Literature DB >> 22931390

Comparing model performance for survival prediction using total Glasgow Coma Scale and its components in traumatic brain injury.

Mehdi Moazzez Lesko1, Tom Jenks, Sara J O'Brien, Charmaine Childs, Omar Bouamra, Maralyn Woodford, Fiona Lecky.   

Abstract

The Glasgow Coma Scale (GCS) score is used in clinical practice for patient assessment and communication among clinicians and also in outcome prediction models such as the Trauma and Injury Severity Score (TRIS). The objective of this study is to determine which GCS subscore is best associated with outcome, taking time of assessment into account. Records of patients with brain injury who presented after 1989 were extracted from the Trauma Audit and Research Network (TARN) database. Using logistic regression, a baseline model was derived with age, Injury Severity Score (ISS), and year of injury as covariates and survival at discharge as the dependent variable. Total GCS, its subscores, and their combinations at various time points were separately added to the baseline model to compare their effect on model performance. The dataset contained 21,657 cases. The total GCS score at scene and its subscores had significantly lower predictive power compared with those recorded on arrival at the Emergency Department (ED) (scene total GCS: Area Under the Curve-AUC: 0.89; 95% confidence interval [CI]: 0.89-0.90) and Nagelkerke R(2) of 0.55, admission total GCS: AUC of 0.91; 95% CI: 0.91-0.91, and Nagelkerke R(2) of 0.59). Eye and verbal subscores had significantly lower performances compared with total GCS, motor subscore, and various combinations of subscores. Motor subscore and total GCS appeared to have similar predictive performance (admission total and motor GCS both had AUC of 0.91 (95% CI: 0.91-0.92) and Nagelkerke R(2) of 0.59 and 0.58, respectively). Motor subscore contains most of the predictive power of the total score. GCS on arrival is a significantly better predictor of outcome than that recorded at scene.

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Year:  2012        PMID: 22931390     DOI: 10.1089/neu.2012.2438

Source DB:  PubMed          Journal:  J Neurotrauma        ISSN: 0897-7151            Impact factor:   5.269


  11 in total

1.  Predicting long-term outcome after traumatic brain injury using repeated measurements of Glasgow Coma Scale and data mining methods.

Authors:  Hsueh-Yi Lu; Tzu-Chi Li; Yong-Kwang Tu; Jui-Chang Tsai; Hong-Shiee Lai; Lu-Ting Kuo
Journal:  J Med Syst       Date:  2015-01-31       Impact factor: 4.460

2.  The Role of Substance P in Pulmonary Clearance of Bacteria in Comparative Injury Models.

Authors:  Terry Hsieh; Max H Vaickus; Thor D Stein; Bethany L Lussier; Jiyoun Kim; David M Stepien; Elizabeth R Duffy; Evan L Chiswick; Daniel G Remick
Journal:  Am J Pathol       Date:  2016-12       Impact factor: 4.307

3.  Sleep disturbances and internalizing behavior problems following pediatric traumatic injury.

Authors:  Jesse T Fischer; H Julia Hannay; Candice A Alfano; Paul R Swank; Linda Ewing-Cobbs
Journal:  Neuropsychology       Date:  2018-02       Impact factor: 3.295

4.  Components of traumatic brain injury severity indices.

Authors:  John D Corrigan; Scott Kreider; Jeffrey Cuthbert; John Whyte; Kristen Dams-O'Connor; Mark Faul; Cynthia Harrison-Felix; Gale Whiteneck; Christopher R Pretz
Journal:  J Neurotrauma       Date:  2014-04-21       Impact factor: 5.269

5.  Time-dependent prediction and evaluation of variable importance using superlearning in high-dimensional clinical data.

Authors:  Alan Hubbard; Ivan Diaz Munoz; Anna Decker; John B Holcomb; Martin A Schreiber; Eileen M Bulger; Karen J Brasel; Erin E Fox; Deborah J del Junco; Charles E Wade; Mohammad H Rahbar; Bryan A Cotton; Herb A Phelan; John G Myers; Louis H Alarcon; Peter Muskat; Mitchell J Cohen
Journal:  J Trauma Acute Care Surg       Date:  2013-07       Impact factor: 3.313

6.  Variable importance and prediction methods for longitudinal problems with missing variables.

Authors:  Iván Díaz; Alan Hubbard; Anna Decker; Mitchell Cohen
Journal:  PLoS One       Date:  2015-03-27       Impact factor: 3.240

7.  Outcome Prediction after Traumatic Brain Injury: Comparison of the Performance of Routinely Used Severity Scores and Multivariable Prognostic Models.

Authors:  Marek Majdan; Alexandra Brazinova; Martin Rusnak; Johannes Leitgeb
Journal:  J Neurosci Rural Pract       Date:  2017 Jan-Mar

8.  Comparison of four variable selection methods to determine the important variables in predicting the prognosis of traumatic brain injury patients by support vector machine.

Authors:  Saeedeh Pourahmad; Soheila Rasouli-Emadi; Fatemeh Moayyedi; Hosseinali Khalili
Journal:  J Res Med Sci       Date:  2019-11-27       Impact factor: 1.852

9.  Cardiac arrest after severe traumatic brain injury can be survivable with good outcomes.

Authors:  Zirun Zhao; Justine J Liang; Zhe Wang; Nathan J Winans; Matthew Morris; Stephen Doyle; Adam Fry; Susan M Fiore; Sima Mofakham; Charles B Mikell
Journal:  Trauma Surg Acute Care Open       Date:  2021-02-11

10.  Traumatic brain injury probability of survival assessment in adults using iterative random comparison classification.

Authors:  Mohammed Salah; Reza Saatchi; Fiona Lecky; Derek Burke
Journal:  Healthc Technol Lett       Date:  2020-11-18
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