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.

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

Medicina ◽

10.3390/medicina55100653 ◽

2019 ◽

Vol 55 (10) ◽

pp. 653 ◽

Cited By ~ 2

Author(s):

Thorn ◽

Güting ◽

Maegele ◽

Gruen ◽

Mitra

Keyword(s):

Systematic Review ◽

Blood Pressure ◽

Prediction Models ◽

Scoring Systems ◽

Trauma Patients ◽

Clinical Prediction ◽

Acute Traumatic Coagulopathy ◽

Prediction Tools ◽

Populations At Risk ◽

Traumatic Coagulopathy

: 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.

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A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.234 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

T Heseltine ◽

SW Murray ◽

RL Jones ◽

M Fisher ◽

B Ruzsics

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Coronary Artery ◽

Regression Model ◽

Coronary Artery Calcium ◽

Logistic Regression Model ◽

Prediction Models ◽

Multinomial Logistic Regression ◽

Cvd Risk ◽

Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score >400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score >400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

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Activated Protein C Drives the Hyperfibrinolysis of Acute Traumatic Coagulopathy

Anesthesiology ◽

10.1097/aln.0000000000001428 ◽

2017 ◽

Vol 126 (1) ◽

pp. 115-127 ◽

Cited By ~ 60

Author(s):

Ross A. Davenport ◽

Maria Guerreiro ◽

Daniel Frith ◽

Claire Rourke ◽

Sean Platton ◽

...

Keyword(s):

Murine Model ◽

Protein C ◽

Major Trauma ◽

Activated Protein C ◽

Genetic Mutation ◽

Trauma Patients ◽

Acute Traumatic Coagulopathy ◽

Rotational Thromboelastometry ◽

Level 1 Trauma Center ◽

Traumatic Coagulopathy

Abstract Background Major trauma is a leading cause of morbidity and mortality worldwide with hemorrhage accounting for 40% of deaths. Acute traumatic coagulopathy exacerbates bleeding, but controversy remains over the degree to which inhibition of procoagulant pathways (anticoagulation), fibrinogen loss, and fibrinolysis drive the pathologic process. Through a combination of experimental study in a murine model of trauma hemorrhage and human observation, the authors’ objective was to determine the predominant pathophysiology of acute traumatic coagulopathy. Methods First, a prospective cohort study of 300 trauma patients admitted to a single level 1 trauma center with blood samples collected on arrival was performed. Second, a murine model of acute traumatic coagulopathy with suppressed protein C activation via genetic mutation of thrombomodulin was used. In both studies, analysis for coagulation screen, activated protein C levels, and rotational thromboelastometry (ROTEM) was performed. Results In patients with acute traumatic coagulopathy, the authors have demonstrated elevated activated protein C levels with profound fibrinolytic activity and early depletion of fibrinogen. Procoagulant pathways were only minimally inhibited with preservation of capacity to generate thrombin. Compared to factors V and VIII, proteases that do not undergo activated protein C–mediated cleavage were reduced but maintained within normal levels. In transgenic mice with reduced capacity to activate protein C, both fibrinolysis and fibrinogen depletion were significantly attenuated. Other recognized drivers of coagulopathy were associated with less significant perturbations of coagulation. Conclusions Activated protein C–associated fibrinolysis and fibrinogenolysis, rather than inhibition of procoagulant pathways, predominate in acute traumatic coagulopathy. In combination, these findings suggest a central role for the protein C pathway in acute traumatic coagulopathy and provide new translational opportunities for management of major trauma hemorrhage.

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Machine Learning-based in-hospital Mortality Prediction Models for Patients With Acute Coronary Syndrome

10.21203/rs.3.rs-134944/v1 ◽

2020 ◽

Author(s):

Jun Ke ◽

Yiwei Chen ◽

Xiaoping Wang ◽

Zhiyong Wu ◽

qiongyao Zhang ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Hospital Mortality ◽

Operating Characteristic ◽

Prediction Models ◽

Characteristic Curve ◽

Multivariate Logistic Regression Analysis ◽

Hdl Cholesterol ◽

Coronary Syndrome

Abstract BackgroundThe purpose of this study is to identify the risk factors of in-hospital mortality in patients with acute coronary syndrome (ACS) and to evaluate the performance of traditional regression and machine learning prediction models.MethodsThe data of ACS patients who entered the emergency department of Fujian Provincial Hospital from January 1, 2017 to March 31, 2020 for chest pain were retrospectively collected. The study used univariate and multivariate logistic regression analysis to identify risk factors for in-hospital mortality of ACS patients. The traditional regression and machine learning algorithms were used to develop predictive models, and the sensitivity, specificity, and receiver operating characteristic curve were used to evaluate the performance of each model.ResultsA total of 7810 ACS patients were included in the study, and the in-hospital mortality rate was 1.75%. Multivariate logistic regression analysis found that age and levels of D-dimer, cardiac troponin I, N-terminal pro-B-type natriuretic peptide (NT-proBNP), lactate dehydrogenase (LDH), high-density lipoprotein (HDL) cholesterol, and calcium channel blockers were independent predictors of in-hospital mortality. The study found that the area under the receiver operating characteristic curve of the models developed by logistic regression, gradient boosting decision tree (GBDT), random forest, and support vector machine (SVM) for predicting the risk of in-hospital mortality were 0.963, 0.960, 0.963, and 0.959, respectively. Feature importance evaluation found that NT-proBNP, LDH, and HDL cholesterol were top three variables that contribute the most to the prediction performance of the GBDT model and random forest model.ConclusionsThe predictive model developed using logistic regression, GBDT, random forest, and SVM algorithms can be used to predict the risk of in-hospital death of ACS patients. Based on our findings, we recommend that clinicians focus on monitoring the changes of NT-proBNP, LDH, and HDL cholesterol, as this may improve the clinical outcomes of ACS patients.

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Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

PeerJ ◽

10.7717/peerj.7259 ◽

2019 ◽

Vol 7 ◽

pp. e7259

Author(s):

Bongsong Kim

Keyword(s):

Logistic Regression ◽

Oryza Sativa ◽

Regression Model ◽

Classification Accuracy ◽

Logistic Regression Model ◽

Computational Method ◽

Phenotypic Data ◽

Separation Power

In Oryza sativa, indica and japonica are pivotal subpopulations, and other subpopulations such as aus and aromatic are considered to be derived from indica or japonica. In this regard, Oryza sativa accessions are frequently viewed from the indica/japonica perspective. This study introduces a computational method for indica/japonica classification by applying phenotypic variables to the logistic regression model (LRM). The population used in this study included 413 Oryza sativa accessions, of which 280 accessions were indica or japonica. Out of 24 phenotypic variables, a set of seven phenotypic variables was identified to collectively generate the fully accurate indica/japonica separation power of the LRM. The resulting parameters were used to define the customized LRM. Given the 280 indica/japonica accessions, the classification accuracy of the customized LRM along with the set of seven phenotypic variables was estimated by 100 iterations of ten-fold cross-validations. As a result, the classification accuracy of 100% was achieved. This suggests that the LRM can be an effective tool to analyze the indica/japonica classification with phenotypic variables in Oryza sativa.

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Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

Frontiers in Pediatrics ◽

10.3389/fped.2020.585868 ◽

2021 ◽

Vol 8 ◽

Author(s):

Robert A. Reed ◽

Andrei S. Morgan ◽

Jennifer Zeitlin ◽

Pierre-Henri Jarreau ◽

Héloïse Torchin ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Regression Model ◽

Expert Opinion ◽

Logistic Regression Model ◽

Population Based ◽

Regression Modelling ◽

Preterm Babies ◽

Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

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Relevance of Brain Regions’ Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

Neurology ◽

10.1212/wnl.0000000000012863 ◽

2021 ◽

pp. 10.1212/WNL.0000000000012863

Author(s):

Basile Kerleroux ◽

Joseph Benzakoun ◽

Kévin Janot ◽

Cyril Dargazanli ◽

Dimitri Daly Eraya ◽

...

Keyword(s):

Logistic Regression ◽

Random Forest ◽

Regression Model ◽

Logistic Regression Model ◽

Mechanical Thrombectomy ◽

Predictive Accuracy ◽

Unmet Need ◽

C Statistic ◽

Ischemic Core ◽

Functional Relevance

ObjectiveIndividualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need.We tested the hypothesis, that assessing the functional relevance of both the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT.MethodsMulticenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable if 0-3. Global and regional-eloquence-based core-perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap-random-forest models.ResultsA total of 138 patients with baseline LIC were included (MT n=96 or BMM n=42; mean age±SD, 72.4±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml respectively and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS=0-3 with a c-Statistic of 0.76 and 0.83 for logistic regression model and bootstrap-random-forest testing sets respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p=0.04). In comparison in the logistic regression model disregarding functional eloquence the c-Statistic was 0.67 and the interaction between MT and the mismatch was insignificant.ConclusionConsidering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

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

Trauma ◽

10.1177/1460408619838187 ◽

2019 ◽

Vol 22 (2) ◽

pp. 112-117

Author(s):

Sophie Thorn ◽

Martin Tonglet ◽

Marc Maegele ◽

Russell Gruen ◽

Biswadev Mitra

Keyword(s):

Cohort Study ◽

Early Identification ◽

Adverse Outcomes ◽

Trauma Patients ◽

Operating Characteristics ◽

Hospital Data ◽

Single Centre ◽

Acute Traumatic Coagulopathy ◽

Simple Method ◽

Traumatic Coagulopathy

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.

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Predicting mortality in hemodialysis patients using machine learning analysis

Clinical Kidney Journal ◽

10.1093/ckj/sfaa126 ◽

2020 ◽

Author(s):

Victoria Garcia-Montemayor ◽

Alejandro Martin-Malo ◽

Carlo Barbieri ◽

Francesco Bellocchio ◽

Sagrario Soriano ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Prediction Models ◽

Initial Period ◽

Area Under The Curve ◽

Mortality Prediction ◽

Machine Learning Techniques ◽

Prediction Of Mortality ◽

Mortality Prediction Models

Abstract Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

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Plasma ADAMTS13 Activity Associates with Injury Severity in Pediatric Trauma Patients

Blood ◽

10.1182/blood.v128.22.3777.3777 ◽

2016 ◽

Vol 128 (22) ◽

pp. 3777-3777

Author(s):

Jenny K. McDaniel ◽

Ilan I Maizlin ◽

Michelle C. Shroyer ◽

Morgan E. Banks ◽

Jean-Francois Pittet ◽

...

Keyword(s):

Injury Severity ◽

Pediatric Trauma ◽

Initial Presentation ◽

Trauma Patients ◽

Adamts13 Activity ◽

Acute Traumatic Coagulopathy ◽

Time Period ◽

Increased Risk ◽

Significant Difference ◽

Traumatic Coagulopathy

Abstract Background: Acute traumatic coagulopathy occurs in both pediatric and adult trauma patients and is associated with an increased risk of mortality. Trauma patients not only have increased risk for hemorrhagic complications, but also are at increased risk for thrombosis due to multiple factors including local tissue injury, inflammation, and immobility. The complex underlying pathophysiology of coagulation abnormalities associated with traumatic injury have yet to be fully elucidated. Additionally, there are significant differences in the hemostatic system of pediatric patients compared to adults. Objectives: The purpose of this study was to determine the levels of coagulation parameters including von Willebrand factor (VWF) antigen and ADAMTS13 activity in pediatric trauma patients and evaluate for possible association with injury severity and/or mortality. Methods: This study utilized plasma specimens collected from pediatric trauma patients that presented to our institution over a 2-year time period. The specimens were collected at initial presentation and 24 hours later. The injury severity was estimated using both the Glasgow Coma Scale (GCS) and Injury Severity Score (ISS). A cohort of control samples was obtained from pediatric patients for elective surgical procedures over the same time period. Plasma VWF antigen was determined by a sandwich ELISA; plasma ADAMTS13 activity was determined by FRETS-VWF73. The results were determined by nonparametric tests for the differences in median values. Results: A total of 106 trauma patient samples at initial time point, 78 trauma samples at 24 hour time point, and 54 control samples were obtained and utilized for study. There were statistically significant differences (p<0.05) in the plasma levels of VWF antigen, ADAMTS13 activity, and the ratio of ADAMTS13 activity to VWF antigen for the trauma patient samples at initial presentation when compared to controls (Table 1). At 24 hours, there were still statistically significant differences between ADAMTS13 activity and the ratio of ADAMTS13 activity to VWF antigen in trauma patients compared to controls, but there was no significant difference in VWF antigen between the two cohorts (Table 2). There was a significant difference between the decrease in ADAMTS13 activity and injury severity as estimated by ISS ³ 15 or GCS < 8 at both time points; however, ADAMTS13 activity was not statistically different in survivors vs. non-survivors. A higher VWF antigen level at initial presentation was the only factor found to be significantly different in non-survivors. Conclusions: This study demonstrates significant differences in plasma ADAMTS13 activity and VWF antigen in pediatric trauma patients compared to controls. In patients with more severe injuries as estimated by GCS and ISS, there was also a significant association with decreased levels of ADAMTS13 activity. These finding may underlie part of the prothrombotic propensity in microcirculation that occurs in patients post-trauma. Further investigation is warranted to better understand the mechanisms of acute traumatic coagulopathy and potential prognostic factors, and to determine the most effective interventions for acute traumatic coagulopathy in the pediatric population. Disclosures Zheng: Ablynx: Consultancy; Alexion: Research Funding.

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