An Economic Evaluation of Triage Tools For Patients With Suspected Severe Injuries In England

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J. van Rein, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-504608/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2022 Read the published version in BMC Emergency Medicine → Version 1 posted 11 You are reading this latest preprint version Abstract Background Many health care systems triage injured patients to major trauma centres (MTCs) or local hospitals by using triage tools and paramedic judgement. Triage tools are typically assessed by whether patients with an Injury Severity Score (ISS)≥16 go to an MTC and whether patients with an ISS<16 are sent to their local hospital. There is a trade‐off between sensitivity and specificity of triage tools, with the optimal balance being unknown. We conducted an economic evaluation of major trauma triage tools to identify which tool would be considered cost-effective by UK decision makers. Methods A patient-level, probabilistic, mathematical model of a UK major trauma system was developed. Patients with an ISS≥16 who were only treated at local hospitals had worse outcomes compared to being treated in an MTC. Nine empirically derived triage tools, from a previous study, were examined so we assessed triage tools with realistic trade-offs between triage tool sensitivity and specificity. Lifetime costs, lifetime quality adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated for each tool and compared to maximum acceptable ICERs (MAICERs) in England. Results Four tools had ICERs within the normal range of MAICERs used by English decision makers (£20,000 to £30,000 per QALY gained). A low sensitivity (28.4%) and high specificity (88.6%) would be cost-effective at the lower end of this range while higher sensitivity (87.5%) and lower specificity (62.8%) was cost-effective towards the upper end of this range. These results were sensitive to the cost of MTC admissions and whether MTCs had a benefit for patients with an ISS between 9 and 15. Conclusions The cost-effective triage tool depends on the English decision maker’s MAICER for this health problem. In the usual range of MAICERs, cost-effective prehospital trauma triage involves clinically suboptimal sensitivity, with a proportion of seriously injured patients (at least 10%) being initially transported to local hospitals. High sensitivity trauma triage requires development of more accurate decision rules; research to establish if patients with an ISS between 9 and 15 benefit from MTCs; or, inefficient use of health care resources to manage patients with less serious injuries at MTCs. Critical Care & Emergency Medicine Major Trauma Severe injuries Triage Tools Economic Evaluation Figures Figure 1 Background Major trauma is a significant problem worldwide, with a World Health Organisation report identifying that injuries were responsible for 9% of all deaths in 2012.[ 1 ] Systems and interventions to improve the outcomes of patients with injuries represent a key area in which public health can be improved worldwide. Major trauma centres (MTCs), which concentrate severely injured patients in specialist centres, were introduced in England in 2012. Similar systems have been in use in some regions of the USA for many decades. In MTC systems if patients are suspected to be severely injured then paramedics will bypass local hospitals, if these are closer than the MTC, and the MTC will be pre-alerted to allow activation of a specialist major trauma team for resuscitation and initial management. Evidence from the USA shows that patients who have an injury severity score (ISS) of 16 or more would, on average, have better outcomes if they were treated at a major trauma centre.[ 2 – 6 ] Consequently, severe injuries are often defined in the literature as patients whose ISS was 16 or more. However, ISS can only be derived after the patient has been diagnosed and treated, therefore it is not always clear which hospital the paramedics should decide to transport the patients to. Furthermore, there is a definition of severe injuries that defines severe injuries as those injuries that would benefit from care that is only available at MTCs in a US setting.[ 7 ] As this is not yet widely used in the literature, we use the ISS is greater than or equal to 16 definition throughout. van Rein et al. conducted a systematic review of studies assessing the effectiveness of triage tools in MTC systems.[ 8 ] This study found that no study which had a high methodological quality produced a tool that had adequate performance. Consequently, there is clinical value in developing, testing and implementing triage tools for patients with suspected major trauma that can be applied by paramedics when initially assessing an injured patient. Existing triage tools consist of physiological, anatomical, injury characteristic, and injury mechanism variables. Sensitivity and specificity are dependent on which variables are included in the triage tool and the cut-off level chosen for constituent variables, to give a positive diagnosis of major trauma. Any triage tool, will trade-off the number of true positive cases correctly admitted to major trauma centres (sensitivity), with the number of true negative cases correctly transported to local hospitals (specificity). However, it is uncertain where the optimal balance of sensitivity and specificity lies as the use of MTCs is associated with improved outcomes for severely injured patients, but also costs more. The aim of this paper is to conduct a cost-utility analysis of several plausible major trauma triage tools from the perspective of a UK decision maker. Secondary outcomes of the decision analytic model include system flows of patients throughout the model, as this will influence which tools are feasible. Methods Modelling approach We developed a lifetime patient-level decision tree, followed by a discrete event simulation to model patient flows through an English MTC system. The model estimated patient’s outcomes, costs incurred and quality adjusted life years (QALYs) accrued for patients with suspected severe injuries. Conceptual modelling, informed by a previously published model by Newgard et al and consultation with subject experts informed the final model design.[9] We have taken a patient-level approach for two reasons. Firstly, we can accurately predict the risk of death using a validated 30-day probability of survival equation, developed by the Trauma Audit and Research Network (TARN), in an English population.[10, 11] Secondly, when actual tools are assessed in English populations, this model can be easily adapted to include any correlations that exist between triage tool outcomes and the patient’s probability of survival predicted by the TARN survival equation. Patient Population Our model considered patients who were injured outside of the MTC’s local area for two reasons. Firstly, patients who live closest to an MTC will usually go to the MTC regardless of the severity of their injury. However, if a patient is thought to be severely injured, the MTC will be pre-alerted. Secondly, the available effectiveness evidence appears to treat all patients who live closest to an MTC as having been treated at an MTC regardless of whether the trauma team were pre-alerted. Consequently, there is no trade-off between cost and effectiveness that can be assessed in our analyses. The model was populated with simulated patients’ representative of injured patients presenting to an English trauma system. To generate these characteristics we obtained access to baseline demographic and clinical data from a recent prehospital major trauma triage study by van Rein et al, using the data collected in the Central Netherlands region.[12] This provided a high quality data set, which should be representative of developed world patient. This data is summarised in Table 1. Means, standard deviations and covariances between all parameters were obtained from the van Rein et al data for patients with complete data for Age, Gender, ISS, Glasgow Coma Scale (GCS) and trauma type.[12] Simulated patients were sampled using correlated draws from these distributions, further details are provided in the appendix. Table 1: A summary of the simulated characteristics of the patients included in the model Characteristic Mean SD / n/N Source Age 46.8 21.3 Patients with complete Age, Gender, ISS, GCS and trauma type data in Van Rein et al .[12] Percentage Male 58.3% 2887/4720 ISS 5.2 7.2 Percentage with an ISS ≥ 16 9.1% 428/4720 GCS 14.4 1.9 Percentage with blunt trauma 98.2% 4637/4720 SD, standard deviation; ISS, injury severity score; GCS, Glasgow Coma Scale Interventions Nine triage tools were examined, based on Newgard et al in which triage tools were derived by statistically analysing a retrospective cohort study conducted at 6 sites in the Western US between January 2006 and December 2008.[13] This study was selected as it fit nine triage tools to one dataset, so represented feasible trade-offs between sensitivity and specificity for any new rule. These analyses produced nine triage tools for which, the reported sensitivities and specificities for each tool were: Sensitivity 99.8%, Specificity 2.5% Sensitivity 94.8%, Specificity 18.7% Sensitivity 90.4%, Specificity 58.4% Sensitivity 87.5%, Specificity 62.8% Sensitivity 74.6%, Specificity 65.7% Sensitivity 69.8%, Specificity 70.1% Sensitivity 64.2%, Specificity 76.1% Sensitivity 57.0%, Specificity 80.0% Sensitivity 99.8%, Specificity 88.6% We assumed that the sensitivity and specificity values for each triage tool represented the final triage decision, combining the diagnostic accuracy of the triage tool and the application of judgement by the on-scene paramedics. A recent analysis of a Dutch triage tool in an English data set produced a received-operator curve that would result in similar triage tool sensitivity and specificity as observed by Newgard et al .[14] Perspective In line with guidance from the English and Welsh decision maker, the National Institute for Health and Care Excellence (NICE): we undertook a cost-utility analysis; our analyses had a lifetime horizon; an NHS and personal social services perspective was taken; and, future costs and QALYs were discounted at 3.5%.[15] Outcome measures The primary outcome measure of the model was the incremental cost-effectiveness ratio (ICER). Key secondary outcomes of the model included: life expectancy; discounted costs; discounted QALYs; the number of patients who died prior to discharge; the number of patients who died between discharge and one-year post-injury; and, the number of patients sent to an MTC. The ICERs between the strategies were calculated as difference in cost / difference in QALYs. As we have a decision problem with multiple strategies, a full incremental analysis was undertaken in line with the NICE methods guide.[15] In this approach strategies are ordered by their effectiveness (measured in QALYs). Any tools that are dominated (they produce less QALYs at a higher cost than another tool) or extendedly dominated (a combination of two other tools can produce the same QALYs at a lower cost) were removed from consideration. ICERs were then calculated comparing each remaining tool, except the least effective tool, to the next least effective remaining tool. The maximum acceptable ICER (MAICER), is the amount of money that a decision-maker is willing to pay to gain 1 additional QALY. NICE’s MAICER is usually considered to be £20,000 per QALY, but may increase to £30,000, as detailed in the NICE methods guide.[15] In a full incremental analysis, the most effective tool with an ICER below the decision maker’s MAICER is the cost-effective tool. Model Structure and Logic A diagram of the model is presented in Figure 1. Patients enter the model and the sensitivity and specificity of the triage tool determines whether they go to the MTC or local hospital. For patients who go to a local hospital, there is a probability that they will undergo secondary transfer to the MTC. For patients who the triage tool suggests that they go directly to the MTC, there is a chance that they will initially go to a local hospital for urgent care after which they will undergo a secondary transfer to the MTC. Any patient who goes to the MTC will gain the full benefit from MTC care, but all patients who undergo a secondary transfer will incur costs for an additional ambulance callout. Post-admission, each patient’s probability of survival within 30 days is estimated. Patients with an ISS of 16 or more and who did not go to an MTC, have a relative risk of death applied to their 30 day survival probability to reflect the poorer outcomes we expect for these patients. Patients who survive up until 30 days post-injury, have their probability of survival up to one year post-injury estimated. Again, a relative risk of death was applied to increase the probability of death for patients with an ISS of 16 or more who did not receive MTC care. Patients who survive up to one year post-injury, enter a long-term discrete event simulation in which their life expectancy is estimated using general population mortality data, increased by a hazard ratio dependent on their ISS, to estimate their expected remaining life expectancy. This model was developed in R v4.0.2.[16] Probability of events and effectiveness of MTCs We used the same evidence as the previously published Newgard et al economic model for the effectiveness of MTCs on patient outcomes. These studies are analyses of large cohort studies in a North American setting.[2–4] The data on the probability of death was updated to include UK data, where this was known to exist. The 2006 TARN survival prediction model was selected for use in our base case economic model, as our patient cohort did not have information on comorbidities which is required in the 2015 TARN survival equation.[10, 11] Table 2: A summary of the parameters used in the model. Clinical parameters Parameter Value Source Probability of patients having a transfer from a local hospital to an MTC if: They were a true positive (ISS ≥ 16 & tool positive) 26.6% Newgard et al 2016[32] They were a false negative (ISS ≥ 16 & tool negative) 32.5% They were a true negative (ISS < 16 & tool negative) 4.3% They were a false positive (ISS < 16 & tool positive) 7.4% Probability of death within 30 days Risk equation TARN[10] Relative risk of death within 30 days of hospitalisation for patients with an ISS ≥ 16 who were treated at a local hospital 1.25 Newgard et al 2013[3] Relative risk of death within 30 days of hospitalisation for patients with an ISS < 16 who were treated at a local hospital 1 Assumption Probability of death between 30 days post-injury and 1-year post-injury for patients with an ISS ≥ 16 3.6% Mackenzie et al . 2006[2] Relative risk of death between 30 days and 1 year post-hospitalisation for patients with an ISS ≥ 16 who were treated at an local hospital 1.64 Probability of death between 30 days post-injury and 1-year post-injury for patients with an ISS < 16 1.7% Davidson et al 2011[33] Probability of death after 1 year Age and gender dependant ONS[34] Hazard Ratio for the risk of death if someone has a suspected major trauma case with: An ISS of less than 16 1.38 Newgard et al 2016[9] Cameron et al. 2005[4] An ISS of greater than or equal to 16 5.19 Utility parameters Parameter Value Source Utility for patients with: An ISS of 16 or more 0.65 Ahmed et al [17] An ISS of 15 or less 0.65 General population utility Constant 0.9508566 Ara and Brazier[35] Age -0.0002587 Age squared -0.0000332 Male (1 = male, 0 = otherwise) 0.0212126 Calculations Age and gender matched general population utility for the Ahmed et al population 0.824 Calculated. Mean age was 61 years and 59.1% of the analysis population was male in Ahmed et al .[17] Utility multipliers, relative to the utility in the general population, for patients with: An ISS of 16 or more 0.789 Calculated An ISS between 15 and 9 0.789 Calculated An ISS of under 9 1 We assumed that these patients would have a utility equal to that of the general population Cost Parameters Parameter Value Source Admission costs – base case Transfers between local hospitals and MTCs £252 Assumed to be one additional ambulance call out. NHS improvement.[23] Currency Code ASS02. MTC admission, if ISS is 16 or over £2,819 NHS improvement[22] MTC admission, if ISS is less than 16 and over 8 £1,466 Treatment of a patient with blunt trauma and an ISS in the range of: ISS≤9 £6,198 Christensen et al[20] 9<ISS≤16 £8,989 16 25 £21,173 Treatment of a patient with penetrating trauma and an ISS in the range of: ISS≤9 £6,501 Christensen et al[21] 9<ISS≤15 £6,035 15< ISS≤24 £9,453 24 34 £16,438 Post discharge costs Cost between discharge and 6 months post treatment £1,766 John Nichol, Personal communication Relative increase in lifetime treatment costs for patients with an ISS ≥ 16 compared to the general population 1.45 Cameron et al . 2006[25] Delgado et al 2013[24] Relative increase in lifetime treatment costs for patients with an ISS < 16 compared to the general population 1.25 Cameron et al . 2006[25] Delgado et al 2013[24] Yearly costs of NHS treatment Age and gender dependent Asaria 2017[26] NB – distributions and the standard errors around each parameter are provided in the appendix local hospital – local hospital; MTC, major trauma centre; ISS, injury severity score Utilities The utility parameters used in the model are provided in Table 2. Our utilities are from Ahmed et al , which is a survey in which 154 patients, whose ISS was 9 or more, completed the EQ-5D-5L questionnaire at an English MTC one year post-injury.[17] There was no evidence in this study that utility varied by ISS score. For patients with an ISS of 9 or more we applied these utilities multiplicatively to age-gender matched utilities for the UK general population.[18] For patients with an ISS of less than 9 we assumed that their injury did not have long term effects on their utility. Costs The costs in the model reflect English practice and are provided in Table 2. All costs are in 2017/18 prices. Costs from previous years were inflated to 2017/18 prices using the HCHS Pay and Prices inflation index.[19] Other costs incurred within the first 6 months post-injury were obtained from UK based studies and sources.[20–23][John Nicholl, personal communication] After 6 months we adopt the data reported in Delgado et al , which was a cost-effectiveness analysis of helicopter versus ground transport in the US.[24] These parameters, which is that based on data from one Canadian study, are an increase in costs for patients with a history of trauma compared to the general population.[25] We used English health care costs incurred by the general population, according to their age and gender, and the data from Delgado et al to calculate the increased long term health care costs incurred by each patient in our model.[24, 26] Scenario analyses A base case deterministic analysis was performed, where all parameters are set to mean values. In order to account for the uncertainty in model inputs a probabilistic sensitivity analysis (PSA) was conducted using Monte Carlo simulation to randomly sample from a distribution assigned to each model parameter (see Appendix). Multiple model runs were performed, each with independent random draws from every parameter’s distribution. ICERs were calculated from the mean expected costs and effects over all model runs. We assessed the stability of our model results with respect to the number of patients (assessed visually) and number of PSA runs (assessed using the Hatswell et al method).[27] We found that 25,000 simulated patients and 2,000 PSA runs produced stable results (see Appendix). We conducted three scenario analyses to explore the robustness of model assumptions. In the first scenario analysis we used the 2015 TARN survival equation and assumed that the simulated patients in our model were in the same risk category as people with missing Charlson Comorbidity Index (CCI).[11] In the second scenario analysis, we explored the benefit of MTC care to patients with an ISS between 9 and 15 inclusive, as these patients incur costs for going to an MTC in England implying there may be a belief by payers that these patients would benefit from MTC care. In the final set of scenario analyses we varied the cost of MTC care, as the cost of MTC care in England is reviewed regularly. Results Base Case analysis The results of the deterministic base case analysis is given in Table 3 . All ICERs are in excess of £30,000 per QALY gained, consequently they are above the upper limit of the ICER that NICE would consider acceptable meaning that the cost-effective strategy is the least sensitive triage tool.[ 15 ] The PSA results are given in Table 3 . The PSA results, in terms of the ICERs and the tools which are dominated or extendedly dominated, are very different to the deterministic results. Consequently all conclusions and scenario analyses are based on the PSA results, as the difference between the deterministic and PSA results for the base case indicates that conducting deterministic analysis introduces bias into the estimated ICER (non-linearity).[ 27 ] In the PSA results, a low sensitivity and high specificity tool results in the least number of cases going to an MTC, the lowest cost and the worst outcomes (probability of death, life expectancy and QALYs). Conversely a highly sensitive and low specificity tool results in the most cases going to an MTC, the highest cost, and the best outcomes. Three strategies have ICERs above £20,000 per QALY gained, but below £30,000 per QALY gained (57% sensitivity, 64.2% sensitivity, 87.5% sensitivity). The two remaining strategies, that were not dominated or extendedly dominated, had ICERs that were above the usual upper limit of NICE’s MAICER of £30,000 per QALY gained. The model results indicate that out of a population of 100,000 patients to whom a major trauma triage tool was applied, 18,448 out of the 100,000 assessed patients would go to an MTC using the most specific tool whereas 97, 860 of the 100,000 assessed patients would go to the MTC using the most sensitive tool. Even when using a very specific triage tool, the majority patients with an ISS ≥ 16 would go to an MTC due to transfers from the local hospitals. Table 3 The results of the deterministic base case analyses Triage Tool Number of cases sent to the MTC per 100,000 patients Number of cases sent to the MTC per 8,916 patients (ISS ≥ 16) Number of cases sent to the MTC per 91,084 patients (ISS < 16) Proportion of patients who died before discharge Proportion of patients who die between discharge and 1-year post-injury Mean years lived Mean discounted QALYs Mean discounted Costs ICER Deterministic 28.4% Sens, 88.6% Spec 18,912 4,600 14,312 4.17% 1.80% 32.07 13.620 £32,574 - 57.0% Sens, 80.0% Spec 28,120 6,220 21,900 4.14% 1.78% 32.08 13.624 £32,698 ED 64.2% Sens, 76.1% Spec 31,892 6,724 25,168 4.12% 1.78% 32.08 13.625 £32,743 ED 69.8% Sens, 70.1% Spec 37,536 7,092 30,444 4.11% 1.77% 32.08 13.626 £32,774 ED 74.6% Sens 65.7% Spec 41,672 7,392 34,280 4.10% 1.78% 32.08 13.626 £32,793 ED 87.5% Sens, 62.8% Spec 44,976 8,156 36,820 4.09% 1.76% 32.09 13.629 £32,854 £33,026 90.4% Sens, 58.4% Spec 49,100 8,364 40,736 4.08% 1.75% 32.09 13.630 £32,889 £39,584 94.8% Sens, 18.7% Spec 83,116 8,612 74,504 4.08% 1.75% 32.09 13.630 £32,979 ED 99.8% Sens, 2.5% Spec 97,860 8,912 88,948 4.06% 1.74% 32.10 13.633 £33,064 £54,515 Probabilistic (all values are mean values) 28.4% Sens, 88.6% Spec 18,448 4,607 13,841 4.78% 1.78% 32.05 13.580 £33,024 - 57.0% Sens, 80.0% Spec 27,670 6,331 21,339 4.72% 1.76% 32.07 13.586 £33,181 £25,039 64.2% Sens, 76.1% Spec 31,505 6,763 24,741 4.70% 1.75% 32.07 13.588 £33,223 £27,311 69.8% Sens, 70.1% Spec 37,069 7,100 29,969 4.69% 1.75% 32.07 13.589 £33,262 ED 74.6% Sens 65.7% Spec 41,192 7,388 33,804 4.68% 1.74% 32.08 13.590 £33,294 ED 87.5% Sens, 62.8% Spec 44,499 8,165 36,334 4.65% 1.73% 32.08 13.593 £33,363 £27,624 90.4% Sens, 58.4% Spec 48,516 8,339 40,177 4.65% 1.73% 32.08 13.594 £33,386 £35,791 94.8% Sens, 18.7% Spec 83,383 8,603 74,779 4.64% 1.72% 32.09 13.594 £33,486 ED 99.8% Sens, 2.5% Spec 97,810 8,904 88,906 4.62% 1.72% 32.09 13.596 £33,542 £77,477 MTC, major trauma centre; ISS, injury severity score; QALYS, quality adjusted life years; ICER, incremental cost-effectiveness ratio; Sens, sensitivity; Spec, specificity; ED, extendedly dominated Scenario analyses Table 4 summarises the results of the scenario analyses. When the TARN 2015 survival equation is used and all patients in our simulation are treated as having a missing CCI, the results are remarkably similar to the base case analysis as the strategies which are cost-effective at £20,000 and £30,000 per QALY gained are the same.[ 11 ] In the scenario analyses in which patients with an ISS between 9 and 15 inclusive receive a benefit from MTC care, the conclusions of the base case are changed. If the benefit that these patients receive is 25% or 50% of the benefit accrued from MTC care by patients with an ISS of 16 or more, then the most cost-effective tool at an MAICER of £30,000 per QALY gained is the most sensitive triage tool. Although when these patients receive 50% of the benefit of MTC care, the ICER for the most sensitive strategy is only £20,306 per QALY gained (see Appendix) indicating that the NICE’s ICER would only have to be a very small amount over the lower end of their usual range of MAICERs to consider the most sensitive rule to be cost-effective in this scenario. In the scenario where these patients receive 75% of the benefit of MTC care that is accrued by patients with an ISS over 16, then the most sensitive triage tool would be cost-effective at MAICERs of both £20,000 and £30,000 per QALY gained. Table 4 The results of the scenario analyses Scenario Cost-effective tool at £20,000 per QALY gained Cost-effective tool at £30,000 per QALY gained Base Case 28.4% Sens, 88.6% Spec 87.5% Sens, 62.8% Spec Scenario analyses TARN 2015 survival equation with every patient’s CCI being missing 28.4% Sens, 88.6% Spec 87.5% Sens, 62.8% Spec MTC benefit for people with and ISS in the range 16 > ISS ≥ 9 MTCs have 25% benefit RR of death prior to discharge = 1.07 RR of death discharge and one year = 1.16 28.4% Sens, 88.6% Spec 99.8% Sens, 2.5% Spec MTCs have 50% benefit RR of death prior to discharge = 1.13 RR of death discharge to one year = 1.32 28.4% Sens, 88.6% Spec 99.8% Sens, 2.5% Spec MTCs have 75% benefit RR of death prior to discharge = 1.19 RR of death discharge to one year = 1.48 99.8% Sens, 2.5% Spec 99.8% Sens, 2.5% Spec Full results are given in the Appendix QALY, quality adjusted life year; Sens, sensitivity; Spec, specificity; TARN, Trauma Audit and Research Network; CCI, Charlson comorbidty index; MTCs, major trauma centres; RR, relative risk Table 5 shows the tool that is cost-effective when the cost of MTC care in England is changed. This is assessed at MAICERs of £20,000 and £30,000 per QALY gained. At an MAICER of £20,000 the optimal tool is highly sensitive to the level of best practice tariffs for MTCs in the UK. If the tariffs are set to their 2017/18 levels, then the optimal tool is a highly specific triage tool. If the tariffs were set to £0, then the optimal tool would be a tool with a sensitivity of 88% and a specificity of 63%. These results are similar at MAICERs of £30,000 per QALY gained, with either the tool with a sensitivity of 88% or a sensitivity of 90% being cost-effective. Table 5 Cost-effective triage tool in the threshold analyses on the cost of MTC care in Engalnd MAICER = £20,000 per QALY gained Cost of MTC care for patients with : ISS ≥ 16 (rows) 16 > ISS ≥ 9 (columns) £1541 (2020/21 tariff levels) £1,466 (100%) £1099.50 (75%) £733 (50%) £366.50(25%) £2961 (2020/21 tariff levels) 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 57.0% Sens 80.0% Spec £2,819 (100%) 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 57.0% Sens 80.0% Spec £2114.25 (75%) 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 57.0% Sens 80.0% Spec £1409.50 (50%) 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 57.0% Sens 80.0% Spec 87.5% Sens 62.8% Spec £704.75 (25%) 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 28.4% Sens 88.6% Spec 87.5% Sens 62.8% Spec 90.4% Sens 58.4% Spec MAICER = £30,000 per QALY gained Cost of MTC care for patients with : ISS ≥ 16 (rows) 16 > ISS ≥ 9 (columns) £1541 (2020/21 tariff levels) £1,466 (100%) £1099.50 (75%) £733 (50%) £366.50(25%) £1541 (2020/21 tariff levels) 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 90.4% Sens 58.4% Spec 87.5% Sens 62.8% Spec £2,819 (100%) 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec £2114.25 (75%) 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 87.5% Sens 62.8% Spec 90.4% Sens 58.4% Spec 90.4% Sens 58.4% Spec £1409.50 (50%) 90.4% Sens 58.4% Spec 87.5% Sens 62.8% Spec 90.4% Sens 58.4% Spec 90.4% Sens 58.4% Spec 90.4% Sens 58.4% Spec £704.75 (25%) 90.4% Sens 58.4% Spec 90.4% Sens 58.4% Spec 90.4% Sens 58.4% Spec 90.4% Sens 58.4% Spec 99.8% Sens 2.5% Spec MAICER, maximum acceptable incremental cost-effectiveness ratio; ISS, injury severity score; Sens, sensitivity; Spec, specificity Full results as per the base case analysis are available in the Appendix Discussion Summary of findings The cost-effective triage tool for patients with suspected major trauma is highly uncertain, as three potential tools have emerged with ICERs within the range of £20,000 to £30,000 per QALY gained. If the MAICER in the UK for this problem is £20,000 per QALY gained, then the cost-effective triage tool will be a highly specific tool. However, if the MAICER in the UK for this problem is £30,000 per QALY gained then the cost-effective triage tool will be a moderately sensitive tool with a sensitivity in the region of 85–90%. The sensitivity of these tools are slightly lower than the American College of Surgeons Committee on Trauma’s (ACSCOT) recommended sensitivity for any new tool of 95%.[28] The key uncertainties in our ICERs relate to the benefit that MTCs offer to patients with an ISS of between 9 and 15. If this subgroup of patients gains a benefit from MTC care, then the cost-effective tool may be a highly sensitive tool. Furthermore, the results are sensitive to the current cost of MTC care in England, which is determined by best practice tariffs payments made to hospitals. When deciding upon the exact MAICER used for a decision problem in England, NICE committees consider: how certain the ICERs are; whether health related quality of life has been adequately captured in utilities; whether they believe the technology is innovative; and, whether the technology helps the NHS meet its non-health objectives. As the MAICERs increase from £20,000 to £30,000 per QALY gained, the committee will make explicit references to these criteria in their judgement as to whether a new technology is cost-effective. Therefore, if the moderately sensitive tool was to be judged cost-effective in our base case analysis, then the tool would have to be judged as meeting one or more of these additional criteria by a NICE guidelines committee on major trauma. Comparison to previous literature Despite large-scale investment in major trauma networks, the cost-effectiveness of major trauma triage is not well studied, with only one previously published economic model available by Newgard et al .[9] They found in a US setting, implementing a high sensitivity tool (as recommended by ASCOT) was unlikely to be cost-effective. This conclusion is very similar to our findings and shows that developing high sensitivity tools for major trauma are likely to not be a cost-effective use of resources in either the UK or the US based on our current understanding of major trauma systems. Strength and Limitations This model is the first model of major trauma set in the UK that we are aware of, follows best practice recommendations for health economic evaluations, and brings together the best available evidence to inform decision making on major trauma centres in the England. However, there are a number of limitations in the underlying evidence base that must be taken into account when considering the results and when designing future research projects designed to make well-informed decisions regarding major trauma care in an English or UK wide context. Firstly, all the clinical evidence underpinning the model related to a definition of major trauma based solely on patient’s ISS. However, ISS may not be the gold standard definition of major trauma with a new criterion for patients who benefit from major trauma existing.[7, 29, 30] Secondly, a Dutch cohort was used to simulate patients in our model. This provided a high quality data set which should be representative of patients in the developed world, however complete generalisability to the UK, or other settings cannot be guaranteed.[12] Thirdly, whilst most of the data in this model is from the UK setting, further research on the effectiveness of MTCs, the probability of receiving an secondary transfer to an MTC, the effect of having major trauma on patient’s long-term outcomes in a UK setting, and the total number of patients with an ISS ≥ 16 not transported to an MTC would be desirable. Fourthly, it is thought that patients who go directly to MTCs have better outcomes than patients who receive secondary transfers to the MTC, however quantitative evidence on this effect is lacking meaning that this cannot be accurately quantified in our analyses. Fourhtly, the data on the health care costs incurred by major trauma patients in the UK is old, as it uses TARN data from 2000 to 2005. Therefore, an update of this evidence would be a useful addition to this model. There is a partial update to this evidence, which uses TARN data from 2009 to 2011.[31] However, the population of this study was limited to patients with major trauma who also had severe bleeding. Finally, to conduct a simulated population it was necessary to exclude patients with missing data from the Dutch Cohort. Future Research As highlighted in the strengths and limitations there are several key areas of research that would improve decision making in the area of major trauma triage these include: 1) Estimating the effectiveness of MTC care for patients who meet the Lerner et al . criteria of major trauma centre need, rather than ISS.[7] Without this evidence we cannot reliably estimate the cost-effectiveness of triage tools designed around these criteria. 2) The evidence on the cost of major trauma cases (especially in the long term) in the UK NHS should be updated. 3) Further research should be conducted into whether patients with an ISS of between 9 and 15 receive any benefit from MTC care. 4) Quantifying the benefit of MTCs for patients sent directly to the MTCs and the patients who receive a secondary transfers should estimated separately. Implications for practice This work indicates that based on the current major trauma system in England and the best currently available evidence, if a cost-effective rule is to be selected then we have to accept a non-negligible proportion of severely injured patients (at least 10%) will not initially be identified as needing care at a major trauma centre. Based on current evidence we would expect around 30% of these patients to be transferred to an MTC, but that still leaves 7% of all major trauma cases not receiving the best available care. Conclusion In conclusion, cost-effective prehospital trauma triage involves clinically suboptimal sensitivity unless it can be proved that patients with an ISS between 9 and 15 receive a significant benefit from MTC care. Cost-effective trauma triage tools result in a proportion of severely injured patients (at least 10%) being initially transported to local hospitals. Implementing high sensitivity trauma triage tools in England requires development of more accurate decision rules; research to establish if patients with an ISS between 9 and 15 benefit from MTCs; changes in the MTC funding system in England; or, inefficient use of health care resources to manage patients with less serious injuries at MTCs. Abbreviations List of abbreviations Abbreviation Meaning MTCs Major trauma centres ISS Injury Severity Score QALYs Quality adjusted life years TARN Trauma audit research network GCS Glasgow coma Scale NICE National Institute for Health and Care Excellence ICER Incremental cost-effectiveness ratio MAICER Maximum acceptable incremental cost-effectiveness ratio PSA Probabilistic sensitivity analysis CCI Charlson Comorbidity Index Declarations Ethics approval, guidelines and consent to participate The Major Trauma Triage Study, of which this analysis of part, has ethical approval from Bradford Leeds Research Ethics Committee (REC 28 th June 2019, Reference number 19/YH/0197). All substantial protocol amendments were approved by Bradford Leeds REC and the Health Research Authority (HRA) before implementation. The data collected by University Medical Center Utrecht was judged by the Medical Ethical Committee of University Medical Center Utrecht, Utrecht, the Netherlands, as not subject to the Medical Research Involving Human Subjects Act and therefore exempt from the need for informed consent. The study was performed in accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments Consent for publication Not applicable, the only individual patient data used in these analyses was exempt for the need for informed consent. Availability of data and materials The data from van Rein et al .[8] was accessed by request from MH. The model code, PSA parameters and patient characteristics are available open access under a MIT licence. These are given at: https://doi.org/10.15131/shef.data.13379036.v2 Competing interests No authors report any competing interests Funding This study/project is funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme (grant number 17/16/04). The NIHR had no control over the conduct of the study. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Author contributions DP had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis Conception and design: DP, GF, SG Acquisition, analysis, or interpretation of data: DP, GF, SG, EAJR, JFW and MH Drafting of the manuscript: DP, GF, SG Critical revision of the manuscript for important intellectual content: EAJR, JFW, MH Model development and Economic analysis: DP Acknowledgements None References World Health Orgnaisation. Injuries and Violence The Facts 2014. 2014;:20. https://apps.who.int/iris/bitstream/handle/10665/149798/9789241508018_eng.pdf;jsessionid=B8DC599DEC637E1B22284BDF61C611CD?sequence=1. Accessed 18 Nov 2020. MacKenzie EJ, Rivara FP, Jurkovich GJ, Nathens AB, Frey KP, Egleston BL, et al. A National Evaluation of the Effect of Trauma-Center Care on Mortality. N Engl J Med. 2006;354:366–78. Newgard CD, Staudenmayer K, Hsia RY, Mann NC, Bulger EM, Holmes JF, et al. The cost of overtriage: More than one-third of low-risk injured patients were taken to major trauma centers. Health Aff. 2013. Cameron CM, Purdie DM, Kliewer E V., McClure RJ. Long-term mortality following trauma: 10 Year follow-up in a population-based sample of injured adults. J Trauma - Inj Infect Crit Care. 2005. Cudnik MT, Newgard CD, Sayre MR, Steinberg SM. Level i versus level II trauma centers: An outcomes-based assessment. J Trauma - Inj Infect Crit Care. 2009. Polites SF, Leonard JM, Glasgow AE, Zielinski MD, Jenkins DH, Habermann EB. Undertriage after severe injury among United States trauma centers and the impact on mortality. Am J Surg. 2018. Lerner EB, Willenbring BD, Pirrallo RG, Brasel KJ, Cady CE, Colella MR, et al. A consensus-based criterion standard for trauma center need. In: Journal of Trauma and Acute Care Surgery. 2014. van Rein EAJ, van der Sluijs R, Houwert RM, Gunning AC, Lichtveld RA, Leenen LPH, et al. Effectiveness of prehospital trauma triage systems in selecting severely injured patients: Is comparative analysis possible? American Journal of Emergency Medicine. 2018. Newgard CD, Yang Z, Nishijima D, McConnell KJ, Trent SA, Holmes JF, et al. Cost-effectiveness of field trauma triage among injured adults served by emergency medical services. J Am Coll Surg. 2016. Bouamra O, Wrotchford A, Hollis S, Vail A, Woodford M, Lecky F. Outcome prediction in trauma. Injury. 2006. Bouamra O, Jacques R, Edwards A, Yates DW, Lawrence T, Jenks T, et al. Prediction modelling for trauma using comorbidity and “true” 30-day outcome. Emerg Med J. 2015. Van Rein EAJ, Van Der Sluijs R, Voskens FJ, Lansink KWW, Houwert RM, Lichtveld RA, et al. Development and Validation of a Prediction Model for Prehospital Triage of Trauma Patients. JAMA Surg. 2019. Newgard CD, Hsia RY, Mann NC, Schmidt T, Sahni R, Bulger EM, et al. The trade-offs in field trauma triage: A multiregion assessment of accuracy metrics and volume shifts associated with different triage strategies. J Trauma Acute Care Surg. 2013. Shanahan T, Fuller GW, Sheldon T, Turton E, Quility FMA, Marincoqitz C. External validation of the Dutch prediction model for prehospital triage of trauma patients in South West region of England, United Kingdom. Forthcommi. National Institute for Health and Care Excellence. Guide to the methods of technology appraisal. Online Source. 2013;Available Last Accessed: 17th March 2016:1–93. doi:10.2165/00019053-200826090-00002. R Core Team (2019). R: A language and environment for statistical computing. Accessed 1st April 2019. 2019. W. A, R. A, Ahmed W, Alwe R, Wade D. One-year functional outcomes following major trauma: experience of a UK level 1 major trauma centre. Clin Rehabil. 2017;31:1646–52. doi:https://dx.doi.org/10.1177/0269215517712044. Ara R, Brazier JE. Populating an economic model with health state utility values: moving toward better practice. Value Heal. 2010;13:509–18. doi:10.1111/j.1524-4733.2010.00700.x. Curtis L, Burns A. Unit Costs of Health and Social Care 2018. Online Source. 2019;Available Last Accessed 19th July 2019. Christensen MC, Ridley S, Lecky FE, Munro V, Morris S. Outcomes and costs of blunt trauma in England and Wales. Crit Care. 2008. Christensen MC, Nielsen TG, Ridley S, Lecky FE, Morris S. Outcomes and costs of penetrating trauma injury in England and Wales. Injury. 2008. NHS Improvement. 2017/18 and 2018/19 National Tariff Payment System. Online Source Available from https//improvement.nhs.uk/documents/1044/2017-18_and_2018-19_National_Tariff_Payment_System.pdf. 2019; Last Accessed: 19th July 2019. NHS Improvement. 2017/18 reference cost data. Online Source Available from https//improvement.nhs.uk/resources/reference-costs/. 2019; Last Accessed 19th July 2019. Delgado MK, Staudenmayer KL, Wang NE, Spain DA, Weir S, Owens DK, et al. Cost-effectiveness of helicopter versus ground emergency medical services for trauma scene transport in the United States. Ann Emerg Med. 2013. Cameron CM, Purdie DM, Kliewer E V., McClure RJ. Ten-year health service use outcomes in a population-based cohort of 21 000 injured adults: The Manitoba Injury Outcome Study. Bull World Health Organ. 2006. Asaria M. Health care costs in the English NHS: reference tables for average annual NHS spend by age, sex and deprivation group. Unit Costs Heal Soc Care. 2017;:16–21. Hatswell AJ, Bullement A, Briggs A, Paulden M, Stevenson MD. Probabilistic Sensitivity Analysis in Cost-Effectiveness Models: Determining Model Convergence in Cohort Models. Pharmacoeconomics. 2018. Criteria R. Resources for optimal care of the injured patient 2006. Chicago Am …. 2006. Newgard CD, Fu R, Zive D, Rea T, Malveau S, Daya M, et al. Prospective Validation of the National Field Triage Guidelines for Identifying Seriously Injured Persons. J Am Coll Surg. 2016. van der Sluijs R, Lokerman RD, Waalwijk JF, de Jongh MAC, Edwards MJR, den Hartog D, et al. Accuracy of pre-hospital trauma triage and field triage decision rules in children (P2-T2 study): an observational study. Lancet Child Adolesc Heal. 2020. Campbell HE, Stokes EA, Bargo DN, Curry N, Lecky FE, Edwards A, et al. Quantifying the healthcare costs of treating severely bleeding major trauma patients: A national study for England. Crit Care. 2015. Newgard CD, Yang Z, Nishijima D, McConnell KJ, Trent SA, Holmes JF, et al. Cost-effectiveness of field trauma triage among injured adults served by emergency medical services. J Am Coll Surg. 2016;222:1125–37. doi:http://dx.doi.org/10.1016/j.jamcollsurg.2016.02.014. Davidson GH, Hamlat CA, Rivara FP, Koepsell TD, Jurkovich GJ, Arbabi S. Long-term survival of adult trauma patients. JAMA - J Am Med Assoc. 2011. Office for National Statistics. National life tables: UK 2015-2017. Online Source Available from https//www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/datasets/nationallifetablesunitedkingdomreferencetables. Ara R, Brazier JE. Populating an economic model with health state utility values: Moving toward better practice. Value Heal. 2010. Additional Declarations No competing interests reported. Supplementary Files BMCemergencymedicinepaperAppendixSubmittedApril2021.docx Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2022 Read the published version in BMC Emergency Medicine → Version 1 posted Editorial decision: Major revision 25 Oct, 2021 Reviews received at journal 24 Oct, 2021 Reviewers agreed at journal 27 Sep, 2021 Reviews received at journal 07 Jun, 2021 Reviewers agreed at journal 22 May, 2021 Reviewers agreed at journal 22 May, 2021 Reviewers invited by journal 18 May, 2021 Editor assigned by journal 18 May, 2021 Editor invited by journal 18 May, 2021 Submission checks completed at journal 18 May, 2021 First submitted to journal 07 May, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-504608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":27762174,"identity":"583f1879-c762-42af-bdee-d641f449b00f","order_by":0,"name":"Daniel Pollard","email":"data:image/png;base64,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","orcid":"","institution":"University of Sheffield","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Pollard","suffix":""},{"id":27762175,"identity":"8cb8d876-4d7a-4443-a858-c07838a34333","order_by":1,"name":"Gordon Fuller","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gordon","middleName":"","lastName":"Fuller","suffix":""},{"id":27762176,"identity":"70342ed0-14e1-43de-b069-8ada897fcd22","order_by":2,"name":"Steve Goodacre","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Steve","middleName":"","lastName":"Goodacre","suffix":""},{"id":27762177,"identity":"33c7073a-aa0c-4cbe-ba66-a4c61b6f936c","order_by":3,"name":"Eveline A. J. van Rein","email":"","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eveline","middleName":"A. J. van","lastName":"Rein","suffix":""},{"id":27762178,"identity":"3939d616-ba5b-4f96-b80f-a2cc0ca0ff4c","order_by":4,"name":"Job, F. Waalwijk","email":"","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"F.","middleName":"Waalwijk","lastName":"Job","suffix":""},{"id":27762179,"identity":"9155a512-76cf-4994-a5a4-4932af85c99f","order_by":5,"name":"Mark van Heijl","email":"","orcid":"","institution":"University Medical Center Utrecht","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mark","middleName":"van","lastName":"Heijl","suffix":""}],"badges":[],"createdAt":"2021-05-07 10:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-504608/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-504608/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12873-021-00557-6","type":"published","date":"2022-01-11T18:47:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9406076,"identity":"18c38930-1423-48f0-9dd8-49c0dfb0479c","added_by":"auto","created_at":"2021-05-20 21:20:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138169,"visible":true,"origin":"","legend":"The model structure","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-504608/v1/f7094162cc5ff669c7f4770b.png"},{"id":17215868,"identity":"4a96d70f-68af-492f-a9bc-11de7dc1b5e8","added_by":"auto","created_at":"2022-01-11 18:47:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-504608/v1/e4b29939-1ebe-45e9-bdd8-d4a1d1d7dfa7.pdf"},{"id":9406114,"identity":"a567f396-0702-4747-a52e-6eaa248d30ff","added_by":"auto","created_at":"2021-05-20 21:23:18","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":170331,"visible":true,"origin":"","legend":"","description":"","filename":"BMCemergencymedicinepaperAppendixSubmittedApril2021.docx","url":"https://assets-eu.researchsquare.com/files/rs-504608/v1/96bb588783d476f0abdc46e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e An Economic Evaluation of Triage Tools For Patients With Suspected Severe Injuries In England\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eMajor trauma is a significant problem worldwide, with a World Health Organisation report identifying that injuries were responsible for 9% of all deaths in 2012.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Systems and interventions to improve the outcomes of patients with injuries represent a key area in which public health can be improved worldwide.\u003c/p\u003e \u003cp\u003eMajor trauma centres (MTCs), which concentrate severely injured patients in specialist centres, were introduced in England in 2012. Similar systems have been in use in some regions of the USA for many decades. In MTC systems if patients are suspected to be severely injured then paramedics will bypass local hospitals, if these are closer than the MTC, and the MTC will be pre-alerted to allow activation of a specialist major trauma team for resuscitation and initial management. Evidence from the USA shows that patients who have an injury severity score (ISS) of 16 or more would, on average, have better outcomes if they were treated at a major trauma centre.[\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Consequently, severe injuries are often defined in the literature as patients whose ISS was 16 or more. However, ISS can only be derived after the patient has been diagnosed and treated, therefore it is not always clear which hospital the paramedics should decide to transport the patients to. Furthermore, there is a definition of severe injuries that defines severe injuries as those injuries that would benefit from care that is only available at MTCs in a US setting.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] As this is not yet widely used in the literature, we use the ISS is greater than or equal to 16 definition throughout.\u003c/p\u003e \u003cp\u003evan Rein \u003cem\u003eet al.\u003c/em\u003e conducted a systematic review of studies assessing the effectiveness of triage tools in MTC systems.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] This study found that no study which had a high methodological quality produced a tool that had adequate performance. Consequently, there is clinical value in developing, testing and implementing triage tools for patients with suspected major trauma that can be applied by paramedics when initially assessing an injured patient. Existing triage tools consist of physiological, anatomical, injury characteristic, and injury mechanism variables. Sensitivity and specificity are dependent on which variables are included in the triage tool and the cut-off level chosen for constituent variables, to give a positive diagnosis of major trauma. Any triage tool, will trade-off the number of true positive cases correctly admitted to major trauma centres (sensitivity), with the number of true negative cases correctly transported to local hospitals (specificity). However, it is uncertain where the optimal balance of sensitivity and specificity lies as the use of MTCs is associated with improved outcomes for severely injured patients, but also costs more.\u003c/p\u003e \u003cp\u003eThe aim of this paper is to conduct a cost-utility analysis of several plausible major trauma triage tools from the perspective of a UK decision maker. Secondary outcomes of the decision analytic model include system flows of patients throughout the model, as this will influence which tools are feasible.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eModelling approach \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a lifetime patient-level decision tree, followed by a discrete event simulation to model patient flows through an English MTC system. The model estimated patient\u0026rsquo;s outcomes, costs incurred and quality adjusted life years (QALYs) accrued for patients with suspected severe injuries. Conceptual modelling, informed by a previously published model by Newgard \u003cem\u003eet al\u003c/em\u003e and consultation with subject experts informed the final model design.[9] We have taken a patient-level approach for two reasons. Firstly, we can accurately predict the risk of death using a validated 30-day probability of survival equation, developed by the Trauma Audit and Research Network (TARN), in an English population.[10, 11] Secondly, when actual tools are assessed in English populations, this model can be easily adapted to include any correlations that exist between triage tool outcomes and the patient\u0026rsquo;s probability of survival predicted by the TARN survival equation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur model considered patients who were injured outside of the MTC\u0026rsquo;s local area for two reasons. Firstly, patients who live closest to an MTC will usually go to the MTC regardless of the severity of their injury. However, if a patient is thought to be severely injured, the MTC will be pre-alerted. Secondly, the available effectiveness evidence appears to treat all patients who live closest to an MTC as having been treated at an MTC regardless of whether the trauma team were pre-alerted. Consequently, there is no trade-off between cost and effectiveness that can be assessed in our analyses.\u003c/p\u003e\n\u003cp\u003eThe model was populated with simulated patients\u0026rsquo; representative of injured patients presenting to an English trauma system. To generate these characteristics we obtained access to baseline demographic and clinical data from a recent prehospital major trauma triage study by van Rein et al, using the data collected in the Central Netherlands region.[12] This provided a high quality data set, which should be representative of developed world patient. This data is summarised in Table 1. Means, standard deviations and covariances between all parameters were obtained from the van Rein \u003cem\u003eet al\u003c/em\u003e data for patients with complete data for Age, Gender, ISS, Glasgow Coma Scale (GCS) and trauma type.[12] Simulated patients were sampled using correlated draws from these distributions, further details are provided in the appendix.\u003c/p\u003e\n\u003cp\u003eTable 1: A summary of the simulated characteristics of the patients included in the model\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u003cstrong\u003eSD / n/N\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"140\"\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e46.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e21.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" width=\"140\"\u003e\n\u003cp\u003ePatients with complete Age, Gender, ISS, GCS and trauma type data in Van Rein \u003cem\u003eet al\u003c/em\u003e.[12]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003ePercentage Male\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e58.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e2887/4720\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003eISS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003ePercentage with an ISS \u0026ge; 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e9.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e428/4720\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003eGCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e14.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"166\"\u003e\n\u003cp\u003ePercentage with blunt trauma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e98.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e4637/4720\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"601\"\u003e\n\u003cp\u003eSD, standard deviation; ISS, injury severity score; GCS, Glasgow Coma Scale\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNine triage tools were examined, based on Newgard \u003cem\u003eet al\u003c/em\u003e in which triage tools were derived by statistically analysing a retrospective cohort study conducted at 6 sites in the Western US between January 2006 and December 2008.[13] This study was selected as it fit nine triage tools to one dataset, so represented feasible trade-offs between sensitivity and specificity for any new rule. These analyses produced nine triage tools for which, the reported sensitivities and specificities for each tool were:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSensitivity 99.8%, Specificity 2.5%\u003c/li\u003e\n\u003cli\u003eSensitivity 94.8%, Specificity 18.7%\u003c/li\u003e\n\u003cli\u003eSensitivity 90.4%, Specificity 58.4%\u003c/li\u003e\n\u003cli\u003eSensitivity 87.5%, Specificity 62.8%\u003c/li\u003e\n\u003cli\u003eSensitivity 74.6%, Specificity 65.7%\u003c/li\u003e\n\u003cli\u003eSensitivity 69.8%, Specificity 70.1%\u003c/li\u003e\n\u003cli\u003eSensitivity 64.2%, Specificity 76.1%\u003c/li\u003e\n\u003cli\u003eSensitivity 57.0%, Specificity 80.0%\u003c/li\u003e\n\u003cli\u003eSensitivity 99.8%, Specificity 88.6%\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe assumed that the sensitivity and specificity values for each triage tool represented the final triage decision, combining the diagnostic accuracy of the triage tool and the application of judgement by the on-scene paramedics. A recent analysis of a Dutch triage tool in an English data set produced a received-operator curve that would result in similar triage tool sensitivity and specificity as observed by Newgard \u003cem\u003eet al\u003c/em\u003e.[14]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerspective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with guidance from the English and Welsh decision maker, the National Institute for Health and Care Excellence (NICE): we undertook a cost-utility analysis; our analyses had a lifetime horizon; an NHS and personal social services perspective was taken; and, future costs and QALYs were discounted at 3.5%.[15]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome measure of the model was the incremental cost-effectiveness ratio (ICER). Key secondary outcomes of the model included: life expectancy; discounted costs; discounted QALYs; the number of patients who died prior to discharge; the number of patients who died between discharge and one-year post-injury; and, the number of patients sent to an MTC.\u003c/p\u003e\n\u003cp\u003eThe ICERs between the strategies were calculated as difference in cost / difference in QALYs. As we have a decision problem with multiple strategies, a full incremental analysis was undertaken in line with the NICE methods guide.[15] In this approach strategies are ordered by their effectiveness (measured in QALYs). Any tools that are dominated (they produce less QALYs at a higher cost than another tool) or extendedly dominated (a combination of two other tools can produce the same QALYs at a lower cost) were removed from consideration. ICERs were then calculated comparing each remaining tool, except the least effective tool, to the next least effective remaining tool.\u003c/p\u003e\n\u003cp\u003eThe maximum acceptable ICER (MAICER), is the amount of money that a decision-maker is willing to pay to gain 1 additional QALY. NICE\u0026rsquo;s MAICER is usually considered to be \u0026pound;20,000 per QALY, but may increase to \u0026pound;30,000, as detailed in the NICE methods guide.[15] In a full incremental analysis, the most effective tool with an ICER below the decision maker\u0026rsquo;s MAICER is the cost-effective tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Structure and Logic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA diagram of the model is presented in Figure 1. Patients enter the model and the sensitivity and specificity of the triage tool determines whether they go to the MTC or local hospital. For patients who go to a local hospital, there is a probability that they will undergo secondary transfer to the MTC. For patients who the triage tool suggests that they go directly to the MTC, there is a chance that they will initially go to a local hospital for urgent care after which they will undergo a secondary transfer to the MTC. Any patient who goes to the MTC will gain the full benefit from MTC care, but all patients who undergo a secondary transfer will incur costs for an additional ambulance callout. Post-admission, each patient\u0026rsquo;s probability of survival within 30 days is estimated. Patients with an ISS of 16 or more and who did not go to an MTC, have a relative risk of death applied to their 30 day survival probability to reflect the poorer outcomes we expect for these patients. Patients who survive up until 30 days post-injury, have their probability of survival up to one year post-injury estimated. Again, a relative risk of death was applied to increase the probability of death for patients with an ISS of 16 or more who did not receive MTC care. Patients who survive up to one year post-injury, enter a long-term discrete event simulation in which their life expectancy is estimated using general population mortality data, increased by a hazard ratio dependent on their ISS, to estimate their expected remaining life expectancy. This model was developed in R v4.0.2.[16]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProbability of events and effectiveness of MTCs \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the same evidence as the previously published Newgard \u003cem\u003eet al \u003c/em\u003eeconomic model for the effectiveness of MTCs on patient outcomes. These studies are analyses of large cohort studies in a North American setting.[2\u0026ndash;4] The data on the probability of death was updated to include UK data, where this was known to exist. The 2006 TARN survival prediction model was selected for use in our base case economic model, as our patient cohort did not have information on comorbidities which is required in the 2015 TARN survival equation.[10, 11]\u003c/p\u003e\n\u003cp\u003eTable 2: A summary of the parameters used in the model.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003eClinical parameters\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eProbability of patients having a transfer from a local hospital to an MTC if:\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u0026nbsp;They were a true positive\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(ISS \u0026ge; 16 \u0026amp; tool positive)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e26.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"219\"\u003e\n\u003cp\u003eNewgard \u003cem\u003eet al\u003c/em\u003e 2016[32]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u0026nbsp;They were a false negative\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(ISS \u0026ge; 16 \u0026amp; tool negative)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e32.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u0026nbsp;They were a true negative\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(ISS \u0026lt; 16 \u0026amp; tool negative)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e4.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u0026nbsp;They were a false positive\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(ISS \u0026lt; 16 \u0026amp; tool positive)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e7.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eProbability of death within 30 days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eRisk equation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eTARN[10]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eRelative risk of death within 30 days of hospitalisation for patients with an ISS \u0026ge; 16 who were treated at a local hospital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eNewgard \u003cem\u003eet al\u003c/em\u003e 2013[3]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eRelative risk of death within 30 days of hospitalisation for patients with an ISS \u0026lt; 16 who were treated at a local hospital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eAssumption\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eProbability of death between 30 days post-injury and 1-year post-injury for patients with an ISS \u0026ge; 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e3.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"219\"\u003e\n\u003cp\u003eMackenzie \u003cem\u003eet al\u003c/em\u003e. 2006[2]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eRelative risk of death between 30 days and 1 year post-hospitalisation for patients with an ISS \u0026ge; 16 who were treated at an local hospital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eProbability of death between 30 days post-injury and 1-year post-injury for patients with an ISS \u0026lt; 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eDavidson \u003cem\u003eet al \u003c/em\u003e2011[33]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eProbability of death after 1 year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eAge and gender dependant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eONS[34]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eHazard Ratio for the risk of death if someone has a suspected major trauma case with:\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS of less than 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"219\"\u003e\n\u003cp\u003eNewgard \u003cem\u003eet al\u003c/em\u003e 2016[9]\u003c/p\u003e\n\u003cp\u003eCameron et al. 2005[4]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS of greater than or equal to 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e5.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003eUtility parameters\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eUtility for patients with:\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS of 16 or more\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"219\"\u003e\n\u003cp\u003eAhmed \u003cem\u003eet al \u003c/em\u003e[17]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS of 15 or less\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eGeneral population utility\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.9508566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"219\"\u003e\n\u003cp\u003eAra and Brazier[35]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e-0.0002587\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAge squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e-0.0000332\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eMale (1 = male, 0 = otherwise)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.0212126\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eCalculations\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAge and gender matched general population utility for the Ahmed\u003cem\u003e et al \u003c/em\u003epopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.824\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eCalculated. Mean age was 61 years and 59.1% of the analysis population was male in Ahmed \u003cem\u003eet al\u003c/em\u003e.[17]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eUtility multipliers, relative to the utility in the general population, for patients with:\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS of 16 or more\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eCalculated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS between 15 and 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eCalculated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eAn ISS of under 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eWe assumed that these patients would have a utility equal to that of the general population\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003eCost Parameters\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003eAdmission costs \u0026ndash; base case\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eTransfers between local hospitals and MTCs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eAssumed to be one additional ambulance call out. NHS improvement.[23] Currency Code ASS02.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eMTC admission, if ISS is 16 or over\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;2,819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"219\"\u003e\n\u003cp\u003eNHS improvement[22]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eMTC admission, if ISS is less than 16 and over 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;1,466\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003eTreatment of a patient with blunt trauma and an ISS in the range of:\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eISS\u0026le;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;6,198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"219\"\u003e\n\u003cp\u003eChristensen et al[20]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e9\u0026lt;ISS\u0026le;16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;8,989\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e16\u0026lt; ISS\u0026le;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;14,205\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eISS \u0026gt; 25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;21,173\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003eTreatment of a patient with penetrating trauma and an ISS in the range of:\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eISS\u0026le;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;6,501\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" width=\"219\"\u003e\n\u003cp\u003eChristensen et al[21]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e9\u0026lt;ISS\u0026le;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;6,035\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e15\u0026lt; ISS\u0026le;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;9,453\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003e24\u0026lt; ISS\u0026le;34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;12,347\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eISS \u0026gt; 34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;16,438\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003e\u003cem\u003ePost discharge costs\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eCost between discharge and 6 months post treatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026pound;1,766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eJohn Nichol, Personal communication\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eRelative increase in lifetime treatment costs for patients with an ISS \u0026ge; 16 compared to the general population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eCameron \u003cem\u003eet al\u003c/em\u003e. 2006[25]\u003c/p\u003e\n\u003cp\u003eDelgado \u003cem\u003eet al\u003c/em\u003e 2013[24]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eRelative increase in lifetime treatment costs for patients with an ISS \u0026lt; 16 compared to the general population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eCameron \u003cem\u003eet al\u003c/em\u003e. 2006[25]\u003c/p\u003e\n\u003cp\u003eDelgado \u003cem\u003eet al\u003c/em\u003e 2013[24]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"293\"\u003e\n\u003cp\u003eYearly costs of NHS treatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eAge and gender dependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"219\"\u003e\n\u003cp\u003eAsaria 2017[26]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"601\"\u003e\n\u003cp\u003eNB \u0026ndash; distributions and the standard errors around each parameter are provided in the appendix\u003c/p\u003e\n\u003cp\u003elocal hospital \u0026ndash; local hospital; MTC, major trauma centre; ISS, injury severity score\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eUtilities \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe utility parameters used in the model are provided in Table 2. Our utilities are from Ahmed \u003cem\u003eet al\u003c/em\u003e, which is a survey in which 154 patients, whose ISS was 9 or more, completed the EQ-5D-5L questionnaire at an English MTC one year post-injury.[17] There was no evidence in this study that utility varied by ISS score. For patients with an ISS of 9 or more we applied these utilities multiplicatively to age-gender matched utilities for the UK general population.[18] For patients with an ISS of less than 9 we assumed that their injury did not have long term effects on their utility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCosts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe costs in the model reflect English practice and are provided in Table 2. All costs are in 2017/18 prices. Costs from previous years were inflated to 2017/18 prices using the HCHS Pay and Prices inflation index.[19] Other costs incurred within the first 6 months post-injury were obtained from UK based studies and sources.[20\u0026ndash;23][John Nicholl, personal communication] After 6 months we adopt the data reported in Delgado \u003cem\u003eet al\u003c/em\u003e, which was a cost-effectiveness analysis of helicopter versus ground transport in the US.[24] These parameters, which is that based on data from one Canadian study, are an increase in costs for patients with a history of trauma compared to the general population.[25] We used English health care costs incurred by the general population, according to their age and gender, and the data from Delgado et al to calculate the increased long term health care costs incurred by each patient in our model.[24, 26]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA base case deterministic analysis was performed, where all parameters are set to mean values. In order to account for the uncertainty in model inputs a probabilistic sensitivity analysis (PSA) was conducted using Monte Carlo simulation to randomly sample from a distribution assigned to each model parameter (see Appendix). Multiple model runs were performed, each with independent random draws from every parameter\u0026rsquo;s distribution. ICERs were calculated from the mean expected costs and effects over all model runs. We assessed the stability of our model results with respect to the number of patients (assessed visually) and number of PSA runs (assessed using the Hatswell et al method).[27] We found that 25,000 simulated patients and 2,000 PSA runs produced stable results (see Appendix).\u003c/p\u003e\n\u003cp\u003eWe conducted three scenario analyses to explore the robustness of model assumptions.\u003c/p\u003e\n\u003cp\u003eIn the first scenario analysis we used the 2015 TARN survival equation and assumed that the simulated patients in our model were in the same risk category as people with missing Charlson Comorbidity Index (CCI).[11]\u003c/p\u003e\n\u003cp\u003eIn the second scenario analysis, we explored the benefit of MTC care to patients with an ISS between 9 and 15 inclusive, as these patients incur costs for going to an MTC in England implying there may be a belief by payers that these patients would benefit from MTC care.\u003c/p\u003e\n\u003cp\u003eIn the final set of scenario analyses we varied the cost of MTC care, as the cost of MTC care in England is reviewed regularly.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBase Case analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the deterministic base case analysis is given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. All ICERs are in excess of \u0026pound;30,000 per QALY gained, consequently they are above the upper limit of the ICER that NICE would consider acceptable meaning that the cost-effective strategy is the least sensitive triage tool.[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] The PSA results are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The PSA results, in terms of the ICERs and the tools which are dominated or extendedly dominated, are very different to the deterministic results. Consequently all conclusions and scenario analyses are based on the PSA results, as the difference between the deterministic and PSA results for the base case indicates that conducting deterministic analysis introduces bias into the estimated ICER (non-linearity).[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eIn the PSA results, a low sensitivity and high specificity tool results in the least number of cases going to an MTC, the lowest cost and the worst outcomes (probability of death, life expectancy and QALYs). Conversely a highly sensitive and low specificity tool results in the most cases going to an MTC, the highest cost, and the best outcomes. Three strategies have ICERs above \u0026pound;20,000 per QALY gained, but below \u0026pound;30,000 per QALY gained (57% sensitivity, 64.2% sensitivity, 87.5% sensitivity). The two remaining strategies, that were not dominated or extendedly dominated, had ICERs that were above the usual upper limit of NICE\u0026rsquo;s MAICER of \u0026pound;30,000 per QALY gained.\u003c/p\u003e\n\u003cp\u003eThe model results indicate that out of a population of 100,000 patients to whom a major trauma triage tool was applied, 18,448 out of the 100,000 assessed patients would go to an MTC using the most specific tool whereas 97, 860 of the 100,000 assessed patients would go to the MTC using the most sensitive tool. Even when using a very specific triage tool, the majority patients with an ISS\u0026thinsp;\u0026ge;\u0026thinsp;16 would go to an MTC due to transfers from the local hospitals.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe results of the deterministic base case analyses\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTriage Tool\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of cases sent to the MTC per 100,000 patients\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of cases sent to the MTC per 8,916 patients\u003c/p\u003e\n\u003cp\u003e(ISS\u0026thinsp;\u0026ge;\u0026thinsp;16)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of cases sent to the MTC per 91,084 patients\u003c/p\u003e\n\u003cp\u003e(ISS\u0026thinsp;\u0026lt;\u0026thinsp;16)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProportion of patients who died before discharge\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProportion of patients who die between discharge and 1-year post-injury\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean years lived\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean discounted QALYs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean discounted Costs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eICER\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\n\u003cp\u003eDeterministic\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens, 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18,912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14,312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.17%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.0% Sens, 80.0% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28,120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21,900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.14%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.78%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.2% Sens, 76.1% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31,892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25,168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.12%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.78%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.8% Sens, 70.1% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37,536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30,444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.11%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.6% Sens 65.7% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41,672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34,280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.10%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.78%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,793\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens, 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44,976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36,820\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.76%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens, 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49,100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40,736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.08%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;39,584\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.8% Sens, 18.7% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83,116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74,504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.08%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;32,979\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens, 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97,860\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88,948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.06%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.74%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;54,515\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\n\u003cp\u003eProbabilistic (all values are mean values)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens, 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18,448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.78%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.78%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.0% Sens, 80.0% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27,670\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21,339\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.76%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;25,039\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.2% Sens, 76.1% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31,505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24,741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;27,311\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.8% Sens, 70.1% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37,069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29,969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.69%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.6% Sens 65.7% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41,192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,388\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33,804\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.68%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.74%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.590\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens, 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44,499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36,334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.73%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;27,624\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens, 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48,516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,339\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40,177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.73%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;35,791\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.8% Sens, 18.7% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83,383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,603\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74,779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.64%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens, 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97,810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88,906\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.62%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;33,542\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026pound;77,477\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\n\u003cp\u003eMTC, major trauma centre; ISS, injury severity score; QALYS, quality adjusted life years; ICER, incremental cost-effectiveness ratio; Sens, sensitivity; Spec, specificity; ED, extendedly dominated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eScenario analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarises the results of the scenario analyses. When the TARN 2015 survival equation is used and all patients in our simulation are treated as having a missing CCI, the results are remarkably similar to the base case analysis as the strategies which are cost-effective at \u0026pound;20,000 and \u0026pound;30,000 per QALY gained are the same.[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] In the scenario analyses in which patients with an ISS between 9 and 15 inclusive receive a benefit from MTC care, the conclusions of the base case are changed. If the benefit that these patients receive is 25% or 50% of the benefit accrued from MTC care by patients with an ISS of 16 or more, then the most cost-effective tool at an MAICER of \u0026pound;30,000 per QALY gained is the most sensitive triage tool. Although when these patients receive 50% of the benefit of MTC care, the ICER for the most sensitive strategy is only \u0026pound;20,306 per QALY gained (see Appendix) indicating that the NICE\u0026rsquo;s ICER would only have to be a very small amount over the lower end of their usual range of MAICERs to consider the most sensitive rule to be cost-effective in this scenario. In the scenario where these patients receive 75% of the benefit of MTC care that is accrued by patients with an ISS over 16, then the most sensitive triage tool would be cost-effective at MAICERs of both \u0026pound;20,000 and \u0026pound;30,000 per QALY gained.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe results of the scenario analyses\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenario\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCost-effective tool at \u0026pound;20,000 per QALY gained\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCost-effective tool at \u0026pound;30,000 per QALY gained\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBase Case\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens, 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens, 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScenario analyses\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTARN 2015 survival equation with every patient\u0026rsquo;s CCI being missing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens, 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens, 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMTC benefit for people with and ISS in the range 16\u0026thinsp;\u0026gt;\u0026thinsp;ISS\u0026thinsp;\u0026ge;\u0026thinsp;9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMTCs have 25% benefit\u003c/p\u003e\n\u003cp\u003eRR of death prior to discharge\u0026thinsp;=\u0026thinsp;1.07\u003c/p\u003e\n\u003cp\u003eRR of death discharge and one year\u0026thinsp;=\u0026thinsp;1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens, 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens, 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMTCs have 50% benefit\u003c/p\u003e\n\u003cp\u003eRR of death prior to discharge\u0026thinsp;=\u0026thinsp;1.13\u003c/p\u003e\n\u003cp\u003eRR of death discharge to one year\u0026thinsp;=\u0026thinsp;1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens, 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens, 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMTCs have 75% benefit\u003c/p\u003e\n\u003cp\u003eRR of death prior to discharge\u0026thinsp;=\u0026thinsp;1.19\u003c/p\u003e\n\u003cp\u003eRR of death discharge to one year\u0026thinsp;=\u0026thinsp;1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens, 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens, 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eFull results are given in the Appendix\u003c/p\u003e\n\u003cp\u003eQALY, quality adjusted life year; Sens, sensitivity; Spec, specificity; TARN, Trauma Audit and Research Network; CCI, Charlson comorbidty index; MTCs, major trauma centres; RR, relative risk\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the tool that is cost-effective when the cost of MTC care in England is changed. This is assessed at MAICERs of \u0026pound;20,000 and \u0026pound;30,000 per QALY gained. At an MAICER of \u0026pound;20,000 the optimal tool is highly sensitive to the level of best practice tariffs for MTCs in the UK. If the tariffs are set to their 2017/18 levels, then the optimal tool is a highly specific triage tool. If the tariffs were set to \u0026pound;0, then the optimal tool would be a tool with a sensitivity of 88% and a specificity of 63%. These results are similar at MAICERs of \u0026pound;30,000 per QALY gained, with either the tool with a sensitivity of 88% or a sensitivity of 90% being cost-effective.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCost-effective triage tool in the threshold analyses on the cost of MTC care in Engalnd\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eMAICER = \u0026pound;20,000 per QALY gained\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCost of MTC care for patients with\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eISS\u0026thinsp;\u0026ge;\u0026thinsp;16 (rows)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e16\u0026thinsp;\u0026gt;\u0026thinsp;ISS\u0026thinsp;\u0026ge;\u0026thinsp;9 (columns)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1541\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(2020/21 tariff levels)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1,466 (100%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1099.50 (75%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;733 (50%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;366.50(25%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;2961\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(2020/21 tariff levels)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.0% Sens 80.0% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;2,819 (100%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.0% Sens 80.0% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;2114.25 (75%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.0% Sens 80.0% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1409.50 (50%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.0% Sens 80.0% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;704.75 (25%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4% Sens 88.6% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMAICER = \u0026pound;30,000 per QALY gained\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCost of MTC care for patients with\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eISS\u0026thinsp;\u0026ge;\u0026thinsp;16 (rows)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e16\u0026thinsp;\u0026gt;\u0026thinsp;ISS\u0026thinsp;\u0026ge;\u0026thinsp;9 (columns)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1541\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(2020/21 tariff levels)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1,466 (100%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1099.50 (75%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;733 (50%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;366.50(25%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1541\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(2020/21 tariff levels)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;2,819 (100%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;2114.25 (75%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;1409.50 (50%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5% Sens 62.8% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026pound;704.75 (25%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.4% Sens 58.4% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.8% Sens 2.5% Spec\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eMAICER, maximum acceptable incremental cost-effectiveness ratio; ISS, injury severity score; Sens, sensitivity; Spec, specificity\u003c/p\u003e\n\u003cp\u003eFull results as per the base case analysis are available in the Appendix\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eSummary of findings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe cost-effective triage tool for patients with suspected major trauma is highly uncertain, as three potential tools have emerged with ICERs within the range of \u0026pound;20,000 to \u0026pound;30,000 per QALY gained. If the MAICER in the UK for this problem is \u0026pound;20,000 per QALY gained, then the cost-effective triage tool will be a highly specific tool. However, if the MAICER in the UK for this problem is \u0026pound;30,000 per QALY gained then the cost-effective triage tool will be a moderately sensitive tool with a sensitivity in the region of 85\u0026ndash;90%. The sensitivity of these tools are slightly lower than the American College of Surgeons Committee on Trauma\u0026rsquo;s (ACSCOT) recommended sensitivity for any new tool of 95%.[28] The key uncertainties in our ICERs relate to the benefit that MTCs offer to patients with an ISS of between 9 and 15. If this subgroup of patients gains a benefit from MTC care, then the cost-effective tool may be a highly sensitive tool. Furthermore, the results are sensitive to the current cost of MTC care in England, which is determined by best practice tariffs payments made to hospitals.\u003c/p\u003e\n\u003cp\u003eWhen deciding upon the exact MAICER used for a decision problem in England, NICE committees consider: how certain the ICERs are; whether health related quality of life has been adequately captured in utilities; whether they believe the technology is innovative; and, whether the technology helps the NHS meet its non-health objectives. As the MAICERs increase from \u0026pound;20,000 to \u0026pound;30,000 per QALY gained, the committee will make explicit references to these criteria in their judgement as to whether a new technology is cost-effective. Therefore, if the moderately sensitive tool was to be judged cost-effective in our base case analysis, then the tool would have to be judged as meeting one or more of these additional criteria by a NICE guidelines committee on major trauma.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison to previous literature\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDespite large-scale investment in major trauma networks, the cost-effectiveness of major trauma triage is not well studied, with only one previously published economic model available by Newgard \u003cem\u003eet al\u003c/em\u003e.[9] They found in a US setting, implementing a high sensitivity tool (as recommended by ASCOT) was unlikely to be cost-effective. This conclusion is very similar to our findings and shows that developing high sensitivity tools for major trauma are likely to not be a cost-effective use of resources in either the UK or the US based on our current understanding of major trauma systems.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStrength and Limitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis model is the first model of major trauma set in the UK that we are aware of, follows best practice recommendations for health economic evaluations, and brings together the best available evidence to inform decision making on major trauma centres in the England. However, there are a number of limitations in the underlying evidence base that must be taken into account when considering the results and when designing future research projects designed to make well-informed decisions regarding major trauma care in an English or UK wide context.\u003c/p\u003e\n\u003cp\u003eFirstly, all the clinical evidence underpinning the model related to a definition of major trauma based solely on patient\u0026rsquo;s ISS. However, ISS may not be the gold standard definition of major trauma with a new criterion for patients who benefit from major trauma existing.[7, 29, 30] Secondly, a Dutch cohort was used to simulate patients in our model. This provided a high quality data set which should be representative of patients in the developed world, however complete generalisability to the UK, or other settings cannot be guaranteed.[12] Thirdly, whilst most of the data in this model is from the UK setting, further research on the effectiveness of MTCs, the probability of receiving an secondary transfer to an MTC, the effect of having major trauma on patient\u0026rsquo;s long-term outcomes in a UK setting, and the total number of patients with an ISS\u0026thinsp;\u0026ge;\u0026thinsp;16 not transported to an MTC would be desirable. Fourthly, it is thought that patients who go directly to MTCs have better outcomes than patients who receive secondary transfers to the MTC, however quantitative evidence on this effect is lacking meaning that this cannot be accurately quantified in our analyses. Fourhtly, the data on the health care costs incurred by major trauma patients in the UK is old, as it uses TARN data from 2000 to 2005. Therefore, an update of this evidence would be a useful addition to this model. There is a partial update to this evidence, which uses TARN data from 2009 to 2011.[31] However, the population of this study was limited to patients with major trauma who also had severe bleeding. Finally, to conduct a simulated population it was necessary to exclude patients with missing data from the Dutch Cohort.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFuture Research\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs highlighted in the strengths and limitations there are several key areas of research that would improve decision making in the area of major trauma triage these include:\u003c/p\u003e\n\u003cp\u003e1) Estimating the effectiveness of MTC care for patients who meet the Lerner \u003cem\u003eet al\u003c/em\u003e. criteria of major trauma centre need, rather than ISS.[7] Without this evidence we cannot reliably estimate the cost-effectiveness of triage tools designed around these criteria.\u003c/p\u003e\n\u003cp\u003e2) The evidence on the cost of major trauma cases (especially in the long term) in the UK NHS should be updated.\u003c/p\u003e\n\u003cp\u003e3) Further research should be conducted into whether patients with an ISS of between 9 and 15 receive any benefit from MTC care.\u003c/p\u003e\n\u003cp\u003e4) Quantifying the benefit of MTCs for patients sent directly to the MTCs and the patients who receive a secondary transfers should estimated separately.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImplications for practice\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work indicates that based on the current major trauma system in England and the best currently available evidence, if a cost-effective rule is to be selected then we have to accept a non-negligible proportion of severely injured patients (at least 10%) will not initially be identified as needing care at a major trauma centre. Based on current evidence we would expect around 30% of these patients to be transferred to an MTC, but that still leaves 7% of all major trauma cases not receiving the best available care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, cost-effective prehospital trauma triage involves clinically suboptimal sensitivity unless it can be proved that patients with an ISS between 9 and 15 receive a significant benefit from MTC care. Cost-effective trauma triage tools result in a proportion of severely injured patients (at least 10%) being initially transported to local hospitals. Implementing high sensitivity trauma triage tools in England requires development of more accurate decision rules; research to establish if patients with an ISS between 9 and 15 benefit from MTCs; changes in the MTC funding system in England; or, inefficient use of health care resources to manage patients with less serious injuries at MTCs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eList of abbreviations\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAbbreviation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMeaning\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMTCs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMajor trauma centres\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInjury Severity Score\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQALYs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuality adjusted life years\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTARN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrauma audit research network\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlasgow coma Scale\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNICE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational Institute for Health and Care Excellence\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eICER\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncremental cost-effectiveness ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAICER\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum acceptable incremental cost-effectiveness ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePSA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProbabilistic sensitivity analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval, guidelines and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Major Trauma Triage Study, of which this analysis of part, has ethical approval from Bradford Leeds Research Ethics Committee (REC 28\u003csup\u003eth\u003c/sup\u003e June 2019, Reference number 19/YH/0197). All substantial protocol amendments were approved by Bradford Leeds REC and the Health Research Authority (HRA) before implementation.\u003c/p\u003e\n\u003cp\u003eThe data collected by University Medical Center Utrecht was judged by the Medical Ethical Committee of University Medical Center Utrecht, Utrecht, the Netherlands, as not subject to the Medical Research Involving Human Subjects Act and therefore exempt from the need for informed consent.\u003c/p\u003e\n\u003cp\u003eThe study was performed in accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, the only individual patient data used in these analyses was exempt for the need for informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from van Rein \u003cem\u003eet al\u003c/em\u003e.[8] was accessed by request from MH.\u003c/p\u003e\n\u003cp\u003eThe model code, PSA parameters and patient characteristics are available open access under a MIT licence. These are given at: \u003ca href=\"https://doi.org/10.15131/shef.data.13379036.v2\"\u003ehttps://doi.org/10.15131/shef.data.13379036.v2\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo authors report any competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study/project is funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme (grant number 17/16/04). The NIHR had no control over the conduct of the study. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDP had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConception and design:\u003c/em\u003e DP, GF, SG\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcquisition, analysis, or interpretation of data: \u003c/em\u003eDP, GF, SG, EAJR, JFW and MH\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDrafting of the manuscript:\u003c/em\u003e DP, GF, SG\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCritical revision of the manuscript for important intellectual content: \u003c/em\u003eEAJR, JFW, MH\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModel development and Economic analysis: \u003c/em\u003eDP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Orgnaisation. Injuries and Violence The Facts 2014. 2014;:20. https://apps.who.int/iris/bitstream/handle/10665/149798/9789241508018_eng.pdf;jsessionid=B8DC599DEC637E1B22284BDF61C611CD?sequence=1. Accessed 18 Nov 2020.\u003c/li\u003e\n\u003cli\u003eMacKenzie EJ, Rivara FP, Jurkovich GJ, Nathens AB, Frey KP, Egleston BL, et al. A National Evaluation of the Effect of Trauma-Center Care on Mortality. N Engl J Med. 2006;354:366\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eNewgard CD, Staudenmayer K, Hsia RY, Mann NC, Bulger EM, Holmes JF, et al. The cost of overtriage: More than one-third of low-risk injured patients were taken to major trauma centers. Health Aff. 2013.\u003c/li\u003e\n\u003cli\u003eCameron CM, Purdie DM, Kliewer E V., McClure RJ. Long-term mortality following trauma: 10 Year follow-up in a population-based sample of injured adults. J Trauma - Inj Infect Crit Care. 2005.\u003c/li\u003e\n\u003cli\u003eCudnik MT, Newgard CD, Sayre MR, Steinberg SM. Level i versus level II trauma centers: An outcomes-based assessment. J Trauma - Inj Infect Crit Care. 2009.\u003c/li\u003e\n\u003cli\u003ePolites SF, Leonard JM, Glasgow AE, Zielinski MD, Jenkins DH, Habermann EB. Undertriage after severe injury among United States trauma centers and the impact on mortality. Am J Surg. 2018.\u003c/li\u003e\n\u003cli\u003eLerner EB, Willenbring BD, Pirrallo RG, Brasel KJ, Cady CE, Colella MR, et al. A consensus-based criterion standard for trauma center need. In: Journal of Trauma and Acute Care Surgery. 2014.\u003c/li\u003e\n\u003cli\u003evan Rein EAJ, van der Sluijs R, Houwert RM, Gunning AC, Lichtveld RA, Leenen LPH, et al. Effectiveness of prehospital trauma triage systems in selecting severely injured patients: Is comparative analysis possible? American Journal of Emergency Medicine. 2018.\u003c/li\u003e\n\u003cli\u003eNewgard CD, Yang Z, Nishijima D, McConnell KJ, Trent SA, Holmes JF, et al. Cost-effectiveness of field trauma triage among injured adults served by emergency medical services. J Am Coll Surg. 2016.\u003c/li\u003e\n\u003cli\u003eBouamra O, Wrotchford A, Hollis S, Vail A, Woodford M, Lecky F. Outcome prediction in trauma. Injury. 2006.\u003c/li\u003e\n\u003cli\u003eBouamra O, Jacques R, Edwards A, Yates DW, Lawrence T, Jenks T, et al. Prediction modelling for trauma using comorbidity and \u0026ldquo;true\u0026rdquo; 30-day outcome. Emerg Med J. 2015.\u003c/li\u003e\n\u003cli\u003eVan Rein EAJ, Van Der Sluijs R, Voskens FJ, Lansink KWW, Houwert RM, Lichtveld RA, et al. Development and Validation of a Prediction Model for Prehospital Triage of Trauma Patients. JAMA Surg. 2019.\u003c/li\u003e\n\u003cli\u003eNewgard CD, Hsia RY, Mann NC, Schmidt T, Sahni R, Bulger EM, et al. The trade-offs in field trauma triage: A multiregion assessment of accuracy metrics and volume shifts associated with different triage strategies. J Trauma Acute Care Surg. 2013.\u003c/li\u003e\n\u003cli\u003eShanahan T, Fuller GW, Sheldon T, Turton E, Quility FMA, Marincoqitz C. External validation of the Dutch prediction model for prehospital triage of trauma patients in South West region of England, United Kingdom. Forthcommi.\u003c/li\u003e\n\u003cli\u003eNational Institute for Health and Care Excellence. Guide to the methods of technology appraisal. Online Source. 2013;Available Last Accessed: 17th March 2016:1\u0026ndash;93. doi:10.2165/00019053-200826090-00002.\u003c/li\u003e\n\u003cli\u003eR Core Team (2019). R: A language and environment for statistical computing. Accessed 1st April 2019. 2019.\u003c/li\u003e\n\u003cli\u003eW. A, R. A, Ahmed W, Alwe R, Wade D. One-year functional outcomes following major trauma: experience of a UK level 1 major trauma centre. Clin Rehabil. 2017;31:1646\u0026ndash;52. doi:https://dx.doi.org/10.1177/0269215517712044.\u003c/li\u003e\n\u003cli\u003eAra R, Brazier JE. 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Crit Care. 2015.\u003c/li\u003e\n\u003cli\u003eNewgard CD, Yang Z, Nishijima D, McConnell KJ, Trent SA, Holmes JF, et al. Cost-effectiveness of field trauma triage among injured adults served by emergency medical services. J Am Coll Surg. 2016;222:1125\u0026ndash;37. doi:http://dx.doi.org/10.1016/j.jamcollsurg.2016.02.014.\u003c/li\u003e\n\u003cli\u003eDavidson GH, Hamlat CA, Rivara FP, Koepsell TD, Jurkovich GJ, Arbabi S. Long-term survival of adult trauma patients. JAMA - J Am Med Assoc. 2011.\u003c/li\u003e\n\u003cli\u003eOffice for National Statistics. National life tables: UK 2015-2017. Online Source Available from https//www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/datasets/nationallifetablesunitedkingdomreferencetables.\u003c/li\u003e\n\u003cli\u003eAra R, Brazier JE. Populating an economic model with health state utility values: Moving toward better practice. Value Heal. 2010.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Major Trauma, Severe injuries, Triage Tools, Economic Evaluation","lastPublishedDoi":"10.21203/rs.3.rs-504608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-504608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMany health care systems triage injured patients to major trauma centres (MTCs) or local hospitals by using triage tools and paramedic judgement. Triage tools are typically assessed by whether patients with an Injury Severity Score (ISS)≥16 go to an MTC and whether patients with an ISS\u0026lt;16 are sent to their local hospital. There is a trade‐off between sensitivity and specificity of triage tools, with the optimal balance being unknown. We conducted an economic evaluation of major trauma triage tools to identify which tool would be considered cost-effective by UK decision makers.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA patient-level, probabilistic, mathematical model of a UK major trauma system was developed. Patients with an ISS≥16 who were only treated at local hospitals had worse outcomes compared to being treated in an MTC. Nine empirically derived triage tools, from a previous study, were examined so we assessed triage tools with realistic trade-offs between triage tool sensitivity and specificity. Lifetime costs, lifetime quality adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated for each tool and compared to maximum acceptable ICERs (MAICERs) in England. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFour tools had ICERs within the normal range of MAICERs used by English decision makers (£20,000 to £30,000 per QALY gained). A low sensitivity (28.4%) and high specificity (88.6%) would be cost-effective at the lower end of this range while higher sensitivity (87.5%) and lower specificity (62.8%) was cost-effective towards the upper end of this range. These results were sensitive to the cost of MTC admissions and whether MTCs had a benefit for patients with an ISS between 9 and 15. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe cost-effective triage tool depends on the English decision maker’s MAICER for this health problem. In the usual range of MAICERs, cost-effective prehospital trauma triage involves clinically suboptimal sensitivity, with a proportion of seriously injured patients (at least 10%) being initially transported to local hospitals. High sensitivity trauma triage requires development of more accurate decision rules; research to establish if patients with an ISS between 9 and 15 benefit from MTCs; or, inefficient use of health care resources to manage patients with less serious injuries at MTCs.\u003c/p\u003e","manuscriptTitle":"An Economic Evaluation of Triage Tools For Patients With Suspected Severe Injuries In England","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-20 21:20:16","doi":"10.21203/rs.3.rs-504608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-10-26T01:23:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-10-24T09:11:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4000ab7e-d49c-4f58-b87b-fb45071e2bae","date":"2021-09-27T13:22:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-06-07T12:36:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140f4eac-e4fd-483a-ad7f-9ad8879de72c","date":"2021-05-22T17:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44c3ee5a-dc60-4939-b426-4ef9ad779dc5","date":"2021-05-22T09:31:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-05-18T22:18:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-05-18T22:15:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-05-18T17:03:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-05-18T16:53:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2021-05-07T10:33:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"643f2fd0-1864-4cac-acef-9cc541aa9c93","owner":[],"postedDate":"May 20th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4452418,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2022-01-11T18:47:24+00:00","versionOfRecord":{"articleIdentity":"rs-504608","link":"https://doi.org/10.1186/s12873-021-00557-6","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2022-01-11 18:47:24","publishedOnDateReadable":"January 11th, 2022"},"versionCreatedAt":"2021-05-20 21:20:16","video":"","vorDoi":"10.1186/s12873-021-00557-6","vorDoiUrl":"https://doi.org/10.1186/s12873-021-00557-6","workflowStages":[]},"version":"v1","identity":"rs-504608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-504608","identity":"rs-504608","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}