PROGNOSIS OF SYMPTOMATIC EPILEPSY DEVELOPMENT IN PATIENTS WITH BRAIN TUMORS THROUGH ANALYSIS OF NEUROPHYSIOLOGICAL PARAMETERS AND BINARY LOGISTIC REGRESSION

Aim: this study was aimed at identifying prognostic potential of electroencephalo- graphic and cardiointervalometric neurophysiological parameters using logistic regression modeling in patients with brain tumors manifesting with symptomatic epilepsy. Methods: the primary group of participants in the study consisted of 88 patients, aged 22 to 83 years admitted at Ryazan State regional hospital neurosurgical department with brain tumor as the admitting diagnosis. The control group consisted of 20 relatively healthy individuals of equal gender distribution. The primary group was further subdivided into groups of patients with brain tumor associated epilepsy and brain tumors with no epileptic seizures. Five minute electrocardiogram as well as electroencephalograms were recorded in 3 functional probes (baseline, hyperventilation and post-hyperventilation) on admission followed by statistical correlational analysis and logistic regression. Results: based on significantly strong correlations the selected electroencephalogram predictor factors included Average power of the delta wave diapazon in F3-A1 and O2-A2 during hyperventilation probe as well as Mode (Mo) and very low frequency component of total power (%VLF) cardiointer- val parameters during post-hyperventilation probe. Selected predictors used in the logistic regression model were able to predict possible prognosis in patients with brain tumor induced epilepsy with 73% sensitivity and 96% specificity. Conclusion: logistic regression analysis of pre-defined neurophysiological predictor factors is perspective in neurooncological patients including patients with brain tumor induced epilepsy in terms of its clinical prognostic value and structuring of complex and effective treatment schemes.