Bayesian Network Decision-Support for Severe Lower Limb Trauma

PhDSevere lower limb injuries are potentially devastating and pose some of the most difficult decisions in trauma surgery. The goal is to ensure survival and reconstruct the most functional limb possible. Ideally this is achieved by salvaging the injured limb. However, in certain situations amputation is the safest and most effective method of achieving an optimal outcome. Errors in these decisions may have profound consequences, yet they are frequently based on incomplete information and uncertain risks. Furthermore, most surgeons have limited experience making these decisions, and existing decision-support tools are unhelpful. The aim of this thesis was to improve the understanding of decision-making following severe lower limb trauma, and develop accurate prognostic models that can help identify those patients whose limb can be safely and effectively salvaged, and also identify those for whom attempts at limb salvage would be dangerous or fail. The rationale for amputation decisions was analysed in a cohort of severe lower limb injuries (n = 579). Two prognostic models were designed to support difficult aspects of these decisions. Both models were developed using Bayesian networks that combine existing knowledge with individual patient data. The first provides early and accurate identification (AUROC = 0.927) of patients at risk of Trauma-Induced Coagulopathy, the principal indication for damage-control intervention. The model’s performance in new patients, and ability to handle missing predictor information, was prospectively validated. The second model accurately predicts the likely outcome, in terms of viability, of attempted limb salvage. This model outperformed the most widely used decision-support tool, the Mangled Extremity Severity Score (AUROC 0.932 versus 0.723; P < 0.0001). These Bayesian network tools accurately quantify critical risks that make rational judgement on the safety and effectiveness of interventions possible. This information enables individualised and evidence-based decisions, at a time when decision-making is most effective.The Academic Department of Military Surgery & Trauma (ADMST), the Royal Centre for Defence Medicine, for funding this research

A systematic review and meta-analysis comparing mortality in pre-hospital tracheal intubation to emergency department intubation in trauma patients

Background Pre-hospital endotracheal intubation is frequently used for trauma patients in many emergency medical systems. Despite a wide range of publications in the field, it is debated whether the intervention is associated with a favourable outcome, when compared to more conservative airway measures. Methods A systematic literature search was conducted to identify interventional and observational studies where the mortality rates of adult trauma patients undergoing pre-hospital endotracheal intubation were compared to those undergoing emergency department intubation. Results Twenty-one studies examining 35,838 patients were included. The median mortality rate in patients undergoing pre-hospital intubation was 48% (range 8–94%), compared to 29% (range 6–67%) in patients undergoing intubation in the emergency department. Odds ratios were in favour of emergency department intubation both in crude and adjusted mortality, with 2.56 (95% CI: 2.06, 3.18) and 2.59 (95% CI: 1.97, 3.39), respectively. The overall quality of evidence is very low. Twelve of the twenty-one studies found a significantly higher mortality rate after pre-hospital intubation, seven found no significant differences, one found a positive effect, and for one study an analysis of the mortality rate was beyond the scope of the article. Conclusions The rationale for wide and unspecific indications for pre-hospital intubation seems to lack support in the literature, despite several publications involving a relatively large number of patients. Pre-hospital intubation is a complex intervention where guidelines and research findings should be approached cautiously. The association between pre-hospital intubation and a higher mortality rate does not necessarily contradict the importance of the intervention, but it does call for a thorough investigation by clinicians and researchers into possible causes for this finding.publishedVersio

Development of a discrete event simulation model for evaluating strategies of red blood cell provision following mass casualty events

Timely and adequate provision of blood following mass casualty events (MCEs) is critical to reducing mortality rates amongst casualties transported to hospital following an event. Developing planning strategies to ensure the blood transfusion demands of casualties are met is challenging. Discrete event simulation (DES) offers a novel solution to this problem which is financially efficient, less disruptive to services and allows for rich experimentation compared to the current industry standards of live exercises, round-table discussion or tabletop planning. There are currently no published models of this type for investigating blood provision in MCEs. The objective of this study was to develop a working model which could be used to target the in-hospital 'levers' and 'supply levels' of the transfusion system and improve outcomes during the response to future events. This was achieved through the robust design of a DES model using exclusive access to qualitative and quantitative data as well as a panel of experts from the field of transfusion and MCE management. The completed model was extensively and formally evaluated with secondary data from the 7th of July 2005 London bombings, the largest UK based civilian MCE in over 50 years. A subsequent sensitivity analysis revealed the five factors displaying the greatest influence on casualty outcomes. Experimental themes based on these findings have generated new solutions for managing future events which have since been presented to MCE stakeholders and policy makers

Managing the surge in demand for blood following mass casualty events. Early automatic restocking may preserve red cell supply

Background: Traumatic hemorrhage is a leading preventable cause of mortality following mass casualty events (MCEs). Improving outcomes requires adequate in-hospital provision of high volume red blood cell (RBC) transfusions. This study investigated strategies for optimizing RBC provision to casualties in MCEs using simulation modeling. Methods: A computerized simulation model of a UK major trauma centre (TC) transfusion system was developed. The model used input data from past MCEs, civilian and military trauma registries. We simulated the effect of varying on-shelf RBC stock hold and the timing of externally restocking RBC supplies on TC treatment capacity across increasing loads of priority one (P1) and two (P2) casualties from an event. Results: 35,000 simulations were performed. A casualty load of 20 P1&amp;2s under standard TC RBC stock conditions left 35% (95% CI 32-38) of P1s and 7% (4-10) of P2s inadequately treated for hemorrhage. Additionally, exhaustion of type O emergency RBC stocks (a surrogate for reaching surge capacity) occurred in a median of 10 hours (IQR 5-&gt;12). Doubling casualty load increased this to 60% (57-63) and 30% (26-34) respectively with capacity reached in 2hours (1-3). The model identified a minimum requirement of 12U of on-shelf RBCs per P1/2 casualty received to prevent surge capacity being reached. Restocking supplies in an MCE versus greater permanent on-shelf RBC stock holds was considered at increasing hourly intervals. T-test analysis showed no difference between stock hold versus supply restocking in terms of overall outcomes for MCEs up to 80 P1&amp;2s in size (p&lt;0.05), provided the restock occurred within 6 hours. Conclusion: Even limited sized MCEs threaten to overwhelm TC transfusion systems. An earlyautomated push approach to restocking RBCs initiated by central suppliers can produce equivocal outcomes compared with holding excess stock permanently at TCs

Understanding prehospital blood transfusion decision-making for injured patients : an interview study

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.Peer reviewe

Clinical evidence framework for Bayesian networks

There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care

All-sky Medium Energy Gamma-ray Observatory: Exploring the Extreme Multimessenger Universe

The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a probe class mission concept that will provide essential contributions to multimessenger astrophysics in the late 2020s and beyond. AMEGO combines high sensitivity in the 200 keV to 10 GeV energy range with a wide field of view, good spectral resolution, and polarization sensitivity. Therefore, AMEGO is key in the study of multimessenger astrophysical objects that have unique signatures in the gamma-ray regime, such as neutron star mergers, supernovae, and flaring active galactic nuclei. The order-of-magnitude improvement compared to previous MeV missions also enables discoveries of a wide range of phenomena whose energy output peaks in the relatively unexplored medium-energy gamma-ray band