A practical method for dealing with missing Glasgow Coma Scale verbal component scores

OBJECTIVE

The Glasgow Coma Scale (GCS) is used for the assessment of impaired consciousness; however, it is not always possible to test each component, most commonly the verbal component. This affects the derivation of the GCS sum score, which has a role in systems for predicting patient outcome. Imputation of missing scores does not add extra information, but it does allow use of tools for predicting outcome that require complete data. The authors devised a simple and practical tool to employ when verbal component data are missing. They then assessed the tool's utility by application to the GCS-Pupils plus age plus CT findings (GCS-PA CT) prognostic model.

METHODS

The authors inspected data from the International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) cohort to characterize the frequency of missing verbal scores. The authors identified a single verbal score to impute for each eye and motor combined sum (EM) score from distributions of verbal scores in a published database of 54,069 patients. The effectiveness of the imputed verbal score was assessed using a dataset containing information from the IMPACT and Corticosteroid Randomisation After Significant Head Injury (CRASH) databases. The authors compared the performance of the prognostic model using actual verbal scores with the performance using imputed verbal scores and assessed the information yield using Nagelkerke's R2 statistic.

RESULTS

Verbal data were most commonly missing in patients with no eye opening and with a motor score of 4 or less. The "simple" imputation model that was developed performed as well as a more complex model involving distinct combinations of eye and motor scores. The imputation model consisted of the following: EM scores 2-6, add 1; EM score 7, add 2; EM score 8 or 9, add 4; and EM score 10, add 5 to provide the GCS sum score. Modeling without information about the verbal score reduced the R2 from 32.1% to 31.4% and from 34.9% to 34.0% for predictions of death and favorable outcome at 6 months, respectively, compared with using full verbal score information.

CONCLUSIONS

This strategy is particularly valuable for imputation in clinical practice, enabling clinicians to make a rapid and reliable determination of the GCS sum score when the verbal component is not testable. This will support clinical communication and decisions based on estimates of injury severity as well as enable estimation of prognosis. The authors suggest that external validation of their imputation strategy and the performance of the GCS-PA charts should be undertaken in other clinical populations.

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury

OBJECTIVE: Gray matter (GM) and white matter (WM) are vulnerable to ischemic-edematous insults after traumatic brain injury (TBI). The extent of secondary insult after brain injury is quantifiable using quantitative CT analysis. One conventional quantitative CT measure, the gray-white matter ratio (GWR), and a more recently proposed densitometric analysis are used to assess the extent of these insults. However, the prognostic capacity of the GWR in patients with TBI has not yet been validated. This study aims to test the prognostic value of the GWR and evaluate the alternative parameters derived from the densitometric analysis acquired during the acute phase of TBI. In addition, the prognostic ability of the conventional TBI prognostic models (i.e., IMPACT [International Mission for Prognosis and Analysis of Clinical Trials in TBI] and CRASH [Corticosteroid Randomisation After Significant Head Injury] models) were compared to that of the quantitative CT measures. METHODS: Three hundred patients with TBI of varying ages (92 pediatric, 94 adult, and 114 geriatric patients) and admitted between 2008 and 2013 were included in this retrospective cohort study. The normality of the density of the deep GM and whole WM was evaluated as the proportion of CT pixels with Hounsfield unit values of 31-35 for GM and 26-30 for WM on CT images of the entire supratentorial brain. The outcome was evaluated using the Glasgow Outcome Scale (GOS) at discharge (GOS score ≤ 3, n = 100). RESULTS: Lower proportions of normal densities in the deep GM and whole WM indicated worse outcomes. The proportion of normal WM exhibited a significant prognostic capacity (area under the curve [AUC] = 0.844). The association between the outcome and the normality of the WM density was significant in adult (AUC = 0.792), pediatric (AUC = 0.814), and geriatric (AUC = 0.885) patients. In pediatric patients, the normality of the overall density and the density of the GM were indicative of the outcome (AUC = 0.751). The average GWR was not associated with the outcome (AUC = 0.511). IMPACT and CRASH models showed adequate and reliable performance in the pediatric and geriatric groups but not in the adult group. The highest overall predictive performance was achieved by the densitometry-augmented IMPACT model (AUC = 0.881). CONCLUSIONS: Both deep GM and WM are susceptible to ischemic-edematous insults during the early phase of TBI. The extent of the secondary injury was better evaluated by analyzing the normality of the deep GM and WM rather than by calculating the GWR.

Simplifying the use of prognostic information in traumatic brain injury. Part 2: Graphical presentation of probabilities

OBJECTIVE Clinical features such as those included in the Glasgow Coma Scale (GCS) score, pupil reactivity, and patient age, as well as CT findings, have clear established relationships with patient outcomes due to neurotrauma. Nevertheless, predictions made from combining these features in probabilistic models have not found a role in clinical practice. In this study, the authors aimed to develop a method of displaying probabilities graphically that would be simple and easy to use, thus improving the usefulness of prognostic information in neurotrauma. This work builds on a companion paper describing the GCS-Pupils score (GCS-P) as a tool for assessing the clinical severity of neurotrauma. METHODS Information about early GCS score, pupil response, patient age, CT findings, late outcome according to the Glasgow Outcome Scale, and mortality were obtained at the individual adult patient level from the CRASH (Corticosteroid Randomisation After Significant Head Injury; n = 9045) and IMPACT (International Mission for Prognosis and Clinical Trials in TBI; n = 6855) databases. These data were combined into a pooled data set for the main analysis. Logistic regression was first used to model the combined association between the GCS-P and patient age and outcome, following which CT findings were added to the models. The proportion of variability in outcomes "explained" by each model was assessed using Nagelkerke's R². RESULTS The authors observed that patient age and GCS-P have an additive effect on outcome. The probability of mortality 6 months after neurotrauma is greater with increasing age, and for all age groups the probability of death is greater with decreasing GCS-P. Conversely, the probability of favorable recovery becomes lower with increasing age and lessens with decreasing GCS-P. The effect of combining the GCS-P with patient age was substantially more informative than the GCS-P, age, GCS score, or pupil reactivity alone. Two-dimensional charts were produced displaying outcome probabilities, as percentages, for 5-year increments in age between 15 and 85 years, and for GCS-Ps ranging from 1 to 15; it is readily seen that the movement toward combinations at the top right of the charts reflects a decreasing likelihood of mortality and an increasing likelihood of favorable outcome. Analysis of CT findings showed that differences in outcome are very similar between patients with or without a hematoma, absent cisterns, or subarachnoid hemorrhage. Taken in combination, there is a gradation in risk that aligns with increasing numbers of any of these abnormalities. This information provides added value over age and GCS-P alone, supporting a simple extension of the earlier prognostic charts by stratifying the original charts in the following 3 CT groupings: none, only 1, and 2 or more CT abnormalities. CONCLUSIONS The important prognostic features in neurotrauma can be brought together to display graphically their combined effects on risks of death or on prospects for independent recovery. This approach can support decision making and improve communication of risk among health care professionals, patients, and their relatives. These charts will not replace clinical judgment, but they will reduce the risk of influences from biases.

The application of adult traumatic brain injury models in a pediatric cohort

OBJECTIVE There is increasing interest in the use of predictive models of outcome in adult head injury. Two international models have been identified to be reliable modalities for predicting outcome: the Corticosteroid Randomisation After Significant Head Injury (CRASH) model, and the International Mission on Prognosis and Analysis of randomized Controlled Trials in TBI (IMPACT) model. However, these models are designed only to identify outcomes in adult populations. METHODS A retrospective analysis was performed on pediatric patients with severe traumatic brain injury (TBI) admitted to the pediatric intensive care unit (PICU) of Addenbrooke's Hospital between January 2009 and December 2013. The individual risk of 14-day mortality was calculated using the CRASH-Basic and -CT models, and the risk of 6-month mortality calculated using the IMPACT-Core and -Extended (including CT findings) models. Model accuracy was determined by standardized mortality ratio (SMtR; observed/expected deaths), discrimination was evaluated as the area under the receiver operating curve (AUROC), and calibration assessed using the Hosmer-Lemeshow χ² test. RESULTS Ninety-four patients with an average age of 7.3 years were admitted to the PICU with a TBI. The mortality rate was 12.7% at 14 days and at 6 months. For the CRASH-Basic model, the SMtR was 1.42 and both calibration (χ² = 6.1, p = 0.64) and discrimination (AUROC = 0.92) were good. For the IMPACT-Core model, the SMtR was 1.03 and the model was also well calibrated (χ² = 8.99, p = 0.34) and had good discrimination (AUROC = 0.85). Poor outcome was observed in 17% of the cohort and identified with the CRASH-Basic and IMPACT-Core models to varying degrees: standardized morbidity ratio = 0.89 vs 0.67, respectively; calibration = 6.5 (χ²) and 0.59 (p value) versus 8.52 (χ²) and 0.38 (p value), respectively; and discrimination (AUROC) = 0.92 versus 0.83, respectively. CONCLUSIONS Adult head injury models may be applied with sufficient accuracy to identify predictors of morbidity and mortality in pediatric TBI.