Aims/Objectives/Background GCS 13–15 patients with TBI identified by CT imaging are routinely admitted for observation in the UK. A small proportion of patients clinically deteriorates or requires intervention. We previously derived a prognostic model and decision rule to identify low-risk patients with injuries on CT who could be safely discharged from the ED. Neither has been externally validated.

We aim to externally validate our empirically derived prognostic model and decision rule.

Methods/Design A cohort of initial GCS13-15 patients with injuries on CT was derived from the CENTER-TBI cohort study. CENTER-TBI recruited patients who underwent CT imaging for head trauma between December 2014 and 2017 at 63 centres across Europe and Israel. A composite outcome encompassing need for hospital admission was used, including: seizures, death, intubation, admission to ICU, neurosurgical intervention and neurological deterioration. Performance of the model was assessed by measures of discrimination and calibration. The sensitivity and specificity of the decision rule to the composite outcome was estimated at the discharge threshold.

• Download figure
• Open in new tab
• Download powerpoint

Abstract 59 Figure 1

STROBE flow diagram of selection of study population

Results/Conclusions 1047 of 4509 patients recruited to the CENTER-TBI study met the inclusion criteria. 25.5% (95% CI: 22.9% to 28.2%) clinically deteriorated and 20.2% (95% CI: 17.9% to 22.8%) underwent neurosurgery, died, or were intubated. The prognostic model had an estimated C-static of 0.81 and a calibration slope of 0.5. Our decision rule achieved 100% (95% CI: 97% to 100%) sensitivity and specificity of 4.7% (95% CI: 3.3% to 6.5%) to clinical deterioration. This would allow 3.5% of patients to be discharged- none of whom deteriorated. The decision rule outperformed the BIG criteria, which is used to triage hospital admissions in the USA.

External validation shows our decision rule may be safe for routine use in clinical practice. The inclusion of biomarkers or other novel factors may improve the calibration of the model and the specificity of the decision rule.