Prognostic model for traumatic death due to bleeding: cross-sectional international study

Francois-Xavier Ageron, Angele Gayet-Ageron, Ewout Steyerberg, Pierre Bouzat, Ian Roberts, Francois-Xavier Ageron, Angele Gayet-Ageron, Ewout Steyerberg, Pierre Bouzat, Ian Roberts

Abstract

Objective: To develop and validate a prognostic model and a simple model to predict death due to bleeding in trauma patients.

Design: Cross-sectional study with multivariable logistic regression using data from two large trauma cohorts.

Setting: 274 hospitals from 40 countries in the Clinical Randomisation of Anti-fibrinolytic in Significant Haemorrhage (CRASH-2) trial and 24 hospitals in the Northern French Alps Trauma registry.

Participants: 13 485 trauma patients in the CRASH-2 trial and 9945 patients in the Northern French Alps Trauma registry who were admitted to hospital within 3 hours of injury.

Main outcome measure: In-hospital death due to bleeding within 28 days.

Results: There were 815 (6%) deaths from bleeding in the CRASH-2 trial and 102 (1%) in the Northern French Alps Trauma registry. The full model included age, systolic blood pressure (SBP), Glasgow Coma Scale (GCS), heart rate, respiratory rate and type of injury (penetrating). The simple model included age, SBP and GCS. In a cross-validation procedure by country, discrimination and calibration were adequate (pooled C-statistic 0.85 (95% CI 0.81 to 0.88) for the full model and 0.84 (95% CI 0.80 to 0.88) for the simple model).

Conclusion: This prognostic model can identify trauma patients at risk of death due to bleeding in a wide range of settings and can support prehospital triage and trauma audit, including audit of tranexamic acid use.

Keywords: audit; bleeding; coagulopathy; death; haemorrhage; human; injury; prognostic; score; trauma.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Relationship between death due to bleeding and potential predictors.
Figure 2
Figure 2
Calibration curves for model development. AUC, area under the curve.
Figure 3
Figure 3
Internal–external cross-validation C-statistics by countries. AUC, area under the curve.
Figure 4
Figure 4
Internal–external cross-validation of calibration slope by countries.
Figure 5
Figure 5
Internal–external cross-validation overall calibration expected and observed number of deaths due to bleeding (E/O) by countries.

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