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

被引：23
作者：
Rizoli S. [1 ]
Petersen A. [2 ]
Bulger E. [3 ]
Coimbra R. [4 ]
Kerby J.D. [5 ]
Minei J. [6 ]
Morrison L. [7 ]
Nathens A. [8 ]
Schreiber M. [9 ]
de Oliveira Manoel A.L. [10 ]
机构：
[1] Department of Trauma, St Michael's Hospital, Toronto, ON
[2] University of Washington, Department of Biostatistics, Seattle, WA
[3] University of Washington, Department of Surgery,, Harborview Medical Center, Seattle, WA
[4] University of California San Diego, Department of Surgery, San Diego, CA
[5] University of Alabama at Birmingham, Department of Surgery, Birmingham, AL
[6] University of Texas Southwestern Medical Center, Department of Surgery, Dallas, TX
[7] University of Toronto, Division of Emergency Medicine, Toronto, ON
[8] Sunnybrook Health Sciences Centre, Division of General Surgery, Toronto, ON
[9] Oregon Health and Science University, Department of Emergency Medicine, Portland, OR
[10] Keenan Research Center of the Li Ka Shing Knowledge Institute of St. Michael's Hospital, Neuroscience Research Program, 30 Bond Street, Toronto, M5B 1W8, ON
来源：
BMC Emergency Medicine | / 16卷 / 1期
关键词：
Outcome measures; Prognostic models; Recovery; Traumatic brain injury;
D O I：
10.1186/s12873-016-0098-x
中图分类号：
学科分类号：
摘要：
Background: 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. Methods: Secondary 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. Results: The 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. Conclusion: Predicting 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. © 2016 The Author(s).
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