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Does Helicopter EMS Save More Lives in Trauma?

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Written by Michael Stocker

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Trauma patients attended to by helicopter EMS (HEMS) survived more often than predicted by national risk models, with the greatest gains among moderately severe injuries. Prehospital emergency anaesthesia (PHEA—basically rapid sequence intubation) was independently associated with improved survival.

Elevating outcomes in trauma transport
Determining which transport strategy best serves trauma patients is critical for both systems design and bedside decision-making. This retrospective study compared observed 30-day survival among HEMS-attended trauma patients, with expected survival derived from the UK Trauma Audit & Research Network (TARN) probability-of-survival model, using case-mix-adjusted W-statistics. In short: “Did these patients survive more often than predicted based on externally validated risk models?” The primary outcome was observed-to-expected survival, stratified by predicted survival bands. Case-mix adjustments weighted patients with moderate survival probability (think “sick but salvageable”) more heavily, while deemphasizing those with very low or very high predicted survival.

HEMS-attended patients demonstrated 5.23 excess survivors per 100 compared with TARN predictions, with the largest effect in patients with 25–45% predicted survival. Age and GCS were the strongest mortality predictors, while PHEA was independently associated with improved survival. Patients presenting in traumatic circulatory arrest (TCA) were analyzed separately, with 356 (27.1%) achieving ROSC sustained to hospital arrival, of which 46 survived to 30 days. 93% of TCA patients received at least one critical advanced intervention, with plasma transfusion, endotracheal intubation, and pelvic binders independently associated with ROSC. The physician-staffed UK HEMS model may limit generalizability to most US systems.

How will this change my practice?
As a HEMS doc, this reinforces my bias in favor of HEMS for severe trauma. That said, I would find it more compelling if HEMS patients were directly compared with similarly injured ground-transported patients, rather than against predicted outcomes alone. Without a non-HEMS comparator, the question we face daily—“Will this patient do just as well by ground or do they need air transport?”—remains unclear.

Source
Helicopter Emergency Medical Services attendance is associated with favourable survival outcomes in major trauma: derivation and internal validation of prediction models in a regional trauma system. Emerg Med J. 2026 Feb 3:emermed-2025-215451. doi: 10.1136/emermed-2025-215451. Epub ahead of print. PMID: 41633812.

View JournalFeed Critical Appraisal

Critical Appraisal

Study Identification

Title: Helicopter Emergency Medical Services attendance is associated with favourable survival outcomes in major trauma: derivation and internal validation of prediction models in a regional trauma system
Authors: Joanne Griggs, Jenny Harris, Jack Barrett, Scott Clarke, John O'Neill, Leigh Curtis, Malcolm Tunnicliff, Claire Baker, Duncan Bootland, Emma Ream, Richard Lyon, On behalf of Air Ambulance Charity Kent Surrey Sussex
Journal: Emergency medicine journal : EMJ
Year: 2026
DOI: 10.1136/emermed-2025-215451
Trial Registration: Not applicable
Country / Region: South-East England
Setting: Regional trauma system in South-East England, served by Air Ambulance Charity Kent Surrey Sussex (KSS) HEMS. Patients conveyed to one of three major trauma centers (MTCs).

Background

Trauma is a leading cause of death and disability worldwide, with optimal outcomes dependent on advanced interventions and expedient transfer to specialized trauma care. Helicopter Emergency Medical Services (HEMS) represent a crucial component of many trauma systems, providing advanced clinical capabilities and expedited transport. International evidence for HEMS effectiveness in major trauma remains inconsistent due to methodological heterogeneity, inadequate sample sizes, and variable outcome definitions. Previous studies have shown conflicting results regarding survival benefits, with limited granular analysis across injury severity bands to identify which patient populations derive the greatest benefit from this resource-intensive service.

Study Question

1. To evaluate regional trauma system performance by comparing observed-to-expected survival across probability bands. 2. To identify independent predictors of 30-day mortality in HEMS-attended trauma sub-grouped by ISS ≥15. 3. To determine factors associated with unexpected survival in patients with low predicted survival probability.

Study Design & Conduct

Study Design: Retrospective observational cohort study
Prospective / Retrospective: Retrospective
Multicenter: Yes
Unit of Allocation: Not applicable
Unit of Analysis: Patients
Randomization Method: Not applicable
Allocation Concealment: Not applicable
Blinding: Not applicable
Follow-up Duration: 30 days

Population

Inclusion Criteria:
  • All trauma patients attended and conveyed by Air Ambulance Charity Kent Surrey Sussex (KSS)
  • Met Trauma Audit and Research Network (TARN) inclusion criteria: hospital length of stay (LOS) >72 hours, death in hospital from injury, critical care admission or inter-hospital transfer for specialist care.
Exclusion Criteria:
  • Patients pronounced life extinct (PLE) at the scene (for primary survival analyses)
  • Patients who died at the scene but underwent post-mortem examination
  • Age < 16 years
  • Non-trauma cases
  • Incomplete records
  • No 30-day survival (for W analysis population)
  • Ground assist
  • Stood down
Number Screened: 12361
Number Enrolled: 3225
Number Analyzed: 2125
Key Baseline Characteristics
Age: Median 43 years (IQR 25-61)
Sex: 75.5% male
Disease Severity: Median ISS 24 (IQR 14-33); 72.7% severe trauma (ISS ≥15)
Care Setting Distribution: Patients attended by regional HEMS in South-East England, conveyed to one of three major trauma centers (MTCs).
Additional Baseline Characteristics
  • Blunt trauma (92% of cases), road traffic collisions (58%) most common mechanism.
  • Presenting GCS (median 14, IQR 8-15).
  • Pre-hospital emergency anaesthesia (PHEA) performed in 34.0%.
  • Endotracheal intubation (ETI) in 38.6%.
  • Blood component transfusion in 4.8%.

Exposures / Interventions

Primary Exposure / Intervention
Description: Helicopter Emergency Medical Services (HEMS) attendance
Definition / Dose: Not applicable
Timing: Pre-hospital
Classification Method: Recorded in Electronic Patient Clinical Record (EPCR) HEMSBase 3.0.
Protocolized / Discretionary: Discretionary
Comparator / Reference
Description: Expected survival based on Trauma Audit and Research Network (TARN) P scores.
Definition: P score calculated by TARN using the P17 model incorporating age, sex, GCS, physiological parameters at hospital arrival, and anatomical injuries. Represents expected survival probability for a trauma patient based on their specific combination of demographic, physiological, and injury characteristics compared with similar patients in the historical TARN database.

Outcomes & Results

Primary Outcomes
Standardized mortality ratio (W-statistic)
Definition: W = (observed survivors (O) – expected survivors (E)) × 100 ÷ total patients. Represents excess survivors per 100 patients.
Time Point: 30-day survival
Measurement Method: Calculated using TARN P scores for expected outcomes (E) and actual 30-day survival for observed outcomes (O). Stratified by probability of survival (P) bands.
Results: Overall W-statistic 5.23 (95% CI 3.27 to 7.19, p<0.001). Observed 30-day survival 84.7% vs expected 81.3% (O/E ratio 1.04). Greatest benefit in P 25-45% band (W 3.33, 95% CI 1.37 to 5.29, p<0.001).
Secondary Outcomes
Predictors of 30-day mortality (all HEMS-attended major trauma)
Definition: Factors independently associated with 30-day mortality.
Time Point: 30 days
Measurement Method: Multivariable logistic regression.
Results: Age (aOR 1.02 per year, 95% CI 1.01 to 1.04, p<0.001), GCS (aOR 0.63 per point, 95% CI 0.57 to 0.69, p<0.001), pre-hospital intubation (aOR 3.66, 95% CI 1.91 to 6.99, p<0.001), cardiac arrest (aOR 2.49, 95% CI 1.53 to 4.08, p<0.001), traumatic hemorrhage (aOR 1.64, 95% CI 1.12 to 2.40, p=0.010), thoracic injury (aOR 2.75, 95% CI 1.27 to 5.95, p=0.010) associated with increased mortality. Penetrating injury (aOR 0.33, 95% CI 0.11 to 0.87, p=0.037) and PHEA (aOR 0.55, p=0.021) associated with reduced mortality.
Predictors of 30-day mortality (severe trauma, ISS ≥15)
Definition: Factors independently associated with 30-day mortality in patients with ISS ≥15.
Time Point: 30 days
Measurement Method: Multivariable logistic regression.
Results: Age (aOR 1.02 per year, p<0.001), presenting GCS (aOR 0.61 per point decrease, p<0.001), blood component transfusion (aOR 1.60, p=0.021), cardiac arrest (aOR 1.85, p=0.020), ETI (aOR 2.80, 95% CI 1.40 to 5.60, p=0.004) associated with increased mortality.
Unexpected survival rates in low predicted survival subgroups (P<30 and P<50)
Definition: Patients surviving to 30 days despite low predicted survival probability.
Time Point: 30 days
Measurement Method: Calculated as observed survival in P<30 and P<50 groups. Predictors identified via adjusted analyses.
Results: 41 (26.3%) unexpectedly survived in P<30 group; 128 (38.7%) unexpectedly survived in P<50 group. Predictors of unexpected survival (P<50): younger age (aOR 0.98 per year, 95% CI 0.97 to 0.99, p<0.001), higher GCS (aOR 1.19 per point, 95% CI 1.12 to 1.28, p<0.001), PHEA (aOR 2.01, 95% CI 1.12 to 3.72, p=0.023).
Return of spontaneous circulation (ROSC) rates in traumatic cardiac arrest (TCA)
Definition: Sustained return of circulation (palpable pulse, measurable blood pressure) maintained until hospital handover.
Time Point: Pre-hospital, until hospital handover.
Measurement Method: Annual improvement in ROSC rates. Multivariable logistic regression for predictors.
Results: 356 (27.1%) sustained ROSC to hospital among 1316 TCA patients. Annual improvement of 6.3% increased odds per year (95% CI 1.02 to 1.10, p=0.002). Predictors of ROSC: ETI (aOR 7.05, 95% CI 3.23 to 15.38), plasma units transfused (aOR 1.63 per unit, 95% CI 1.29 to 2.07), pelvic binder (aOR 1.91, 95% CI 1.11 to 3.28, p=0.019). Male sex (aOR 0.62, 95% CI 0.38 to 1.00, p=0.049) and thoracostomy (aOR 0.13, 95% CI 0.07 to 0.25) negatively associated.
30-day mortality in TCA patients
Definition: Alive/dead status at 30 days for TCA patients who achieved ROSC.
Time Point: 30 days
Measurement Method: Observed survival rate.
Results: 46 (24.9%) survived to 30 days among 185 ROSC patients with 30-day data. Post-ROSC in-hospital mortality was 75.1% (139/185).

Risk of Bias

Risk of Bias - ROBINS-I
  • Bias due to confounding (Some concerns): The study adjusted for numerous potential confounders (age, sex, GCS, physiological parameters, anatomical injuries, pre-hospital interventions) using multivariable regression and W-statistic. However, the authors acknowledge that unmeasured confounders (e.g., comorbidities, frailty) and confounding by indication for certain interventions (e.g., blood transfusion, thoracostomy) may still influence outcomes.
  • Bias in selection of participants into the study (Some concerns): Participants were selected based on HEMS attendance and specific TARN inclusion criteria. The authors note that HEMS is routinely dispatched to the most severely injured patients, creating systematic case-mix differences that cannot be adequately matched against ground EMS cohorts, suggesting potential selection bias.
  • Bias in classification of interventions (Low): Intervention data were extracted from a structured Electronic Patient Clinical Record (EPCR) HEMSBase 3.0, which provides a robust method for classifying interventions.
  • Bias due to deviations from intended interventions (Some concerns): As an observational study, there were no 'intended interventions' in the RCT sense. However, the study notes that some interventions (e.g., blood transfusion, thoracostomy) showed inverse associations with survival, likely reflecting confounding by indication where these interventions are preferentially performed in patients with more severe physiological derangement, which could bias their observed effect.
  • Bias due to missing data (Low): Missing data were handled using multiple imputation by chained equations (MICE) with predictive mean matching, and sensitivity analysis comparing complete case analysis showed consistent effect estimates, indicating robust handling of missing data.
  • Bias in measurement of outcomes (Low): 30-day survival was obtained from the national TARN registry, and Return of Spontaneous Circulation (ROSC) was clearly defined and measured, representing objective outcomes.
  • Bias in selection of the reported result (Low): The study reports multiple primary and secondary outcomes, performs internal validation, and discusses limitations, suggesting a comprehensive and unbiased approach to reporting results.
Transparency
Funding Disclosed: TRUE
COI Statement Present: TRUE
This critical appraisal is generated using a structured framework applied to the full text of the original article and is presented separately from the physician’s commentary.

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