeling, our study illustrates a practical method that should greatly facilitates both warning and guided target intervention for ATC.

Validation of the COAST score for predicting acute traumatic coagulopathy: A retrospective single-centre cohort study

Purpose

Early identification of trauma patients at risk of developing acute traumatic coagulopathy is important in initiating appropriate, coagulopathy-focused treatment. A clinical acute traumatic coagulopathy prediction tool is a quick, simple method to evaluate risk. The COAST score was developed in Australia and we hypothesised that it could predict coagulopathy and bleeding-related adverse outcomes in other advanced trauma systems. We validated COAST on a single-centre cohort of trauma patients from a trauma centre in Belgium.

Methods

The COAST score was modified to suit available data; we used entrapment, blood pressure, temperature, major chest injury and abdominal injury to calculate the score. Acute traumatic coagulopathy was defined as international normalised ratio >1.5 or activated partial thromboplastin time >60 s upon arrival of the patient to the hospital. Data were extracted from the local trauma registry on patients that presented between 1 January and 31 December 2015.

Results

In all, 133 patients met the inclusion criteria (>16 years old, available COAST and outcome data) for analysis. The COAST score had an area under the receiver operating characteristics curve of 0.941 (95% CI: 0.884–0.999) and at COAST ≥3, it had 80% sensitivity and 96% specificity. The score also identified patients with higher rates of mortality, blood transfusion and emergent surgery.

Conclusion

This retrospective cohort study demonstrated the utility of the COAST score in identifying trauma patients who are likely to have bleeding-related poor outcomes. The early identification of these patients will facilitate timely, appropriate treatment for acute traumatic coagulopathy and minimise the risk of over-treatment. It can also be used to select patients with acute traumatic coagulopathy for trials involving therapeutic agents targeted at acute traumatic coagulopathy.

Pre-hospital modified shock index for prediction of massive transfusion and mortality in trauma patients

BACKGROUND

Modified shock index (MSI) is a useful predictor in trauma patients. However, the value of prehospital MSI (preMSI) in trauma patients is unknown. The aim of this study was to investigate the accuracy of preMSI in predicting massive transfusion (MT) and hospital mortality among trauma patients.

METHODS

This was a retrospective, observational, single-center study. Patients presenting consecutively to the trauma center between January 2016 and December 2017, were included. The predictive ability of both prehospital shock index (preSI) and preMSI for MT and hospital mortality was assessed by calculating the areas under the receiver operating characteristic curves (AUROCs).

RESULTS

A total of 1007 patients were included. Seventy-eight (7.7%) patients received MT, and 30 (3.0%) patients died within 24 h of admission to the trauma center. The AUROCs for predicting MT with preSI and preMSI were 0.773 (95% confidence interval [CI], 0.746-0.798) and 0.765 (95% CI, 0.738-0.791), respectively. The AUROCs for predicting 24-hour mortality with preSI and preMSI were 0.584 (95% CI, 0.553-0.615) and 0.581 (95% CI, 0.550-0.612), respectively.

CONCLUSIONS

PreSI and preMSI showed moderate accuracy in predicting MT. PreMSI did not have higher predictive power than preSI. Additionally, in predicting hospital mortality, preMSI was not superior to preSI.

Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model

OBJECTIVE

The aim of this study was to develop and validate a risk prediction tool for trauma-induced coagulopathy (TIC), to support early therapeutic decision-making.

BACKGROUND

TIC exacerbates hemorrhage and is associated with higher morbidity and mortality. Early and aggressive treatment of TIC improves outcome. However, injured patients that develop TIC can be difficult to identify, which may compromise effective treatment.

METHODS

A Bayesian Network (BN) prediction model was developed using domain knowledge of the causal mechanisms of TIC, and trained using data from 600 patients recruited into the Activation of Coagulation and Inflammation in Trauma (ACIT) study. Performance (discrimination, calibration, and accuracy) was tested using 10-fold cross-validation and externally validated on data from new patients recruited at 3 trauma centers.

RESULTS

Rates of TIC in the derivation and validation cohorts were 11.8% and 11.0%, respectively. Patients who developed TIC were significantly more likely to die (54.0% vs 5.5%, P < 0.0001), require a massive blood transfusion (43.5% vs 1.1%, P < 0.0001), or require damage control surgery (55.8% vs 3.4%, P < 0.0001), than those with normal coagulation. In the development dataset, the 14-predictor BN accurately predicted this high-risk patient group: area under the receiver operating characteristic curve (AUROC) 0.93, calibration slope (CS) 0.96, brier score (BS) 0.06, and brier skill score (BSS) 0.40. The model maintained excellent performance in the validation population: AUROC 0.95, CS 1.22, BS 0.05, and BSS 0.46.

CONCLUSIONS

A BN (http://www.traumamodels.com) can accurately predict the risk of TIC in an individual patient from standard admission clinical variables. This information may support early, accurate, and efficient activation of hemostatic resuscitation protocols.

Early Identification of Acute Traumatic Coagulopathy Using Clinical Prediction Tools: A Systematic Review

: Background and objectives: Prompt identification of patients with acute traumatic coagulopathy (ATC) is necessary to expedite appropriate treatment. An early clinical prediction tool that does not require laboratory testing is a convenient way to estimate risk. Prediction models have been developed, but none are in widespread use. This systematic review aimed to identify and assess accuracy of prediction tools for ATC. Materials and Methods: A search of OVID Medline and Embase was performed for articles published between January 1998 and February 2018. We searched for prognostic and predictive studies of coagulopathy in adult trauma patients. Studies that described stand-alone predictive or associated factors were excluded. Studies describing prediction of laboratory-diagnosed ATC were extracted. Performance of these tools was described. Results: Six studies were identified describing four different ATC prediction tools. The COAST score uses five prehospital variables (blood pressure, temperature, chest decompression, vehicular entrapment and abdominal injury) and performed with 60% sensitivity and 96% specificity to identify an International Normalised Ratio (INR) of >1.5 on an Australian single centre cohort. TICCS predicted an INR of >1.3 in a small Belgian cohort with 100% sensitivity and 96% specificity based on admissions to resuscitation rooms, blood pressure and injury distribution but performed with an Area under the Receiver Operating Characteristic (AUROC) curve of 0.700 on a German trauma registry validation. Prediction of Acute Coagulopathy of Trauma (PACT) was developed in USA using six weighted variables (shock index, age, mechanism of injury, Glasgow Coma Scale, cardiopulmonary resuscitation, intubation) and predicted an INR of >1.5 with 73.1% sensitivity and 73.8% specificity. The Bayesian network model is an artificial intelligence system that predicted a prothrombin time ratio of >1.2 based on 14 clinical variables with 90% sensitivity and 92% specificity. Conclusions: The search for ATC prediction models yielded four scoring systems. While there is some potential to be implemented effectively in clinical practice, none have been sufficiently externally validated to demonstrate associations with patient outcomes. These tools remain useful for research purposes to identify populations at risk of ATC.

Management of Severe Bleeding in Patients Treated With Oral Anticoagulants: Proceedings Monograph From the Emergency Medicine Cardiac Research and Education Group-International Multidisciplinary Severe Bleeding Consensus Panel October 20, 2018

In this Emergency Medicine Cardiac Research and Education Group (EMCREG)-International Proceedings Monograph from the October 20, 2018, EMCREG-International Multidisciplinary Consensus Panel on Management of Severe Bleeding in Patients Treated With Oral Anticoagulants held in Orlando, FL, you will find a detailed discussion regarding the treatment of patients requiring anticoagulation and the reversal of anticoagulation for patients with severe bleeding. For emergency physicians, critical care physicians, hospitalists, cardiologists, internists, surgeons, and family physicians, the current approach and disease indications for treatment with anticoagulants such as coumadin, factor IIa, and factor Xa inhibitors are particularly relevant. When a patient treated with anticoagulants presents to the emergency department, intensive care unit, or operating room with severe, uncontrollable bleeding, achieving rapid, controlled hemostasis is critically important to save the patient's life. This EMCREG-International Proceedings Monograph contains multiple sections reflecting critical input from experts in Emergency Cardiovascular Care, Prehospital Emergency Medical Services, Emergency Medicine Operations, Hematology, Hospital Medicine, Neurocritical Care, Cardiovascular Critical Care, Cardiac Electrophysiology, Cardiology, Trauma and Acute Care Surgery, and Pharmacy. The first section provides a description of the current indications for the treatment of patients using oral anticoagulants including coumadin, the factor IIa (thrombin) inhibitor dabigatran, and factor Xa inhibitors such as apixaban and rivaroxaban. In the remaining sections, the treatment of patients presenting to the hospital with major bleeding becomes the focus. The replacement of blood components including red blood cells, platelets, and clotting factors is the critically important initial treatment for these individuals. Reversing the anticoagulated state is also necessary. For patients treated with coumadin, infusion of vitamin K helps to initiate the process of protein synthesis for the vitamin K-dependent coagulation proteins II, VII, IX, and X and the antithrombotic protein C and protein S. Repletion of clotting factors for the patient with 4-factor prothrombin complex concentrate, which includes factors II (prothrombin), VII, IX, and X and therapeutically effective concentrations of the regulatory proteins (protein C and S), provides real-time ability to slow bleeding. For patients treated with the thrombin inhibitor dabigatran, treatment using the highly specific, antibody-derived idarucizumab has been demonstrated to reverse the hypocoagulable state of the patient to allow blood clotting. In May 2018, andexanet alfa was approved by the US Food and Drug Administration to reverse the factor Xa anticoagulants apixaban and rivaroxaban in patients with major bleeding. Before the availability of this highly specific agent, therapy for patients treated with factor Xa inhibitors presenting with severe bleeding usually included replacement of lost blood components including red blood cells, platelets, and clotting factors and 4-factor prothrombin complex concentrate, or if not available, fresh frozen plasma. The evaluation and treatment of the patient with severe bleeding as a complication of oral anticoagulant therapy are discussed from the viewpoint of the emergency physician, neurocritical and cardiovascular critical care intensivist, hematologist, trauma and acute care surgeon, hospitalist, cardiologist, electrophysiologist, and pharmacist in an approach we hope that the reader will find extremely practical and clinically useful. The clinician learner will also find the discussion of the resumption of oral anticoagulation for the patient with severe bleeding after effective treatment important because returning the patient to an anticoagulated state as soon as feasible and safe prevents thrombotic complications. Finally, an EMCREG-International Severe Bleeding Consensus Panel algorithm for the approach to management of patients with life-threatening oral anticoagulant-associated bleeding is provided for the clinician and can be expanded in size for use in a treatment area such as the emergency department or critical care unit.

The European guideline on management of major bleeding and coagulopathy following trauma: fifth edition

BACKGROUND

Severe traumatic injury continues to present challenges to healthcare systems around the world, and post-traumatic bleeding remains a leading cause of potentially preventable death among injured patients. Now in its fifth edition, this document aims to provide guidance on the management of major bleeding and coagulopathy following traumatic injury and encourages adaptation of the guiding principles described here to individual institutional circumstances and resources.

METHODS

The pan-European, multidisciplinary Task Force for Advanced Bleeding Care in Trauma was founded in 2004, and the current author group included representatives of six relevant European professional societies. The group applied a structured, evidence-based consensus approach to address scientific queries that served as the basis for each recommendation and supporting rationale. Expert opinion and current clinical practice were also considered, particularly in areas in which randomised clinical trials have not or cannot be performed. Existing recommendations were re-examined and revised based on scientific evidence that has emerged since the previous edition and observed shifts in clinical practice. New recommendations were formulated to reflect current clinical concerns and areas in which new research data have been generated.

RESULTS

Advances in our understanding of the pathophysiology of post-traumatic coagulopathy have supported improved management strategies, including evidence that early, individualised goal-directed treatment improves the outcome of severely injured patients. The overall organisation of the current guideline has been designed to reflect the clinical decision-making process along the patient pathway in an approximate temporal sequence. Recommendations are grouped behind the rationale for key decision points, which are patient- or problem-oriented rather than related to specific treatment modalities. While these recommendations provide guidance for the diagnosis and treatment of major bleeding and coagulopathy, emerging evidence supports the author group's belief that the greatest outcome improvement can be achieved through education and the establishment of and adherence to local clinical management algorithms.

CONCLUSIONS

A multidisciplinary approach and adherence to evidence-based guidance are key to improving patient outcomes. If incorporated into local practice, these clinical practice guidelines have the potential to ensure a uniform standard of care across Europe and beyond and better outcomes for the severely bleeding trauma patient.

Early identification of trauma patients in need for emergent transfusion: results of a single-center retrospective study evaluating three scoring systems

BACKGROUND

The Trauma-Induced Coagulopathy Clinical Score (TICCS) was developed to be calculable on the site of injury to discriminate between trauma patients with or without the need for damage control resuscitation and thus transfusion. This early alert could then be translated to in-hospital parameters at patient arrival. Base excess (BE) and ultrasound (FAST) are known to be predictive parameters for emergent transfusion. We emphasize that adding these two parameters to the TICCS could improve the scoring system predictability.

METHODS

A retrospective study was conducted in the University Hospital of Liège. TICCS was calculated for every patient. BE and FAST results were recorded and points were added to the TICCS according to the TICCS.BE definition (+ 3 points if BE < - 5 and + 3 points in case of a positive FAST). Emergent transfusion was defined as the use of at least one blood product in the resuscitation room. The capacity of the TICCS, the TICCS.BE and the Trauma-Associated Severe Hemorrhage (TASH) to predict emergent transfusion was assessed.

RESULTS

A total of 328 patients were included. Among them, 14% needed emergent transfusion. The probability for emergent transfusion grows with the TICCS and the TICCS.BE values. We did not find a significant difference between the TICCS (AUC 0.73) and the TICCS.BE (AUC 0.76). The TASH proved to be more predictive (AUC 0.89). 66.6% of the patients with a TICCS ≥ 10 and 81.5% with a TICCS.BE ≥ 14 required emergent transfusion.

CONCLUSION

Adding BE and FAST to the original TICCS does not significantly improve the scoring system predictability. A prehospital TICCS > 10 could be used as a trigger for emergent transfusion activation. TASH could then be used at hospital arrival. Prehospital TASH calculation may be possible but should be further investigated.

LEVEL OF EVIDENCE

Diagnostic test, level III.

Prehospital parameters can help to predict coagulopathy and massive transfusion in trauma patients

BACKGROUND

This study aimed to evaluate the accuracy of prehospital parameters, including vital signs and resuscitation (fluids, vasopressor), to predict trauma-induced coagulopathy (TIC, fibrinogen <1·5 g/l or PT_ratio > 1·5 or platelet count <100 × 10⁹ /l), and a massive transfusion (MT, ≥10 RBC units within the first 24 h).

METHODS

From a trauma registry (2011-2015), in which patients are prospectively included, we retrospectively retrieved the heart rate (HR), systolic blood pressure (SBP), volume of prehospital fluids and administration of noradrenaline. We calculated the shock index (SI: HR/SBP), the MGAP prehospital triage score and the Injury Severity Score (ISS). We also identified patients who had positive criteria from the Resuscitation Outcome Consortium (ROC, SBP < 70 mmHg or SBP 70-90 and HR > 107 pulse/min). For these parameters, we drew a ROC curve and defined a cut-off value to predict TIC or MT. The strength of association between prehospital parameters and TIC as well as MT was assessed using logistic regression, and cut-off values were determined using ROC curves.

RESULTS

Among the 485 patients included in the study, TIC was observed in 112 patients (23%) and MT in 22 patients (5%). For the prediction of TIC, ISS had good accuracy (AUC: 0·844, 95% confidence interval, CI: 0·799-0·879), as did the volume of fluids (>1000 ml) given during prehospital care (AUC: 0·801, 95% CI: 0·752-0·842). For the prediction of MT, ISS had excellent accuracy (AUC: 0·932, 95% CI: 0·866-0·966), whereas good accuracy was found for SI (> 0·9; AUC: 0·859, 95% CI: 0·705-0·936), vasopressor administration (AUC: 0·828, 95% CI: 0·736-0·890) and fluids (>1000 ml; AUC: 0·811, 95% CI: 0·737-0·867). Vasopressor administration, ISS and SI were independent predictors of TIC and MT, whereas fluid volume and ROC criteria were independent predictor of TIC but not MT. No independent relationship was found between MGAP and TIC or MT.

CONCLUSIONS

Prehospital parameters including the SI and resuscitation may help to better identify the severity of bleeding in trauma patients and the need for blood product administration at admission.

Thromboelastometry and Thrombelastography Analysis under Normal Physiological Conditions - Systematic Review

BACKGROUND

Studies investigating thromboelastometry or thrombelastography analyses in a physiological context are scattered and not easy to access.

OBJECTIVE

To systematically retrieve and describe published reports studying healthy subjects and targeting at the correlation of ROTEM® and TEG® measurements with conventional parameters of hemostasis.

METHODS

Systematic Review: Papers were searched in Medline, Scopus and the Science Citation Index database. Reference lists of included studies and of reviews were screened. To be included papers had to report ROTEM or TEG data on healthy subjects. Two reviewers screened papers for inclusion, read full texts of potentially relevant papers, and extracted data of included papers.

RESULTS

Searches identified 1,721 records of which 1,713 were either excluded immediately or after reading the full text. The remaining 8 studies enrolled 632 subjects. The association of conventional parameters of hemostasis with ROTEM and with TEG was investigated in one and two studies, respectively. Overall correlation was limited and ranged from 0.0 to 0.40 (total thrombus generation vs. fibrinogen; clotting time INTEM vs. activated partial thromboplastin time).

CONCLUSIONS

Studies assessing the relationship between thromboelastometry or thromboelastography analyses and conventional parameters of hemostasis in healthy subjects remains scarce, and correlations are limited. Further research is needed to understand the physiology of thromboelastometry and thromboelastography parameters.