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

Medicina ◽  
2019 ◽  
Vol 55 (10) ◽  
pp. 653 ◽  
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.

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

2020 ◽  
Vol 26 ◽  
pp. 107602961989782 ◽  
Author(s):  
Kaiyuan Li ◽  
Huitao Wu ◽  
Fei Pan ◽  
Li Chen ◽  
Cong Feng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Classification Accuracy ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Recall Score ◽  
Trauma Patients ◽  
Acute Traumatic Coagulopathy ◽  
Emergency Rescue ◽  
Traumatic Coagulopathy

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.

scholarly journals Activated Protein C Drives the Hyperfibrinolysis of Acute Traumatic Coagulopathy

2017 ◽  
Vol 126 (1) ◽  
pp. 115-127 ◽  
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.

scholarly journals A systematic review of the quality of clinical prediction models in in vitro fertilisation

2020 ◽  
Vol 35 (1) ◽  
pp. 100-116 ◽  
Author(s):  
M B Ratna ◽  
S Bhattacharya ◽  
B Abdulrahim ◽  
D J McLernon
Keyword(s):  
Systematic Review ◽  
Live Birth ◽  
Prediction Models ◽  
Conflicts Of Interest ◽  
External Validation ◽  
Post Treatment ◽  
Treatment Model ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Abstract STUDY QUESTION What are the best-quality clinical prediction models in IVF (including ICSI) treatment to inform clinicians and their patients of their chance of success? SUMMARY ANSWER The review recommends the McLernon post-treatment model for predicting the cumulative chance of live birth over and up to six complete cycles of IVF. WHAT IS KNOWN ALREADY Prediction models in IVF have not found widespread use in routine clinical practice. This could be due to their limited predictive accuracy and clinical utility. A previous systematic review of IVF prediction models, published a decade ago and which has never been updated, did not assess the methodological quality of existing models nor provided recommendations for the best-quality models for use in clinical practice. STUDY DESIGN, SIZE, DURATION The electronic databases OVID MEDLINE, OVID EMBASE and Cochrane library were searched systematically for primary articles published from 1978 to January 2019 using search terms on the development and/or validation (internal and external) of models in predicting pregnancy or live birth. No language or any other restrictions were applied. PARTICIPANTS/MATERIALS, SETTING, METHODS The PRISMA flowchart was used for the inclusion of studies after screening. All studies reporting on the development and/or validation of IVF prediction models were included. Articles reporting on women who had any treatment elements involving donor eggs or sperm and surrogacy were excluded. The CHARMS checklist was used to extract and critically appraise the methodological quality of the included articles. We evaluated models’ performance by assessing their c-statistics and plots of calibration in studies and assessed correct reporting by calculating the percentage of the TRIPOD 22 checklist items met in each study. MAIN RESULTS AND THE ROLE OF CHANCE We identified 33 publications reporting on 35 prediction models. Seventeen articles had been published since the last systematic review. The quality of models has improved over time with regard to clinical relevance, methodological rigour and utility. The percentage of TRIPOD score for all included studies ranged from 29 to 95%, and the c-statistics of all externally validated studies ranged between 0.55 and 0.77. Most of the models predicted the chance of pregnancy/live birth for a single fresh cycle. Six models aimed to predict the chance of pregnancy/live birth per individual treatment cycle, and three predicted more clinically relevant outcomes such as cumulative pregnancy/live birth. The McLernon (pre- and post-treatment) models predict the cumulative chance of live birth over multiple complete cycles of IVF per woman where a complete cycle includes all fresh and frozen embryo transfers from the same episode of ovarian stimulation. McLernon models were developed using national UK data and had the highest TRIPOD score, and the post-treatment model performed best on external validation. LIMITATIONS, REASONS FOR CAUTION To assess the reporting quality of all included studies, we used the TRIPOD checklist, but many of the earlier IVF prediction models were developed and validated before the formal TRIPOD reporting was published in 2015. It should also be noted that two of the authors of this systematic review are authors of the McLernon model article. However, we feel we have conducted our review and made our recommendations using a fair and transparent systematic approach. WIDER IMPLICATIONS OF THE FINDINGS This study provides a comprehensive picture of the evolving quality of IVF prediction models. Clinicians should use the most appropriate model to suit their patients’ needs. We recommend the McLernon post-treatment model as a counselling tool to inform couples of their predicted chance of success over and up to six complete cycles. However, it requires further external validation to assess applicability in countries with different IVF practices and policies. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Elphinstone Scholarship Scheme and the Assisted Reproduction Unit, University of Aberdeen. Both D.J.M. and S.B. are authors of the McLernon model article and S.B. is Editor in Chief of Human Reproduction Open. They have completed and submitted the ICMJE forms for Disclosure of potential Conflicts of Interest. The other co-authors have no conflicts of interest to declare. REGISTRATION NUMBER N/A

Acute traumatic coagulopathy in the setting of isolated traumatic brain injury: A systematic review and meta-analysis

Injury ◽  
2014 ◽  
Vol 45 (5) ◽  
pp. 819-824 ◽  
Author(s):  
Daniel S. Epstein ◽  
Biswadev Mitra ◽  
Gerard O’Reilly ◽  
Jeffrey V. Rosenfeld ◽  
Peter A. Cameron
Keyword(s):  
Systematic Review ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Meta Analysis ◽  
Acute Traumatic Coagulopathy ◽  
Traumatic Coagulopathy

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

Trauma ◽  
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.

Plasma ADAMTS13 Activity Associates with Injury Severity in Pediatric Trauma Patients

Blood ◽  
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.

scholarly journals Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

2016 ◽  
Vol 24 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Benjamin A Goldstein ◽  
Ann Marie Navar ◽  
Michael J Pencina ◽  
John P A Ioannidis
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Model Building ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Health Records ◽  
Clinical Risk ◽  
Electronic Health ◽  
And Performance

Objective: Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. Methods: We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). Conclusions: EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.

Sign in / Sign up

Export Citation Format

Share Document