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№2' 2017


International Medical Journal, Vol. 23., Iss. 2, 2017, P. 86−88.


Shkolnyk V. M., Fesenko H. D.

Dnipropetrovsk Medical Academy of Ministry of Health of Ukraine, Dnipro, Ukraine

To describe a mathematical model for predicting the risk of disability prolongation in a long−term period of traumatic brain injury, the data of two groups of patients with consequences of traumatic brain injury of varying severity and duration of posttraumatic period for more than 2 years were analyzed. According to the results of correlation analysis, 15 indicators that were associated with prolongation of disability in the long−term period of traumatic brain injury were selected. The indicators included patient`s age at the time of injury, current unemployment, absence of family, active pre−morbid somatic diseases, presence of subdural, epidural, intracranial hematoma, basal skull fracture or postponing hematoma evacuation or decompression craniotomy in acute period of trauma, severity of vestibular dysfunction, the results of the MoCA−test, rating from 0 to 5 in categories of energy and drive functions, control of voluntary movement functions, sensation of pain, conversation and walking functions assessed by the Brief Core Set of the international classification of functioning for TBI, as well as detection of pathological EEG types (II−V types of EEG according to Zhyrmunska) and disturbances of cerebral vessels reactivity resulting from visual load test. Predictive coefficients (PCs) were calculated for each indicator by means of Wald analysis. The sum of coefficients determines the risk of disability prolongation. According to logit−regression analysis, the maximum values of the total score were determined, which allowed to form the following groups of risk of disability prolongation: very low risk, low risk, moderate risk, high risk, and very high risk. Based on the data from clinical examination, past history and additional instrumental findings, using the methods of logit−regression analysis, a scale to assess 15 indicators, which allows a healthcare specialist to predict the prolongation of disability in patients in a long−term period of TBI with 91.0 % accuracy was created. The model provides the opportunity to identify the groups of patients in a long−term period of traumatic brain injury with high and very high risk of disability prolongation for optimization of treatment strategy.

Key words: traumatic brain injury, long−term period, disability, prediction.

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