Psychological aspects in the rehabilitation of patients with chronic back pain

Abstract

Background. The relevance of the work is associated with the high prevalence and socio-economic significance of chronic pain. The number of analgesics consumed in the world is estimated in the tens of tonns. Hardware effects, including neurosurgical interventions, are not always effective and are associated with many side effects of complications. Existing therapeutic and surgical approaches to the treatment of chronic pain require additions. In this regard, the information and structural theory of pain was developed, revealing the information processes occurring in the Central nervous system against the background of chronic pain, as well as the theory of psychological types and information metabolism. In this case, it is easier not to look for the right solution, but to create a mechanism that will come up with a method for finding the right solution. A neural network is one of the ways to implement artificial intelligence (AI). It studies methods for building algorithms that can learn independently. This is necessary if there is no clear solution to any problem.

Aims: based on the comparison of data from neuropsychological, clinical and neurophysiological studies, as well as mathematical (neural network) modeling of chronic pain, to identify information and structural justification and practical application of psychoalgology.

Methods. A total of A total of 105 patients diagnosed with Dorsopathy (M54.4; M51.1, M54.1) were studied. 50 men and 55 women, men: average age 49 ± 0.5 years; women: average age 52 ± 1.6 years. Assessment of the level of reactive and personal anxiety using the adapted Spielberger–Hanin questionnaire, the SAN test, and the assessment of vegetative status using the “vegetative questionnaire” by A.M. Wein. Neuroimaging research: CT, MRI of the brain and spine for diagnostic purposes. The neurophysiological study consisted of EEG, TCD, duplex scanning of the craniocervical junction vessels. For a more detailed assessment, a neural network analyzer of lumbar pain was used, which allows predicting its course.

Results. A clinical and neurophysiological study of patients with back pain revealed that, along with other disorders of cerebral neurodynamics, a large role is played by lateralization of cerebral neurodynamics (asymmetry), which is manifested by more pronounced changes in the EEG in the contralateral hemisphere. When studying the subjective state of patients, two main types of disorders were distinguished: the type of associated and the type of non-associated mental disorders.

As a result of the analysis of mathematical (neural network) algorithms of pain syndromes, clinical and neurophysiological studies, new principles of chronization of the pathological process with the transformation of the pain syndrome into an independent psychological disease were formulated. ALGIC DISEASE is characterized by a pronounced clinical polymorphism due to complex information-structural interactions of dominant and subdominant zones and characterized by: 1) heterogeneity and chaotic spatial parameters of pain in relation to the zones of innervation of nociogenic structures; 2) non-topological time parameters of peripheral and Central sensitization with increased pain − from instant to prolonged; 3) mutual suppression, displacement, migration of pain centers; 4) changing the monocausal dependence of the polycausal pain syndrome with the possibility of a reverse process in the process of regional integrative measures with a multidisciplinary approach; 5) the relationship of chronic pain with pronounced cognitive, emotional and vegetative reactions.

Conclusions. Based on the information and structural theory of pain, the results of research and the proposed psychoalgological approach, the principles of building a rehabilitation program for patients with chronic pain are formulated, which consists in a complex effect on nociogenic structures of types 1, 2 and 3 in combination with the modification of patient behavior through individually selected psychotherapeutic techniques.

Keywords

pain syndrome / mathematical (neural network) modeling / algic disease / neural network analyzer of lumbar pain / psychotherapeutic correction

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