Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis. This study targets the development of a set of integrated prognostication model combining different classes of outcome and prognostic factors. Methodologies such as discriminant analysis, logistic regression, decision tree, Bayesian network, and neural network were employed in the study. Several prognostication models were developed using prospectively collected data from 513 severe closed head-injured patients admitted to the Neurocritical Unit at National Neuroscience Institute of Singapore, from April 1999 to February 2003. The correlation between prognostic factors at admission and outcome at 6 months following injury was studied. Overfitting error, which may falsely distinguish different outcomes, was compared graphically. Tenfold cross-validation technique, which reduces overfitting error, was used to validate outcome prediction accuracy. The overall prediction accuracy achieved ranged from 49.79% to 81.49%. Consistently high outcome prediction accuracy was seen with logistic regression and decision tree. Combining both logistic regression and decision tree models, a hybrid prediction model was then developed. This hybrid model would more accurately predict the 6-month post-severe head injury outcome using baseline admission parameters.

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Traumatic brain injury is a major socioeconomic burden, 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), and pressure reactivity index (PRx), as well as multimodal monitoring parameters to assess brain tissue oxygenation (PbtO(2)), 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 was assessed using a 10-fold cross-validation technique. In addition, the confusion matrix was also used to assess the sensitivity, specificity, and the F-ratio. In all, 2,413 time series records were extracted from 26 patients treated at our neurocritical care unit over a 1-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 PbtO(2), 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 multivariate setting of advanced multimodality monitoring, and validates the use of multimodal monitoring in the neurocritical care setting to have a potential benefit in predicting outcomes of patients with severe head injury.

The monitoring data of MAP and BTemp are more reliable for reuse than ICP and PbtO (2); and, for ICP and PbtO (2) data, a more cautious re-usage strategy should be employed. We also observe that, for the scenarios tested, the lazy learning method, K-STAR, and the tree-based method, M5P, are consistently 2 of the best among the 17 predictive methods investigated in this study.

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