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Background Cervical spine immobilisation increases the difficulty of tracheal intubation. Many intubation devices have been evaluated in this setting, but their relative performance remains uncertain. Methods MEDLINE, EMBASE, and the Cochrane Library were searched to identify randomised trials comparing two or more intubation devices in adults with cervical spine immobilisation. After critical appraisal, a random-effects network meta-analysis was used to pool and compare device performance. The primary outcome was the probability of first-attempt intubation success (first-pass success). For relative performance, the Macintosh direct laryngoscopy blade was chosen as the reference device. Results We included 80 trials (8039 subjects) comparing 26 devices. Compared with the Macintosh, McGrath™ (odds ratio [OR]=11.5; 95% credible interval [CrI] 3.19–46.20), C-MAC D Blade™ (OR=7.44; 95% CrI, 1.06–52.50), Airtraq™ (OR=5.43; 95% CrI, 2.15–14.2), King Vision™ (OR=4.54; 95% CrI, 1.28–16.30), and C-MAC™ (OR=4.20; 95% CrI=1.28–15.10) had a greater probability of first-pass success. This was also true for the GlideScope™ when a tube guide was used (OR=3.54; 95% CrI, 1.05–12.50). Only the Airway Scope™ had a better probability of first-pass success compared with the Macintosh when manual-in-line stabilisation (MILS) was used as the immobilisation technique (OR=7.98; 95% CrI, 1.06–73.00). Conclusions For intubation performed with cervical immobilisation, seven devices had a better probability of first-pass success compared with the Macintosh. However, more studies using MILS (rather than a cervical collar or other alternative) are needed, which more accurately represent clinical practice. Clinical trial registration PROSPERO 2019 CRD42019158067 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=158067).

This paper is one in a series of analyses of the Dutch Simonshaven murder case, each using a different modeling approach. We adopted a Bayesian network (BN)-based approach which requires us to determine the relevant hypotheses and evidence in the case and their relationships (captured as a directed acyclic graph) along with explicit prior conditional probabilities. This means that both the graph structure and probabilities had to be defined using subjective judgments about the causal, and other, connections between variables and the strength and nature of the evidence. Determining if a useful BN could be quickly constructed by a small group using the previously established idioms-based approach which provides a generic method for translating legal cases into BNs, was a key aim. The model described was built by the authors during a workshop dedicated to the case at the Isaac Newton Institute, Cambridge, in September 2016. The total effort involved was approximately 26 h (i.e., an average of 6 h per author). With the basic assumptions described in the paper, the posterior probability of guilt once all the evidence is entered is 74%. The paper describes a formal evaluation of the model, using sensitivity analysis, to determine how robust the model conclusions are to key subjective prior probabilities over a full range of what may be deemed "reasonable" from both defense and prosecution perspectives. The results show that the model is reasonably robust-pointing not only generally to a reasonably high posterior probability of guilt but also generally below the 95% threshold expected in criminal law. Given the constraints on building a complex model so quickly, there are inevitably weaknesses; hence, the paper describes these and how they might be addressed, including how to take account of supplementary case information not known at the time of the workshop.

Estimating the likely success of limb revascularization in patients with lower-extremity arterial trauma is central to decisions between attempting limb salvage and amputation. However, the projected outcome is often unclear at the time these decisions need to be made, making them difficult and threatening sound judgement. The objective of this study was to develop and validate a prediction model that can quantify an individual patient's risk of failed revascularization.A BN prognostic model was developed using domain knowledge and data from the US joint trauma system. Performance (discrimination, calibration, and accuracy) was tested using ten-fold cross validation and externally validated on data from the UK Joint Theatre Trauma Registry. BN performance was compared to the mangled extremity severity score.Rates of amputation performed because of nonviable limb tissue were 12.2% and 19.6% in the US joint trauma system (n = 508) and UK Joint Theatre Trauma Registry (n = 51) populations respectively. A 10-predictor BN accurately predicted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, calibration slope 1.96, Brier score (BS) 0.05, and Brier skill score 0.50. The model maintained excellent performance in an external validation population: AUROC 0.97, calibration slope 1.72, Brier score 0.08, Brier skill score 0.58, and had significantly better performance than mangled extremity severity score at predicting the need for amputation [AUROC 0.95 (0.92-0.98) vs 0.74 (0.67-0.80); P0.0001].A BN (https://www.traumamodels.com) can accurately predict the outcome of limb revascularization at the time of initial wound evaluation. This information may complement clinical judgement, support rational and shared treatment decisions, and establish sensible treatment expectations.

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.

Objective To describe the long-term outcomes of military lower-extremity vascular injuries, and the decision making of surgeons treating these injuries. Background Lower-extremity vascular trauma is an important cause of preventable death and severe disability, and decisions on amputation or limb salvage can be difficult. Additionally, the complexity of the condition is not amenable to controlled study, and there is limited data to guide clinical decision making and establish sensible treatment expectations during rehabilitation. Methods A cohort study of 554 US service members who sustained lower-extremity vascular injury in Iraq or Afghanistan (March 2003 to February 2012) was performed using the military's trauma registry, its electronic health record, patient interviews, and quality-of-life surveys. Long-term surgical and functional outcomes, and the timing and rationale of surgical decisions, were analyzed. Results Of 579 injured extremities, 49 (8.5%) underwent primary amputation and 530 (91.5%) an initial attempt at salvage. Ninety extremities underwent secondary amputation, occurring in the early (n = 60; 30 days) phases after injury. For salvage attempts, freedom from amputation 10 years after injury was 82.7% (79.1%-85.7%). Long-term physical and mental health outcomes were similar between service members who underwent reconstruction and those who underwent amputation. Conclusion This military experience provides data that will inform an array of military and civilian providers who care for patients with severe lower-extremity injury. While the majority salvage attempts endure, success is hindered by ischemia and necrosis during the acute stage and pain, dysfunction and infection in the later phases of recovery. Level of evidence Therapeutic/prognostic, level III.

Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model's performance.

Display Omitted We focus on complex clinical problems where data is available in small amounts.Our methodology compensates for the lack of data by using published evidence.It combines multivariate data with univariate meta-analysis to build BN models.The method is illustrated by a medical case study on trauma care.Our method outperformed data-driven techniques and MESS model in the case study. Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report 'univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model - ignoring some complexities of the problem - or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.

Nonoperative management (NOM) of hemodynamically normal patients with blunt splenic injury (BSI) is the standard of care. Guidelines recommend additional splenic angioembolization (SAE) in patients with American Association for the Surgery of Trauma (AAST) Grade IV and Grade V BSI, but the role of SAE in Grade III injuries is unclear and controversial. The aim of this systematic review was to compare the safety and effectiveness of SAE as an adjunct to NOM versus NOM alone in adults with BSI.A systematic literature search (Medline, Embase, and CINAHL) was performed to identify original studies that compared outcomes in adult BSI patients treated with SAE or NOM alone. Primary outcome was failure of NOM. Secondary outcomes included morbidity, mortality, hospital length of stay, and transfusion requirements. Bayesian meta-analyses were used to calculate an absolute (risk difference) and relative (risk ratio [RR]) measure of treatment effect for each outcome.Twenty-three studies (6,684 patients) were included. For Grades I to V combined, there was no difference in NOM failure rate (SAE, 8.6% vs NOM, 7.7%; RR, 1.09 [0.80-1.51]; p = 0.28), mortality (SAE, 4.8% vs NOM, 5.8%; RR, 0.82 [0.45-1.31]; p = 0.81), hospital length of stay (11.3 vs 9.5 days; p = 0.06), or blood transfusion requirements (1.8 vs 1.7 units; p = 0.47) between patients treated with SAE and those treated with NOM alone. However, morbidity was significantly higher in patients treated with SAE (SAE, 38.1% vs NOM, 18.6%; RR, 1.83 [1.20-2.66]; p0.01). When stratified by grade of splenic injury, SAE significantly reduced the failure rate of NOM in patients with Grade IV and Grade V splenic injuries but had minimal effect in those with Grade I to Grade III injuries.Splenic angioembolization should be strongly considered as an adjunct to NOM in patients with AAST Grade IV and Grade V BSI but should not be routinely recommended in patients with AAST Grade I to Grade III injuries.Systematic review and meta-analysis, level III.

Graphical abstractDisplay Omitted We focus on latent clinical factors that can only be indirectly observed.We propose a methodology of developing BNs that reason with latent variables.A series of expert reviews reveal the relation between data and latent variables.The method is illustrated by a medical case study on trauma care.The case study displays significant predictive improvements from the expert reviews. Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.

A method based on the underlying principle of Value of Information analysis.For interventional and counterfactual Bayesian networks in forensic medical sciences.Examines whether interventional decision making is subject to amendments.Assesses what further information is worthwhile seeking prior to decision making. ObjectivesInspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. MethodThe method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. ResultsThe method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). ConclusionsWe have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.

Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.

Background Lower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision-making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT. Methods A systematic review was conducted to identify potential prognostic factors. Bayesian meta-analysis was used to calculate an absolute (pooled proportion) and relative (pooled odds ratio, OR) measure of the amputation risk for each factor. Results Forty-five studies, totalling 3187 discrete LEVT repairs, were included. The overall amputation rate was 10·0 (95 per cent credible interval 7·4 to 13·1) per cent. Significant prognostic factors for secondary amputation included: associated major soft tissue injury (26 versus 8 per cent for no soft tissue injury; OR 5·80), compartment syndrome (28 versus 6 per cent; OR 5·11), multiple arterial injuries (18 versus 9 per cent; OR 4·85), duration of ischaemia exceeding 6 h (24 versus 5 per cent; OR 4·40), associated fracture (14 versus 2 per cent; OR 4·30), mechanism of injury (blast 19 per cent, blunt 16 per cent, penetrating 5 per cent), anatomical site of injury (iliac 18 per cent, popliteal 14 per cent, tibial 10 per cent, femoral 4 per cent), age over 55 years (16 versus 9 per cent; OR 3·03) and sex (men 7 per cent versus women 8 per cent; OR 0·64). Shock and nerve or venous injuries were not significant prognostic factors for secondary amputation. Conclusion A significant proportion of patients who undergo lower extremity vascular trauma repair will require secondary amputation. This meta-analysis describes significant prognostic factors needed to inform surgical judgement, risk assessment and patient counselling.