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. 2008 Aug 5;5(8):e165; discussion e165.
doi: 10.1371/journal.pmed.0050165.

Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics

Ewout W Steyerberg  1 , Nino Mushkudiani, Pablo Perel, Isabella Butcher, Juan Lu, Gillian S McHugh, Gordon D Murray, Anthony Marmarou, Ian Roberts, J Dik F Habbema, Andrew I R Maas
Affiliations

Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics

Ewout W Steyerberg et al. PLoS Med. .

Abstract

Background: Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors.

Methods and findings: Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial.

Conclusions: Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Score Chart for 6 Month Outcome after TBI
Sum scores can be calculated for the core model (age, motor score, pupillary reactivity), the extended model (core + hypoxia + hypotension + CT characteristics), and a lab model (core + hypoxia + hypotension + CT + glucose + Hb). The probability of 6 mo outcome is defined as 1 / (1 + e−LP), where LP refers to the linear predictor in a logistic regression model. Six LPs were defined as follows: LPcore, mortality = −2.55 + 0.275 × sum score core LPcore, unfavorable outcome = −1.62 + 0.299 × sum score core LPextended, mortality = −2.98 + 0.256 × (sum score core + subscore CT) LPextended, unfavorable outcome = −2.10 + 0.276 × (sum score core + subscore CT) LPlab, mortality = −3.42 + 0.216 × (sum score core + subscore CT + subscore lab) LPlab, unfavorable outcome = −2.82 + 0.257 × (sum score core + subscore CT + subscore lab) The logistic functions are plotted with 95% confidence intervals in Figure 2.
Figure 2
Figure 2. Predicted Probabilities of Mortality and Unfavorable Outcome at 6 Month after TBI in Relation to the Sum Scores from the Core, Extended, and Lab Models
The logistic functions are plotted with 95% confidence intervals. Dot size is proportional to sample size. Sum scores can be obtained from Figure 1.
Figure 3
Figure 3. Screenshot of the Spreadsheet with Calculations of Probabilities for the Three Prediction Models
Predictions are calculated for a 35-y-old patient with motor score 3, both pupils reacting, hypoxia before admission, mass lesion and tSAH on admission CT scan, glucose 11 mmol/l, and Hb 10 g/dl. A Web-based calculator is available at http://www.tbi-impact.org/.
Figure 4
Figure 4. External Validity for the Core and Core + CT Model Characteristics for Prediction of Mortality in the CRASH Trial
The distribution of predicted probabilities is shown at the bottom of the graphs, by 6-mo mortality. The triangles indicate the observed frequencies by decile of predicted probability.
Figure 5
Figure 5. External Validity for the Core and Core + CT Model Characteristics for Prediction of Unfavorable Outcomes in the CRASH Trial
The distribution of predicted probabilities is shown at the bottom of the graphs, by 6-mo outcome. The triangles indicate the observed frequencies by decile of predicted probability.
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