Prediction of post-psychotic social functioning in children based on determination of immunological parameters

Abstract

Introduction. Prediction of disorders of social functioning (SF) following childhood-manifest endogenous psychosis is an important problem of psychiatry driven by the need to choose the optimal way of further management of the patient and his/her integration into society. Due to the involvement of immune mechanisms in the development of psychosis, the immunological markers reflecting its severity may be related with the level of post-psychotic SF.

Aim. Evaluating a possible prognosis of the level of children's SF in the post-psychotic period of the disease based on the determination of immunological indexes at the psychotic stage.

Material and methods. The study involved 54 patients aged 3 to 16 years (mean age, 8.7±3.7 years) with endogenous psychotic symptoms (F84.02, F84.1x, F20.8xx3 according to ICD-10). Children’s level of SF was determined using the PSP scale adapted for pediatric use, not earlier than 3 months after psychosis had resolved. Activities of leukocyte elastase (LE) and α1-proteinase inhibitor (α1-PI), and the blood levels of antibodies to S100B and myelin basic protein (MBP) were measured. Based on these measurements, we performed a comprehensive assessment of the immune system activation taking into account the interrelation of inflammatory and autoimmune reactions. 20 healthy children age- and sex-matched with the study group were used as a control.

Results. An association was found between the immune system activation during psychosis and the level of post-psychotic SF. An ordinal logistic regression model was constructed that linked the α1-PI activity and the level of MBP antibodies with the prognosis of SF. This mathematical model had a good prognostic significance (81%).

Conclusion. A regression model based on immunological indexes, that determines the probability of various levels of SF in the post-psychotic period can be used in clinical practice to optimize therapy and to choose the optimal follow-up schedule.