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

Outcomes following trauma laparotomy for hypotensive trauma patients: A UK military and civilian perspective.

BACKGROUND: The management of trauma patients has changed radically in the last decade, and studies have shown overall improvements in survival. However, reduction in mortality for the many may obscure a lack of progress in some high-risk patients. We sought to examine the outcomes for hypotensive patients requiring laparotomy in UK military and civilian cohorts. METHODS: We undertook a review of two prospectively maintained trauma databases: the UK Joint Theatre Trauma Registry for the military cohort (February 4, 2003, to September 21, 2014) and the trauma registry of the Royal London Hospital major trauma center (January 1, 2012, to January 1, 2017) for civilian patients. Adults undergoing trauma laparotomy within 90 minutes of arrival at the emergency department (ED) were included. RESULTS: Hypotension was present on arrival at the ED in 155 (20.4%) of 761 military patients. Mortality was higher in hypotensive casualties (25.8% vs. 9.7% in normotensive casualties; p < 0.001). Hypotension was present on arrival at the ED in 63 (35.7%) of 176 civilian patients. Mortality was higher in hypotensive patients (47.6% vs. 12.4% in normotensive patients; p < 0.001). In both cohorts of hypotensive patients, neither the average injury severity, the prehospital time, the ED arrival systolic blood pressure, nor mortality rate changed significantly during the study period. CONCLUSIONS: Despite improvements in survival after trauma for patients overall, the mortality for patients undergoing laparotomy who arrive at the ED with hypotension has not changed and appears stubbornly resistant to all efforts. Specific enquiry and research should continue to be directed at this high-risk group of patients. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, level IV

Electives in undergraduate medical education: AMEE Guide No. 88

This Guide outlines the scope and potential roles an elective can contribute to undergraduate medical training and identifies ways to maximize learning opportunities, including within global health. The types of educational activity available for electives range from meeting individual educational need through to exploration of potential career pathways, with many factors influencing choice. Key areas of organization underpinning a successful elective before, during and after the placement include developing clarity of the intended educational outcomes as well as addressing practicalities such as travel and accommodation. Risk management including the implications for the participating schools as well as the student and their elective supervisors is crucial. This Guide would not be complete without some discussion around ethics and professional conduct during an elective, with consideration of the impact of elective placements, particularly in low-middle income countries

A Process for Evaluating Explanations for Transparent and Trustworthy AI Prediction Models

This study proposes a process to generate and validate algorithmic explanations for the reasoning of an AI prediction model, implemented using a Bayesian network (BN). The intention of the generated explanations is to increase the transparency and trustworthiness of a decision-support system that uses a BN prediction model. To achieve this, explanations should be presented in an easy-to-understand, clear, and concise natural language narrative. We have developed an algorithm for explaining the reasoning of a prediction made using a BN. For the narrative part of the explanation, we use a template which presents the 'content' part of the explanation; this content is a word-less information structure that applies to all BNs. The template, on the other hand, needs to be designed specifically for each BN model. In this paper, we use a BN for the risk of trauma-induced coagulopathy, a critical bleeding problem. We outline a process for using experts' explanations as the basis for designing the explanation template. We do not believe that an algorithmic explanation needs to be indistinguishable from expert explanations; instead we aim to imitate the narrative structure of explanations given by experts, although we find that there is considerable variation in these. We then consider how the generated explanations can be evaluated, since a direct comparison (in the style of a Turing test) would likely fail. We describe a study using questionnaires and interviews to evaluate the effect of an algorithmic explanation on the transparency and also on the trustworthiness of the predictions made by the system. The preliminary results of our study suggest that the presence of an explanation makes the AI model more transparent but not necessarily more trustworthy

Prehospital tranexamic acid shortens the interval to administration by half in Major Trauma Networks: a service evaluation

Fondazione Cassa Rurale di Trento, Italy