Early prediction of outcome after severe traumatic brain injury: a simple and practical model.

Sandro Rizoli,Martin Schreiber,Laurie Morrison,Avery Nathens,Eileen Bulger,Ashley Petersen,Raul Coimbra,Airton Leonardo De Oliveira Manoel,Joseph Minei,Jeffrey D Kerby

doi:10.1186/s12873-016-0098-x

Abstract

BackgroundTraumatic brain injury (TBI) is a heterogeneous syndrome with a broad range of outcome. We developed a simple model for long-term outcome prognostication after severe TBI.MethodsSecondary data analysis of a large multicenter randomized trial. Patients were grouped according to 6-month extended Glasgow outcome scale (eGOS): poor-outcome (eGOS ≤ 4; severe disability or death) and acceptable outcome (eGOS > 4; no or moderate disability). A prediction decision tree was built using binary recursive partitioning to predict poor or acceptable 6-month outcome. Comparison to two previously published and validated models was made.ResultsThe decision tree included the predictors of head Abbreviated Injury Scale (AIS) severity, the Marshall computed tomography score, and pupillary reactivity. All patients with a head AIS severity of 5 were predicted to have a poor outcome. In patients with head AIS severity < 5, the model predicted an acceptable outcome for (1) those with Marshall score of 1, and (2) those with Marshall score above 1 but with reactive pupils at admission. The decision tree had a sensitivity of 72.3 % (95 % CI: 66.4–77.6 %) and specificity of 62.5 % (95 % CI: 54.9–69.6 %). The proportion correctly classified for the comparison models was similar to our model. Our model was more apt at correctly classifying those with poor outcome but more likely to misclassify those with acceptable outcome than the comparison models.ConclusionPredicting long-term outcome early after TBI remains challenging and inexact. This model could be useful for research and quality improvement studies to provide an early assessment of injury severity, but is not sufficiently accurate to guide decision-making in the clinical setting.

Highlights

Traumatic brain injury (TBI) is a heterogeneous syndrome with a broad range of outcome
Descriptive statistics Of the 1282 patients enrolled in the TBI cohort of the trial, 1089 patients were included in our analysis with 193 (15 %) of those enrolled being excluded due to death within 24 h of emergency department (ED) admission, unknown 24-h survival status, no blunt injury, and no head computed tomography (CT) (Fig. 1)
The most striking differences between the two groups were related to higher head Abbreviated Injury Scale (AIS) and Marshall scores and absence of pupillary reactivity among those with a poor neurological outcome

Summary

Introduction

Traumatic brain injury (TBI) is a heterogeneous syndrome with a broad range of outcome. We developed a simple model for long-term outcome prognostication after severe TBI. Traumatic brain injury (TBI) remains the leading cause of death and significant disability after severe blunt trauma [1]. In severe TBI, more than in any other injury, the preoccupation with long-term functional neurological outcome permeates many of the early decisions and interventions offered to these patients. Traumatic brain injury consists of a heterogeneous group of patients and injuries, in whom individual neurological recovery is difficult to predict at any time, but early after admission. Accurate and useful prediction models to estimate neurological recovery is few and most only applicable when used days after the injury [6, 7]. It could reduce unwarranted decisions to withdraw life-supporting measures due to the perception of unfavorable neurological recovery [8, 9], help stratify patients into protocols and clinical trials, and assist with quality evaluation and improvement programs among many other utilities

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