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. 2012 Aug 15:345:e5166.
doi: 10.1136/bmj.e5166.

Predicting early death in patients with traumatic bleeding: development and validation of prognostic model

Pablo Perel  1 , David Prieto-Merino, Haleema Shakur, Tim Clayton, Fiona Lecky, Omar Bouamra, Rob Russell, Mark Faulkner, Ewout W Steyerberg, Ian Roberts
Affiliations

Predicting early death in patients with traumatic bleeding: development and validation of prognostic model

Pablo Perel et al. BMJ. .

Abstract

Objective: To develop and validate a prognostic model for early death in patients with traumatic bleeding.

Design: Multivariable logistic regression of a large international cohort of trauma patients.

Setting: 274 hospitals in 40 high, medium, and low income countries

Participants: Prognostic model development: 20,127 trauma patients with, or at risk of, significant bleeding, within 8 hours of injury in the Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage (CRASH-2) trial. External validation: 14,220 selected trauma patients from the Trauma Audit and Research Network (TARN), which included mainly patients from the UK.

Outcomes: In-hospital death within 4 weeks of injury.

Results: 3076 (15%) patients died in the CRASH-2 trial and 1765 (12%) in the TARN dataset. Glasgow coma score, age, and systolic blood pressure were the strongest predictors of mortality. Other predictors included in the final model were geographical region (low, middle, or high income country), heart rate, time since injury, and type of injury. Discrimination and calibration were satisfactory, with C statistics above 0.80 in both CRASH-2 and TARN. A simple chart was constructed to readily provide the probability of death at the point of care, and a web based calculator is available for a more detailed risk assessment (http://crash2.lshtm.ac.uk).

Conclusions: This prognostic model can be used to obtain valid predictions of mortality in patients with traumatic bleeding, assisting in triage and potentially shortening the time to diagnostic and lifesaving procedures (such as imaging, surgery, and tranexamic acid). Age is an important prognostic factor, and this is of particular relevance in high income countries with an aging trauma population.

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Conflict of interest statement

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisation that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

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Fig 1 Association between continuous predictors and death among CRASH-2 patients
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Fig 2 Internal and external discrimination displayed by receiver operating characteristics curves. AUC=area under curve; PV+=positive predictive value; PV–=negative predictive value
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Fig 3 Internal and external calibration of prognostic model by levels of predicted risk
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Fig 4 Chart to predict death in trauma patients. GCS=Glasgow coma score
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Fig 5 Internal and external calibration of simple chart
See this image and copyright information in PMC

References

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