Motor vehicle collisions (MVCs) are a major global health burden, especially in low and middle-income countries (LMICs), where formal emergency medical services are often absent. In these settings, laypeople—taxi drivers, police officers, or firefighters—may be first on scene. Without access to monitoring equipment or clinical training, these bystanders face the difficult task of determining which patients need urgent care.
Within this context, trauma triage scores play a critical role. One of the most promising in recent years has been the Bleeding Audit Triage Trauma (BATT) score, which predicts early death from bleeding. While BATT uses physiological variables requiring equipment (such as systolic blood pressure and respiratory rate), the simplified BATT (sBATT) score aims to deliver a similar prognostic tool based on observable signs alone—making it potentially usable by lay responders or in settings where time, entrapment, or access to monitoring devices limits traditional assessments.
This paper by Nutbeam et al. outlines the derivation and validation of the sBATT score using data from over 70,000 UK MVC patients in the Trauma Audit and Research Network (TARN) registry. It proposes a pragmatic, low-tech alternative to traditional trauma scores for early risk stratification—particularly in prehospital and resource-limited settings.
Abstract
Objectives: To develop and validate a simplified Bleeding Audit Triage Trauma (sBATT) score for use by lay persons, or in areas and environments where physiological monitoring equipment may be unavailable or inappropriate.
Design: The sBATT was derived from the original BATT, which included prehospital systolic blood pressure (SBP), heart rate, respiratory rate, Glasgow Coma Scale (GCS), age and trauma mechanism. Variables suitable for lay interpretation without monitoring equipment were included (age, level of consciousness, absence of radial pulse, tachycardia and trapped status). The sBATT was validated using data from the UK Trauma Audit Research Network (TARN) registry.
Setting: Data sourced from prehospital observations from multiple trauma systems in the UK.
Participants: 70 027 motor vehicle collision (MVC) patients from the TARN registry (2012-2019). Participants included were those involved in MVCs, with exclusion criteria being incomplete data or non-trauma-related admissions.
Interventions: Not applicable.
Primary and secondary outcome measures: Death within 24 hours of MVC. Secondary: need for trauma intervention.
Results: In a cohort of 70 027 MVC patients, 1976 (3%) died within 24 hours. The sBATT showed an area under receiver operating characteristic curve of 0.90 (95% CI: 0.90 to 0.91) for predicting 24-hour mortality, surpassing other trauma scores such as the Shock Index and Assessment of Blood Consumption score. Sensitivity was 96% and specificity 72%, with a negative likelihood ratio below 0.1, indicating strong rule-out capability. Sensitivity analyses confirmed consistent performance across varying SBP and GCS thresholds. The sBATT was equally effective across sexes with no significant predictive discrepancies.
Conclusions: The sBATT is a novel, simplified tool that performs well at predicting early death in the TARN dataset. It demonstrates high predictive accuracy for 24-hour mortality and need for trauma intervention. Further research should validate sBATT in diverse populations and real-world scenarios to confirm its utility and applicability.
What Kind of Study Is This?
This is a retrospective validation study. The authors derived a simplified trauma score from the existing BATT model by identifying variables that are observable without medical equipment. The sBATT was then validated using data from TARN, a large UK trauma registry that captures prehospital and in-hospital data on major trauma cases.
Importantly, the study used a single dataset (TARN) for both derivation and validation of the score. The sBATT was compared against several other established trauma scores to assess its discrimination, calibration, and potential clinical utility, especially in ruling out patients at low risk of early death.Tell Me About the Patients
The validation cohort included 70,027 patients injured in MVCs between 2012 and 2019. All patients were entered into TARN, which includes those with an Injury Severity Score (ISS) >9 or those who died or were transferred for specialist care.
- The median age was 46 years.
- About 26% were women.
- 20% of patients were aged ≥65 years.
- 11% were recorded as trapped at the scene.
- The mean ISS was 17, indicating a moderately to severely injured cohort.
- 3% died within 24 hours of the MVC.
- 31% required trauma interventions such as ICU admission, urgent surgery, or blood transfusion.
As expected in a TARN dataset, these patients are skewed towards more serious injuries than the average MVC victim in the community, which has implications for generalisability (more on that later).
What is the sBATT score?
It is derived as shown below. The highest possible score is 14.
| Variable | Finding | Points |
|---|---|---|
| Age | >65 years | +2 |
| ≤65 years | 0 | |
| Consciousness | GCS <15 (not alert) | +4 |
| GCS 15 (alert, oriented) | 0 | |
| Radial Pulse | Absent (suggests SBP <85 mmHg) | +4 |
| Present | 0 | |
| Pulse Rate (by palpation) | Fast (≥100 bpm) | +1 |
| Normal (50–99 bpm) | 0 | |
| Slow (<50 bpm) | +1 | |
| Trapped in Vehicle | Yes | +1 |
| No | 0 |
What Were the Measured Outcomes in This Study?
The primary outcome was death within 24 hours—a logical and clinically relevant endpoint, especially for prehospital and bystander interventions which are most likely to affect early mortality. That said, mortality per-se is not always the best outcome as it is a combination of patients who have the potential to be saved and also those for whom no intervention would make a difference. I’m not sure that I’ve got a better outcome that would work from a database based paper, but it is an issue with any of these studies.
Do you want a score that predicts mortality, or do you want a score that identifies patients in whom intervention makes a difference. There is clearly cross over, but as a thought experiment imagine a trauma system where everything is done for everyone, and fantastically well. In that setting (where mortality only occured when it is inevitable) then mortality would be a terrible outcome measure for sBATT. Thankfully for the authors, such a system really does not exist anywhere!
The secondary outcome was the need for trauma intervention (NFTI), defined as one or more of the following: ICU admission, urgent surgery or embolisation, or urgent blood transfusion. These are useful markers of severe injury and resource utilisation.
What Are the Main Results?
Performance of sBATT:
- Area Under the Receiver Operating Characteristic Curve (AUROC) for sBATT: 0.90 (95% CI: 0.90–0.91), which is pretty good performance for a triage score (as none are perfect)
- At a cut-off of ≥3:
- Sensitivity: 96%
- Specificity: 72%
- Negative Likelihood Ratio (NLR): 0.06
Comparison with Other Scores (for predicting 24-hour mortality):
| Score | AUROC | Sensitivity | Specificity | NLR |
|---|---|---|---|---|
| sBATT ≥3 | 0.90 | 96% | 72% | 0.06 |
| BATT ≥3 | 0.91 | 98% | 55% | 0.04 |
| MGAP <23 | 0.94 | 91% | 86% | 0.11 |
| T-RTS ≤11 | 0.93 | 91% | 84% | 0.11 |
| KTS ≤13 | 0.94 | 98% | 62% | 0.04 |
| Shock Index ≥0.9 | 0.61 | 33% | 90% | 0.75 |
| ABC ≥2 | 0.66 | 5% | 99% | 0.96 |
Notably, while MGAP and KTS performed slightly better overall on AUROC, sBATT offers comparable discrimination with the significant advantage of requiring no monitoring equipment.
Sensitivity analyses confirmed that even when assumptions about GCS or SBP thresholds were altered, the sBATT maintained strong predictive power. Additionally, performance was consistent across sexes—an important point given past concerns about gender inequities in trauma care.
Can I believe the findings?
There’s a lot to like here. The derivation of sBATT is sensible and transparently reported. The variables used—age, consciousness level, presence of a radial pulse, pulse rate, and whether the patient is trapped—are all potentially observable by lay responders or professionals without equipment. These were assigned weights based on regression coefficients from the original BATT model.
The study’s strengths include:
- A large dataset (n=70,027), which improves statistical power.
- A clearly defined and meaningful outcome (24-hour mortality) – with the caveat explained above on triage scores in general.
- Sensitivity analyses exploring variability in threshold definitions.
- Sex-disaggregated analysis showing equitable performance.
But we should be cautious.
Firstly, both derivation and validation were done on the same dataset (TARN). While TARN is robust, this raises the possibility of overfitting, meaning the score might perform worse in different populations or contexts.
Secondly, while the score is designed for layperson use, this study did not test it in the hands of actual laypeople. Can untrained responders reliably assess level of consciousness or detect a weak/absent radial pulse? Possibly—but we don’t yet know.
Thirdly, the TARN database only includes patients with an ISS >9 or who received substantial care. That’s appropriate for validating a score focused on major trauma—but limits insight into how the tool might behave in lower acuity patients, or when used in routine EMS triage. In other words we would need to test it in a real world setting including all potential cases and not those already preselected as major trauma patients.
And finally, this study took place entirely in the UK. MVC victims in LMICs may differ in demographics, injury patterns, and healthcare access. The sBATT will need context-specific validation before widespread use elsewhere.
Should We Change Practice Based on This Study?
Not yet—but we’re close. The sBATT shows promise as a low-tech triage tool with strong predictive performance for early death. Its simplicity and reliance on observable signs make it particularly well-suited to:
- Entrapped patients where monitoring may delay extrication.
- First responders (including police, fire, or trained bystanders).
- Resource-limited or rural settings where equipment is scarce.
It may also improve communication between responders by providing a shared, accessible language to describe patient severity.
However, before we roll it out:
- We need prospective external validation in new populations and settings—particularly outside the UK.
- We need studies assessing feasibility and accuracy when used by lay responders.
- We need to consider how it might integrate into dispatch systems, care pathways (e.g., prehospital TXA), and training programmes.
Importantly, it should not be seen as a replacement for full trauma assessment by trained clinicians when that’s available, but rather as a tool to extend triage capacity when clinical support is limited.
Summary
This is a pragmatic, thoughtful study with global relevance. As emergency clinicians and researchers, we should keep an eye on future validation efforts—and consider whether this kind of tool could one day change how we manage trauma at the very first point of contact.
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References
- Nutbeam T, Stassen W, Foote E, Ageron FX. Derivation and validation of the simplified BleedingAudit Triage Trauma (sBATT) score: a simplified trauma score for major trauma patients injured in motor vehicle collisions. BMJ Open. 2024 Dec 26;14(12):e090517. doi: 10.1136/bmjopen-2024-090517. PMID: 39725425; PMCID: PMC11683913.
- Ageron FX, Coats TJ, Darioli V, et al. Validation of the BATT score for prehospital risk stratification of traumatic haemorrhagic death. Scand J Trauma Resusc Emerg Med. 2021;29(1):6.
- Ageron FX, Coats TJ, Darioli V, Roberts I. Validation of the BATT score for prehospital risk stratification of traumatic haemorrhagic death: usefulness for tranexamic acid treatment criteria. Scand J Trauma Resusc Emerg Med. 2021 Jan 6;29(1):6. doi: 10.1186/s13049-020-00827-5. PMID: 33407716; PMCID: PMC7789642.
- Simon Carley, “Differential prescribing of TXA by gender. St Emlyn’s,” in St.Emlyn’s, May 31, 2022, https://www.stemlynsblog.org/differential-prescribing-of-txa-by-gender-st-emlyn-s/.
