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

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 countriesParticipants 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.

Traumatic brain injuries in older adults—6 years of data for one UK trauma centre: retrospective analysis of prospectively collected data

“…As increased age is a strong predictor of mortality in prognostic models of outcome, it is likely that neurosurgery was offered to those patients thought most likely to benefit 8 19 22. However, the finding regarding a gender difference needs further exploration.…”

Red Blood Cell Storage Duration and Trauma

“…Another example is the predictive risk model of death that has been developed and validated by the investigators of the CRASH-2 RCT. Their predictive model uses the Glasgow Coma Scale, age, heart rate, systolic blood pressure, time since injury, type of injury, and geographical region [45]. This model was applied in a recently published secondary retrospective analysis from the CRASH-2 trial data to determine whether there was an association between RBC transfusion and predicted risk of death [46].…”

Guide to presenting clinical prediction models for use in clinical settings

“…Graphical score charts are easy to understand and the colour coding can increase ease of use compared with points score systems 14. Additionally, decision guidelines can be coupled to the predictions; for example, dark red implies referral to intensive care.…”