Citation

Low D. J. Neurotrauma 2009; 26(8): 1177-1182.

Affiliation

National Neuroscience Institute , Neurosurgery, 11 Jalan Tan Tock Seng, Singapore, Singapore, 308433, +65 63577191, +65 63577136; neuro_surg@hotmail.com.

Copyright

(Copyright © 2009, Mary Ann Liebert Publishers)

DOI

10.1089/neu.2008-0841

PMID

19371145

Abstract

Traumatic brain injury is a major socioeconomic burden to society and the use of statistical models to predict outcomes after head injury can help to allocate limited health resources. Earlier prediction models analyzing admission data have been used to achieve prediction accuracies of up to 80%. Our aim was to design statistical models utilizing a combination of both physiological and biochemical variables obtained from multimodal monitoring in the neurocritical care setting as a complement to earlier models. We used decision tree and logistic regression analysis on variables including intracranial pressure (ICP), mean arterial pressure (MAP), cerebral perfusion pressure (CPP), pressure reactivity index (PRx) as well as multimodal monitoring parameters comprising brain tissue oxygenation (PbtO2) and microdialysis parameters to predict outcomes based on a dichotomized Glasgow outcome score. Further analysis was carried out on various subgroup combinations of physiological and biochemical parameters. The reliability of the head injury models were carried out using a 10-fold cross-validation technique. In addition, the confusion matrix was also used to assess the sensitivity, specificity and the F-Ratio. 2413 time series records were extracted from 26 patients treated at our neurocritical care unit over a one year period. Decision tree analysis was found to be superior to logistic regression analysis in predictive accuracy of outcome. The combined use of microdialysis variables and PbtO2 in addition to ICP, MAP and CPP was found have the best predictive accuracy. The use of physiological and biochemical variables based on a decision tree analysis model has shown to provide an improvement in predictive accuracy compared with other previous models. The potential application is for outcome prediction in the multi-variate setting of advanced multi-modality monitoring and validates the use of multimodal monitoring in the neurocritical setting to have a potential benefit in prognostication of patients with severe head injury.

Language: en