Prediction of Outcome Utilizing Both Physiological and Biochemical Parameters in Severe Head Injury

Abstract 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 (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. T...