This analysis evaluates the cost-effectiveness of current practice compared with the Air Medical Prehospital Triage score for helicopter emergency medical services scene triage of trauma patients.
Key Points
Question
Is a selective triage strategy more cost-effective than current practices for determining which injured patients should be transported by helicopter vs ground ambulance to a trauma center?
Findings
In a nationally representative cohort using cost-effectiveness modeling, current helicopter triage practices have an incremental cost-effectiveness ratio of $255 333 per quality-adjusted life-year compared with using the Air Medical Prehospital Triage score, which is significantly more than the generally accepted threshold of $100 000 per quality-adjusted life-year for cost-effective medical interventions.
Meaning
Current helicopter triage practices are not cost-effective compared with the Air Medical Prehospital Triage score for determining helicopter vs ground transport for trauma patients.
Abstract
Importance
Little evidence exists to guide helicopter emergency medical services (HEMS) triage, and current practice is inefficient. The Air Medical Prehospital Triage (AMPT) score was developed to identify patients most likely to benefit from HEMS compared with ground EMS. To our knowledge, no studies have evaluated the potential effect on costs and outcomes of a more targeted HEMS triage strategy, such as the AMPT score.
Objective
To evaluate the cost-effectiveness of current practice compared with the AMPT score for HEMS scene triage of trauma patients.
Design, Setting, and Participants
A cost-effectiveness Markov model was developed for the US health care system to compare current practice with the AMPT score as HEMS scene triage strategies from the health care system perspective over a patient lifetime horizon. A base case was estimated using national data of patient characteristics from the National Trauma Databank from 2007 to 2012. Model inputs, including demographic information, health care costs, survival, and utility estimates, were derived from literature and national registries. Triage strategies were modeled as probability of HEMS transport. Multilevel logistic regression was used to evaluate survival probability between HEMS and ground EMS under the triage strategies. Costs considered included transport reimbursements, hospitalization, cost of health care in the first year postinjury, and annual cost of health care after the first year postinjury. Several sensitivity analyses were performed to evaluate robustness of model assumptions.
Main Outcomes and Measures
Incremental cost-effectiveness ratio, with a threshold of $100 000 or less per quality-adjusted life-year defining cost-effectiveness.
Results
The base case had an incremental cost-effectiveness ratio of $255 333 per quality-adjusted life-year for current practice compared with the AMPT score. Assuming 20% of patients have severe injuries and assuming HEMS only benefits these patients, current practice had an incremental cost-effectiveness ratio of $176 686 per quality-adjusted life-year. Probabilistic sensitivity analysis demonstrated that current practice is inferior in 85% of iterations, only becoming favored when the cost-effectiveness threshold is greater than $310 000 per quality-adjusted life-year.
Conclusions and Relevance
Current practice is not cost-effective compared with the AMPT score for HEMS scene triage. The AMPT score was the preferred strategy across a range of model input values in sensitivity analyses. The AMPT score identifies patients most likely to benefit from HEMS while potentially reducing costs to the health care system and should be considered in air medical transport protocols for trauma patients.
Introduction
Trauma is the leading cause of death for Americans younger than 47 years, costing the health care system $671 billion annually for lifetime medical and productivity loss in 2013. Injured patients access the health care system through emergency medical services (EMS), either by ground or helicopter transport. Helicopter emergency medical services (HEMS) affords access to care for a significant proportion of the US population but is a costly resource, with an average annual operating cost of $2.5 million per helicopter base and charges of $6500 to $13 000 per transport. In 2011, HEMS accounted for 1% of Medicare ambulance claims but 8% of payments at $420 million.
While HEMS has shown survival benefits, to our knowledge, little evidence exists to guide patient selection for HEMS. Many systems extrapolate existing trauma triage criteria developed to identify patients requiring trauma center care, which may not be appropriate for air medical triage. Further, current practice relies on the judgment of first responders for the need of HEMS transport. High rates of unnecessary HEMS transports resulting in excess costs without health benefits have been reported. A more selective HEMS triage strategy could potentially reduce health care system costs for injured patients in the United States.
Our group developed the Air Medical Prehospital Triage (AMPT) score to identify patients at the scene of injury most likely to benefit from HEMS transport. The AMPT score incorporates 7 criteria available at the scene that can be applied to individual patients (Table 1). Prior studies suggest that the AMPT score is able to discriminate between injured patients who have a survival benefit if transported by HEMS and patients who do not. However, to our knowledge, no data exist evaluating the costs and benefits of a more targeted HEMS triage strategy, such as the AMPT score. Therefore, our objective was to evaluate the cost-effectiveness of current triage practices compared with the AMPT score for HEMS triage of trauma patients at the scene of injury. We hypothesize the AMPT score will be more cost-effective than current HEMS triage practices.
Table 1. Air Medical Prehospital Triage (AMPT) Score.
| Criterion | Points |
|---|---|
| Glasgow Coma Scale score <14 | 1 |
| Respiratory rate <10 or >29 breaths/min | 1 |
| Unstable chest wall fracturesa | 1 |
| Suspected hemothorax or pneumothoraxb | 1 |
| Paralysis | 1 |
| Multisystem traumac | 1 |
| Physiologic plus anatomic criteriad | 2 |
| Helicopter transport should be considered if the AMPT score ≥2 | |
Any chest wall instability or deformity, including flail chest or multiple rib fractures on physical examination.
Absence of breath sounds on affected hemithorax along with objective signs of respiratory distress (eg, cyanosis, peripheral oxygen saturation < 92%, or signs of tension physiology).
Three or more anatomic body regions injured.
Any 1 physiologic criterion along with any 1 anatomic criterion present from American College of Surgeons Committee on Trauma National Field Triage guidelines.
Methods
Study Design and Model Inputs
A cost-effectiveness Markov model was developed to compare current triage practices with the AMPT score as HEMS scene triage strategies from the health care system perspective over a patient lifetime horizon. Model inputs were obtained from the National Trauma Databank (NTDB), the National Study on the Costs and Outcomes of Trauma, the US Centers for Medicare and Medicaid Services Ambulance Fee Schedule, and published literature (eMethods 1 in the Supplement). This model was applied to a nationally representative cohort of injured patients. The model follows a patient from time of injury through transport to a trauma center by either ground EMS (GEMS) or HEMS, hospitalization, first year postinjury, and the remainder of their life, assuming no additional injury event. This study was exempt from review by the University of Pittsburgh Institutional Review Board, and informed consent was waived because data were deidentified.
The primary outcome was the incremental cost-effectiveness ratio (ICER), representing the cost paid by the health care system in dollars per quality-adjusted life-year (QALY) gained comparing current practice for HEMS triage with triage using the AMPT score. The ICER was calculated as the difference in cost between the 2 strategies divided by the difference in QALYs between the 2 strategies. An ICER threshold of $100 000/QALY or less was applied, which is a generally accepted value of cost-effective medical interventions in high-income countries.
Decision Analytic Markov Model
A decision analytic tree model was developed with a decision point choosing either current practice or the AMPT score as a triage strategy (Figure 1). Patients evaluated at the hospital then entered the Markov model. Patients could enter the model through 2 health states, the first representing patients who were discharged alive from the hospital and the second representing patients who died during hospitalization. Surviving patients either died within 1 year of injury or survived the first year postinjury. Patients could then progress through each Markov cycle either surviving an additional cycle or dying, with death representing an absorbing health state.
Figure 1. Decision Analytic Tree With Markov Model.
A, Decision analytic tree comparing current practice with the Air Medical Prehospital Triage (AMPT) score as scene transport strategies for allocating patients to either helicopter emergency medical services (HEMS) or ground emergency medical services (GEMS). B, Markov model.
Model Assumptions
Decision tree branch points were populated with the probability of discrete mutually exclusive events (Table 2). Triage strategies were modeled as probability of HEMS or GEMS transport. For the AMPT score triage strategy, the AMPT score was applied to the national NTDB cohort to derive a triage allocation to either HEMS or GEMS irrespective of the patient’s actual transport mode. For the current practice triage strategy, the proportion of patients actually undergoing HEMS or GEMS transport was input as the probability of triage to HEMS and GEMS transport. Fatal crash risk was modeled as the probability of a fatal crash per mile traveled for HEMS and GEMS (eMethods 2 in the Supplement).
Table 2. Model Input Assumptions.
| Variable | Base Case Value | Sensitivity Analysis Range | Distribution | Source |
|---|---|---|---|---|
| Cohort characteristics | ||||
| Age, mean, y | 47 | NA | Normal | NTDB |
| Injury Severity Score >15, % | 20.46 | 5-50 | Beta | NTDB |
| Length of stay, d | 3 | 1-14 | Log normal | NTDB |
| Transport | ||||
| Probability of HEMS transport under current practice strategy | 0.165 | 0.10-0.20 | Triangular | NTDB |
| Probability of HEMS transport under AMPT strategy | 0.095 | 0.05-0.15 | Triangular | NTDB |
| Transport distance, miles | 55 | 25-85 | Triangular | Brown et al |
| Probability of fatal HEMS crash per mile traveled | 0.00000016 | 0.000000016-0.0000016 | Triangular | Blumen and Lees |
| Probability of fatal GEMS crash per mile traveled | 0.00000008 | 0.000000008-0.0000008 | Triangular | Levick |
| Costs, $ | ||||
| HEMS service charge per patient | 7172.37 | 4000-10 000 | Triangular | CMS ambulance fee schedule |
| GEMS service charge per patient | 1048.99 | 500-1500 | Triangular | CMS ambulance fee schedule |
| Cost of hospitalization | 58 172 | 15 000-100 000 | Normal | NSCOT |
| Cost of health care within 1 y after injury | 36 593 | 7000-50 000 | Normal | NSCOT |
| Cost of health care >1 y after injury | CMS annual expenditures | 6000-50 000 | Triangular | Delgado et al |
| Crash sequelae | ||||
| Vehicle cost of GEMS crash, $ | 144 900 | 129 900-169 900 | Triangular | Retail web site |
| Vehicle cost of HEMS crash, $ | 4.6 million | 3.2 million-5.5 million | Triangular | Airbus helicopters and bell helicopters |
| QALYs lost in GEMS crash | 30 | 10-50 | Uniform | Delgado et al |
| QALYs lost in HEMS crash | 120 | 30-200 | Uniform | Delgado et al |
| Outcomes | ||||
| Probability of in-hospital survival for patients transported by GEMS | 0.9520 | 0.80-0.98 | Beta | NTDB |
| Probability of in-hospital survival for patients transported by HEMS assigned using AMPT strategy | 0.9566 | 0.85-0.99 | Beta | NTDB |
| Probability of in-hospital survival for patients transported by HEMS assigned using current practice strategy | 0.9556 | 0.85-0.99 | Beta | NTDB |
| Probability of surviving 1 y after discharge alive | 0.97 | 0.95-0.99 | Beta | MacKenzie et al |
| Utility during hospitalization | 0.3 | 0.1-0.7 | Triangular | Assumed |
| Utility discharged alive | 0.6 | 0.5-0.7 | Normal | Delgado et al |
| Utility 1 y after injury | 0.7 | 0.6-0.8 | Normal | NSCOT; Delgado et al |
| Annual probability of mortality >1 y after injury | US annual life tables | NA | NA | CDC; Delgado et al |
| Annual utility >1 y after injury | Health and activity limitation index | 0.55-0.75 | Normal | Gold et al; NSCOT |
| Model variables | ||||
| Discount rate, % | 3 | NA | NA | Weinstein et al |
| Cycle length, y | 1 | NA | NA | NA |
Abbreviations: AMPT, Air Medical Prehospital Triage; CDC, US Centers for Disease Control and Prevention; CMS, US Centers for Medicare and Medicaid Services; GEMS, ground emergency medical services; HEMS, hospital emergency medical services; NA, not applicable; NSCOT, National Study on the Costs and Outcomes of Trauma; NTDB, National Trauma Databank; QALY, quality-adjusted life-years.
For patients evaluated at the hospital, the probability of survival in the NTDB was used to model patient entry into the Markov model as either discharged alive or dead. Patients transported by GEMS in the model were divided between these 2 health states based on the probability of survival in the NTDB for patients actually transported by GEMS.
For patients transported by HEMS under the current triage strategy, a risk-adjusted odds ratio for survival of HEMS compared with GEMS transport was obtained from a multilevel random coefficient logistic regression model incorporating fixed effects for demographic characteristics, mechanism, vital signs, injury severity, and trauma center level while accounting for center-level clustering (eMethods 3 in the Supplement). This risk-adjusted odds ratio was then converted to an absolute increase in survival to obtain the probability of survival for patients transported by HEMS under the current practice strategy. For patients transported by HEMS under the AMPT score strategy, the same methods were applied to obtain a risk-adjusted odds ratio and absolute increase in survival for HEMS. The resulting probabilities of survival are shown in Table 2.
The probability of mortality within 1 year of injury after being discharged alive was obtained from MacKenzie et al. Annual probability of mortality after 1 year postinjury was obtained from US life tables, inflated for higher mortality after severe injury observed compared with the general population.
A base case was estimated using national patient characteristics from the NTDB and literature (Table 2). Base case inputs included mean patient age, mean length of hospitalization, and mean transport distance. Mean transport distance in miles was estimated assuming average speed of 120 mph from transport time for HEMS transports. The Markov cycle length was set to 1 year, and a 3% discount rate was applied to costs and QALYs.
Costs
Costs in the model include transport costs, hospitalization costs, cost of health care in the first year postinjury, and annual health care costs after the first year postinjury (Table 2). All costs were adjusted to 2015 US dollars using the Consumer Price Index. All economic inputs represent estimated costs, expenditures, or reimbursements rather than charges.
Transport costs were based on the Centers for Medicare and Medicaid Services Ambulance Fee Schedule (eMethods 4 in the Supplement). Hospitalization costs were estimated as index hospitalization costs from trauma centers in the National Study on the Costs and Outcomes of Trauma and discounted 14.35% for patients who died during hospitalization. Health care costs for patients surviving the first year postinjury were estimated from National Study on the Costs and Outcomes of Trauma 1-year costs for patients treated at trauma centers, less hospital care costs. Annual lifetime health care costs after the first year postinjury were obtained from Centers for Medicare and Medicaid Services mean annual health expenditures across age groups and inflated by a factor of 1.45 for patients with severe injury to account for the nearly 50% increase in health service utilization costs compared with a noninjured population (eMethods 5 in the Supplement). Total lifetime patient costs were calculated as the sum of transport, hospitalization, 1-year health care costs, and annual postinjury health care costs.
Costs to the health care system were also included in the event of a fatal crash (Table 2). The cost of replacing a ground ambulance was obtained from a representative manufacturer website. The cost of replacing a helicopter was estimated from helicopter manufactures based on the 3 most common helicopter models used for HEMS in the United States.
Health Outcomes
Health outcomes were evaluated as health state utilities in the form of QALYs. Utilities for hospitalization, discharge alive, 1 year postinjury, and annually were accounted for in the model (Table 2). Utility at discharge and 1 year postinjury were obtained from literature. After the first year of injury, utility annually declines with age and was estimated by the median Health and Activity Limitation Index by age. These utilities were decreased by 30% to reflect a lower baseline utility after surviving severe injury. Finally, loss in QALYs to the health care system were estimated for the loss of ambulance or helicopter crews in the event of a fatal crash from Delgado et al.
Sensitivity Analyses
Several sensitivity analyses were performed to evaluate the robustness of results to model assumptions. The first was a structural sensitivity analysis that altered the model to assume 20.46% of patients were severely injured based on the overall proportion of patients in the NTDB with an Injury Severity Score (ISS) greater than 15. Further, it was assumed only these severely injured patients have a survival benefit from HEMS transport. Thus, the same probability of survival was assigned to patients with an ISS of 15 or less transported by either HEMS or GEMS. These changes in probabilities, costs, and utilities were modeled as weighted averages for patients with an ISS greater than 15 and those with an ISS of 15 or less (eMethods 6 in the Supplement).
This sensitivity analysis is based on the overall proportion of patients with an ISS greater than 15 and assumes the same distribution of patients with an ISS greater than 15 across HEMS and GEMS transport in both triage strategies. Because there is some patient selection occurring under both strategies, this assumption may not accurately reflect the distribution of ISS across transport modes and triage strategies. To further explore this, the model was revised to reflect the actual distribution of ISSs greater than 15 in the NTDB for each transport mode under each triage strategy.
One-way sensitivity analysis was performed across model variables. The most influential variables were selected for 2-way sensitivity analysis to evaluate the effect of varying both inputs simultaneously. Finally, probabilistic sensitivity analysis was performed using 10 000 Monte Carlo simulations (eMethods 7 in the Supplement). A cost-effectiveness acceptability curve was generated to evaluate the proportion of Monte Carlo iterations that are cost-effective under each HEMS triage strategy across a range of ICER threshold values. Stata version 13MP (StataCorp) was used for statistical analysis, and TreeAgePro version 2014 (TreeAge Software) was used for cost-effectiveness modeling.
Results
The base case demonstrated higher cost and slightly higher effectiveness for current practice compared with the AMPT score. This results in an ICER of $255 333/QALY for current practice compared with the AMPT score (Table 3). Under the assumption that 20% of patients had an ISS greater than 15 and that a survival benefit for HEMS transport occurs only in these patients, current practice had an ICER of $176 686/QALY compared with the AMPT score as triage strategies. The actual proportion of patients with an ISS greater than 15 across transport modes in the current practice strategy demonstrated 40.79% of patients transported by HEMS and 16.87% of patients transported by GEMS had an ISS greater than 15. In the AMPT score strategy, 80.06% of patients transported by HEMS and 13.38% of patients transported by GEMS had an ISS greater than 15. Analysis under the actual distribution of severely injured patients across transport modes demonstrated current practice was more expensive and less effective, resulting in this strategy being dominated by the AMPT score strategy (Table 3).
Table 3. Health and Economic Outcomes.
| Triage Strategy | $ | QALY | ICER/QALY, $ | ||
|---|---|---|---|---|---|
| Cost | Incremental Cost | Effectiveness | Incremental Effectiveness | ||
| Base case | |||||
| AMPT | 396 210 | NA | 11.99269 | NA | NA |
| Current practice | 396 698 | 488 | 11.99460 | 0.00191 | 255 333 |
| ISS >15 in 20% of cohort overall | |||||
| AMPT | 275 128 | NA | 12.36998 | NA | NA |
| Current practice | 275 618 | 490 | 12.37275 | 0.00277 | 176 686 |
| Actual distribution of ISS >15 across transport mode | |||||
| AMPT | 273 820 | NA | 12.40434 | NA | NA |
| Current practice | 276 130 | 2310 | 12.37363 | −0.03071 | Dominated |
Abbreviations: AMPT, Air Medical Prehospital Triage; ICER, incremental cost-effectiveness ratio; ISS, Injury Severity Score; NA, not applicable; QALY, quality-adjusted life-years.
One-way sensitivity analysis demonstrated that the probability of HEMS transport, probability of survival for HEMS transport, and cost of HEMS transport were the most influential model inputs (eFigure 1 in the Supplement). Two-way sensitivity analysis of the probability of HEMS transport under both strategies revealed that for current practice to be cost-effective, the AMPT score would need to have a probability of HEMS transport 5% higher than current practice (eFigure 2 in the Supplement). Two-way sensitivity analysis of the probability of survival for HEMS transport under both strategies revealed that the AMPT score remained the cost-effective strategy until the mortality rate of patients transported by HEMS using the AMPT score was more than 2% higher than mortality of patients transported by HEMS under current practice (eFigure 3 in the Supplement).
Probabilistic sensitivity analysis revealed that the AMPT score strategy is the cost-effective strategy in 85% of iterations at the $100 000/QALY threshold (eFigure 4 in the Supplement). Current practice as a HEMS triage strategy does not achieve the majority (greater than 50%) of cost-effective iterations until the ICER is greater than $310 000/QALY (Figure 2).
Figure 2. Cost-effectiveness Acceptability Curve.
Cost-effectiveness acceptability curve across a range of cost-effectiveness thresholds from probabilistic sensitivity analysis. The Air Medical Prehospital Triage (AMPT) score is cost-effective in 85% of iterations at $100 000 per quality-adjusted life-year, while current practice only has more cost-effective iterations when the cost-effectiveness threshold is greater than $310 000 per quality-adjusted life-year. All costs were adjusted to 2015 US dollars using the Consumer Price Index.
Discussion
To our knowledge, this is the first study to evaluate the cost-effectiveness of triage strategies for HEMS transport of injured patients. Our results demonstrate that the cost per QALY gained using current HEMS triage practices is more than 2.5-fold greater than generally accepted cost-effectiveness thresholds in the United States compared with the AMPT score. When assuming only severely injured patients benefit from HEMS, current triage practices are still not cost-effective. Further, when using the actual distribution of severely injured patients across transport modes in each triage strategy, current practice was both costlier and less effective than the AMPT score.
Sensitivity analyses were performed to ensure our results were robust to changes in model assumptions across a range of clinically plausible values. To achieve cost-effectiveness of current triage practices, the AMPT score would have to assign at least 5% more patients to HEMS transport than current practice. Because the AMPT score is a more selective triage strategy, this is unlikely.
The AMPT score remained the cost-effective triage strategy until the risk-adjusted mortality for patients transported by HEMS was at least 2% greater than current practice. Because the AMPT score was developed to identify patients most likely to have a survival benefit from HEMS, current practice is unlikely to have a lower mortality rate. Further, we used a conservative estimate of the AMPT score treatment effect, ignoring patients that should have gone by HEMS but were transported by GEMS with high risk of death. Including these patients suggests an even higher survival benefit for the AMPT score.
Probabilistic sensitivity analysis suggested current triage practices did not become cost-effective until the cost-effectiveness threshold is greater than $310 000/QALY, more than 3-fold the generally accepted cost-effectiveness threshold. Even if using more liberal cost-effectiveness ICER thresholds of $150 000/QALY to $200 000/QALY, as suggested by some for intervention in the United States, our results suggest the AMPT score strategy would be favored over current practice.
Helicopter emergency medical services triage strategy has significant potential implications for the US health care system. Helicopter emergency medical services have expanded significantly in the United States over the last decade, and studies report high rates of unnecessary use and overtriage among patients transported by HEMS. This aligns with our findings suggesting current HEMS triage practices result in high costs, as many patients are transported by helicopter with marginal improvements in outcomes.
The economic effect to the health care system of potentially unnecessary transports is not trivial. Charges can range between $6500 and $13 000 per transport to cover annual operating costs between $2.2 and $2.7 million per helicopter base for aircraft maintenance, fuel, equipment, and personnel required for around-the-clock response readiness. A 2011 Centers for Medicare and Medicaid Services report demonstrated $5.3 billion was spent on medical transport, with HEMS claims resulting in 15-fold higher payment per transport than GEMS claims.
This study has policy implications for the US health care system, given the mortality, morbidity, and economic burden of trauma in the United States. Rising HEMS charges and unnecessary transports are increasing. As focus shifts toward cost-effective care in the United States, the AMPT score represents a potential source of cost savings to the US health care system without reducing the quality of care delivered. This is achieved by reducing the number of HEMS transports for which there is low likelihood of survival benefit to the patient using simple criteria available in the field. Further, the AMPT is not simply a restrictive strategy but identifies patients most likely to benefit from HEMS transport and may reduce undertriage as well. Thus, to our knowledge, the AMPT score represents the first evidence-based air medical triage tool available. Local and regional trauma systems should consider integrating the AMPT score into air medical triage protocols. Further, there may be payer implications, as the AMPT score may form a starting point for evidence-based reimbursement of HEMS charges.
Limitations
This study has several limitations. First, cost-effectiveness models require parameter inputs derived from existing data and literature. This requires many assumptions, and the results are specific to these data and assumptions. The data used for model inputs came from a variety of sources across varied populations and time periods, assuming each is representative of national trauma patients. Survival benefits were assumed at a binary ISS threshold; however, there are likely diminishing benefits at extreme levels of injury. Model inputs derived from literature were not specific to patients transported by HEMS or GEMS for utility and QALYs. Utilities were deflated 30% annually to reflect lower quality of life after severe injury; however, this was applied uniformly across patients and time, which may not reflect true utility for specific types of injury or over long periods of time. Further, several inputs were derived empirically from NTDB data using observational analysis methods and are subject to potential unobserved confounding. We did perform several sensitivity analyses to evaluate the robustness of our conclusions to variations in these data and assumptions, which demonstrated similar results.
The AMPT score is based on patient-level characteristics identified in the field. Application of the score was based on NTDB anatomic diagnoses, and it is unclear if these specific injuries were identified by EMS personnel in the field. Further, the AMPT score does not account for local geography and distance to the trauma center. Some patients under current triage practices may have been triaged to HEMS transport from rural areas with long distances to the closest trauma center rather than because of patient condition. Rural areas may use HEMS transport for less injured patients as long-distance transport by GEMS may leave the region without EMS resources for prolonged periods. Adverse weather can preclude HEMS transport for safety reasons, resulting in triage misclassification under current practices. These logistical considerations will vary by individual trauma system and need to be addressed on a regional level. Thus, the AMPT score is not intended for use in isolation but in conjunction with individual system factors for air medical triage protocols.
Conclusions
Current triage practices are not cost-effective compared with the AMPT score for HEMS triage. The AMPT score was the preferred triage strategy across a range of scenarios, indicating robustness of our results. The AMPT score identifies patients most likely to benefit from HEMS transport while potentially reducing costs to the US health care system. Given the significant health and economic effect of injury in the United States, the AMPT score should be considered when developing air medical transport protocols for trauma patients.
eMethods 1. Data use acknowledgements.
eMethods 2. Fatal crash probability.
eMethods 3. Risk adjustment models and conversion of adjusted odds ratios to probability of survival.
eMethods 4. Calculation of transport costs.
eMethods 5. Annual health care expenditures.
eMethods 6. Model inputs by Injury Severity Score for structural sensitivity analysis.
eMethods 7. Variable distribution parameters for probabilistic sensitivity analysis.
eFigure 1. Incremental cost-effectiveness ratio (ICER) tornado diagram from 1-way sensitivity analysis.
eFigure 2. Two-way sensitivity analysis of probability of helicopter emergency medical services (HEMS) transport under the Air Medical Prehospital Triage (AMPT) score triage strategy (pHEMS_AMPT) and probability of HEMS transport under the current practice triage strategy (pHEMS_SOC).
eFigure 3. Two-way sensitivity analysis of probability of survival for helicopter emergency medical services (HEMS) transport under the Air Medical Prehospital Triage (AMPT) score triage strategy (psurvivalHEMS_AMPT) and probability of survival for HEMS transport under the current practice triage strategy (psurvivalHEMS_SOC).
eFigure 4. Probabilistic sensitivity analysis with 10 000 Monte Carlo iterations by incremental cost and effectiveness comparing the current practice strategy with the AMPT score strategy for HEMS triage.
eReferences.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods 1. Data use acknowledgements.
eMethods 2. Fatal crash probability.
eMethods 3. Risk adjustment models and conversion of adjusted odds ratios to probability of survival.
eMethods 4. Calculation of transport costs.
eMethods 5. Annual health care expenditures.
eMethods 6. Model inputs by Injury Severity Score for structural sensitivity analysis.
eMethods 7. Variable distribution parameters for probabilistic sensitivity analysis.
eFigure 1. Incremental cost-effectiveness ratio (ICER) tornado diagram from 1-way sensitivity analysis.
eFigure 2. Two-way sensitivity analysis of probability of helicopter emergency medical services (HEMS) transport under the Air Medical Prehospital Triage (AMPT) score triage strategy (pHEMS_AMPT) and probability of HEMS transport under the current practice triage strategy (pHEMS_SOC).
eFigure 3. Two-way sensitivity analysis of probability of survival for helicopter emergency medical services (HEMS) transport under the Air Medical Prehospital Triage (AMPT) score triage strategy (psurvivalHEMS_AMPT) and probability of survival for HEMS transport under the current practice triage strategy (psurvivalHEMS_SOC).
eFigure 4. Probabilistic sensitivity analysis with 10 000 Monte Carlo iterations by incremental cost and effectiveness comparing the current practice strategy with the AMPT score strategy for HEMS triage.
eReferences.