PERSONALIZED STRATEGIES FOR RELAPSE PREVENTION IN PATIENTS WITH ENDOGENOUS MENTAL DISORDERS

Mamatxonov Maksudbek Ma’rifjon ugli

Authors

Mamatxonov Maksudbek Ma’rifjon ugli 3rd-year PhD Candidate at Andijan State Medical Institute (ASMI)

Keywords:

endogenous mental disorders, remission, relapse, PANSS, SOFAS, Agranovsky typology, personalized prevention.

Abstract

Objective. To substantiate the need for implementing personalized strategies for relapse prevention in patients with endogenous mental disorders based on clinical-psychopathological and psychometric characteristics.

Materials and Methods. The study included 197 patients diagnosed with endogenous mental disorders (ICD-10: F20–F33) who had been in remission for at least 6 months. Assessment was performed using the PANSS and SOFAS scales, as well as remission typology according to M. L. Agranovsky’s classification. Multifactorial risk analysis was conducted using logistic regression, ROC analysis, and odds ratio (OR, 95% CI) estimation.

Results. The key independent predictors of stable remission were identified as: high level of social functioning (SOFAS ≥ 45), pronounced reduction of symptom severity (ΔPANSS ≥ 15), favorable remission types, and high treatment adherence. The developed prognostic model demonstrated high accuracy (ROC-AUC = 0.83), good calibration (p > 0.05), and high sensitivity (91.8%) and specificity (94.7%). Based on these findings, directions for personalized relapse-prevention strategies were formulated, including optimization of pharmacotherapy (with emphasis on long-acting injectable formulations), psychosocial interventions, functional rehabilitation, individualized “crisis plans,” and digital monitoring.

Conclusion. A personalized approach to relapse prevention makes it possible to take into account the patient’s clinical and psychometric profile and significantly improves the effectiveness of outpatient follow-up. The developed model can be used in clinical practice for risk stratification and the development of individualized support programs.

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