Development of a Clinical Decision Rule for the Early Safe Discharge of Patients with Mild Traumatic Brain Injury and Findings on Computed Tomography Brain Scan: A Retrospective Cohort Study

Abstract

International guidelines recommend routine hospital admission for all patients with mild traumatic brain injury (TBI) who have injuries on computed tomography (CT) brain scan. Only a small proportion of these patients require neurosurgical or critical care intervention. We aimed to develop an accurate clinical decision rule to identify low-risk patients safe for discharge from the emergency department (ED) and facilitate earlier referral of those requiring intervention. A retrospective cohort study of case notes of patients admitted with initial Glasgow Coma Scale 13-15 and injuries identified by CT was completed. Data on a primary outcome measure of clinically important deterioration (indicating need for hospital admission) and secondary outcome of neurosurgery, intensive care unit admission, or intubation (indicating need for neurosurgical admission) were collected. Multi-variable logistic regression was used to derive models and a risk score predicting deterioration using routinely reported clinical and radiological candidate variables identified in a systematic review. We compared the performance of this new risk score with the Brain Injury Guideline (BIG) criteria, derived in the United States. A total of 1699 patients were included from three English major trauma centers. A total of 27.7% (95% confidence interval [CI], 25.5-29.9) met the primary and 13.1% (95% CI, 11.6-14.8) met the secondary outcomes of deterioration. The derived clinical decision rule suggests that patients with simple skull fractures or intracranial bleeding <5 mm in diameter who are fully conscious could be safely discharged from the ED. The decision rule achieved a sensitivity of 99.5% (95% CI, 98.1-99.9) and specificity of 7.4% (95% CI, 6.0-9.1) to the primary outcome. The BIG criteria achieved the same sensitivity, but lower specificity (5%). Our empirical models showed good predictive performance and outperformed the BIG criteria. This would potentially allow ED discharge of 1 in 20 patients currently admitted for observation. However, prospective external validation and economic evaluation are required.

Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach

“…These performance metrics are broadly the same as those recently reported for a classical approach to predictive modelling on the same data set using logistic regression and the BIG criteria, although we report a slightly lower mean NPV (94%) than both the BIG criteria (96.5%) and the logistic regression model (97.7%). 8 The modelling process suggested that the most important variables for predicting deterioration were injury severity, GCS and the number of injuries. While a direct comparison with the previous logistic model developed on these data is not possible, due to some differences in data management and sampling (i.e.…”

“…in the present study the data set was divided into three portions), the largest odds ratios in the logistic model also related to injury severity, GCS and number of injuries. 8 Other predictors in the logistic regression model included extra-cranial ISS value, anti-coagulant and anti-platelet use, an abnormal neurological examination and haemoglobin value. The presence of specific types of injury appeared more important in the machine learning models and this may be due to the modelling being able to account for interactions between injuries when co-occurring.…”

Acute traumatic brain injury presentations and the use of head CT scans in the Emergency Department

“…The most common injury identi ed on CT scan were skull fractures followed by intracranial bleeding, similar to ndings in other studies. 17,19,20 Unsurprisingly, it was signi cantly more likely for those who had lost or may have lost consciousness, had a GCS <13 and those with focal neurology to have a signi cant injury on CT scan. Remarkably, 40% of patients with a signi cant CT nding had a GCS of 15 with no focal neurology.…”

“…This is not an anomalous outcome as other research supports these ndings; for example, studies from Europe and the UK show that between 58-64% of patients with signi cant intracranial injuries seen on CT scan had a GCS 15. 17,20 Clinically this makes risk strati cation without the aid of clinical tools and guidelines di cult and means that clinicians cannot be reassured when a patient appears clinically unharmed. Signi cant intracranial pathology cannot be excluded based solely on clinical history, examination and GCS determination, again exposing the limitations of this approach and justifying the ongoing use of the NICE guidelines despite the low yield of injuries seen following CT head scanning.…”

Performance of the Hull Salford Cambridge Decision Rule (HSC DR) for early discharge of patients with findings on CT scan of the brain: a CENTER-TBI validation study

“…2 We recently developed the first empirically derived prognostic model and decision rule (the Hull Salford Cambridge Decision Rule (HSC DR)) predicting need for hospital admission in this population. 7 We compared the performance of the HSC DR and BIG criteria and found both had high sensitivity to clinical deterioration. The HSC DR maximised sensitivity at a cost of a specificity of 7% at the discharge threshold to ensure clinical safety, but implementation would have recommended fewer than one in ten TBI patients be discharged.…”