Prediction of neuropsychiatric outcome following mild trauma brain injury: an examination of the Glasgow Coma Scale

What are the strongest indicators of intracerebral hemorrhage in mild traumatic brain injury?

BACKGROUND

Although there are eight factors known to indicate a high risk of intracranial hemorrhage (ICH) in mild traumatic brain injury (TBI), identification of the strongest of these factors may optimize the utility of brain CT in clinical practice. This study aimed to evaluate the predictors of ICH based on baseline characteristics/mode of injury, indications for brain CT, and a combination of both to determine the strongest indicator.

METHODS

This was a descriptive, retrospective, analytical study. The inclusion criteria were diagnosis of mild TBI, high risk of ICH, and having undergone a CT scan of the brain. The outcome of the study was any type of ICH. Stepwise logistic regression analysis was used to find the strongest predictors according to three models: (1) injury pattern and baseline characteristics, (2) indications for CT scan of the brain, and (3) a combination of models 1 and 2.

RESULTS

There were 100 patients determined to be at risk of ICH based on indications for CT of the brain in patients with acute head injury. Of these, 24 (24.00%) had ICH. Model 1 found that injury due to motor vehicle crash was a significant predictor of ICH, with an adjusted OR (95% CI) of 11.53 (3.05 to 43.58). Models 2 and 3 showed Glasgow Coma Scale (GCS) score of 13 to 14 after 2 hours of observation and open skull or base of skull fracture to be independent predictors, with adjusted OR (95% CI) of 11.77 (1.32 to 104.96) and 5.88 (1.08 to 31.99) according to model 2.

DISCUSSION

Open skull or base of skull fracture and GCS score of 13 to 14 after 2 hours of observation were the two strongest predictors of ICH in mild TBI.

LEVEL OF EVIDENCE

III.

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population

Background: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning.

Methods: A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results.

Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene.

Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members.

Level of evidence: This observational study provides a level IV evidence on prognosis after TBI.

Postconcussion syndrome: demographics and predictors in 221 patients

OBJECTIVE The objective of this study was to determine the demographics and predictors of postconcussion syndrome (PCS) in a large series of patients using a novel definition of PCS. METHODS The authors conducted a retrospective cohort study of 284 consecutive concussed patients, 221 of whom had PCS on the basis of at least 3 symptoms persisting at least 1 month. This definition of PCS was uniformly employed and is unique in accepting an expanded list of symptoms, in shortening the postconcussion interval to 1 month from 3 months, and in excluding those with focal injuries such as hemorrhages and contusions. RESULTS The 221 cases showed considerable heterogeneity in clinical features of PCS. They averaged 3.3 concussions, with a range of 0 to 12 or more concussions, and 62.4% occurred during sports and recreation. The median duration of PCS was 7 months at the time of examination, with 11.8% lasting more than 2 years, and 23.1% with PCS had only 1 concussion. The average patient age was 27 years (range 10-74 years). The average number of persistent symptoms was 8.1; 26.2% had a previous psychiatric condition, attention-deficit disorder/attention-deficit hyperactivity disorder, a learning disability, or previous migraine headaches. The prevalence of arachnoid cysts and Chiari malformation in PCS exceeded the general population. Additionally, involvement in litigation, presence of extracranial injuries, amnesia and/or loss of consciousness, and female sex were predictive of reporting a high number of symptoms. A prior history of psychiatric conditions or migraines, cause of injury, number of previous concussions, and age did not significantly predict symptom number. Only the number of symptoms reported predicted the duration of PCS. To predict the number of symptoms for those who fulfilled PCS criteria according to the International Classification of Diseases, 10th Revision (ICD-10), and the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV), the number of previous concussions was significant. CONCLUSIONS PCS is commonly associated with multiple concussions, but 23.1% in the present series occurred after only 1 concussion. Most patients with PCS had multiple symptoms persisting for months or years. The median duration of PCS was 7 months, with a range up to 26 years. In only 11.3%, the PCS had ended at the time of consultation. Not all predictors commonly cited in the literature align with the findings in this study. This is likely due to differences in the definitions of PCS used in research. These results suggest that the use of ICD-10 and DSM-IV to diagnose PCS may be biased toward those who are vulnerable to concussions or with more severe forms of PCS. It is thus important to redefine PCS based on evidence-based medicine.

Serum Levels of Biochemical Markers of Traumatic Brain Injury

Background. A biomarker would be valuable in the diagnosis, risk stratification and prognosis of patients with traumatic brain injury (TBI). Methods. We measured serum levels of S-100β, neuron specific enolase (NSE) and myelin basic protein (MBP) in 50 TBI subjects, and 50 age and gender matched controls. Patients were recruited within 6 hours of the initial injury, they had an initial Glasgow Coma Scale (GCS) score of 14 or less, or a GCS score of 15 with witnessed loss of consciousness (LOC) or amnesia. Results. S-100β, NSE and MBP levels were significantly higher in TBI subjects than in control subjects (P<0.001 for S-100β and NSE; P=0.009 for MBP). Initial S-100β levels were significantly higher in TBI subjects who had not retuned to normal activities 2 weeks following their injury than in TBI subjects who had retuned to normal activities (P=0.022). MBP levels were higher in TBI subjects with positive findings on the baseline CT scan than in CT-negative subjects (P=0.007). Conclusions. S-100β, NSE and MBP may be present in the sera of TBI subjects in elevated quantities relative to controls. S-100β may aid in predicting short-term outcome in TBI subjects.