Gerasimova Daria Alexandrovna

Graduated from I.M. Sechenov First Moscow State Medical University (Sechenov University) in 2014, specialty "Pharmacy", qualification "Provizor". In 2015, I was an intern at Sechenov University, specializing in Management and Economics of Pharmacy. I worked at the Department of Organization and Economy of Pharmacy at Sechenov University in 2014-2017. In 2017 - 2020 I worked in the Ministry of Health of the Russian Federation in the Department of Clinical Research. Since 2020 I have been teaching at the Department of Organization and Economy of Pharmacy at Sechenov University. In collaboration with Nasonova Research Institute of Rheumatology has been working on the optimization of pharmaceutical care for patients with systemic autoimmune rheumatic diseases.
Gerasimova D.A., Gerasimova E.., Evsikova M.., Zakharova O.V., Lobuteva L.A., Popovich I.G., Puzikova A.I. 804

To determine the feasibility of using cluster analysis to manage treatment costs of systemic autoimmune rheumatic diseases (SARDs). MATERIALS AND METHODS. The object of the study was the case histories of patients hospitalized in 2020. A total of 954 case histories of patients with SARDs were analyzed, among them systemic scleroderma – 411 patients (43.1%), systemic lupus erythematosus – 263 (27.5%), rheumatoid arthritis seropositive and seronegative – 103 (10.8%), systemic vasculitis associated with anti-neutrophil cytoplasmic antibodies – 98 (10.3%), idiopathic inflam- matory myopathies (polymyositis, dermatomyositis) – 57 (6%), Sjögren’s disease – 22 (2.3%). Hierarchical cluster analysis by weighted pairwise mean using Euclidean distance and Chebyshev distance, and non-hierarchical clus- tering by k-means were performed for the sample of case histories in the STA- TISTICA 13.3. Multiple regression model with a dependent factor of treatment costs was built in MS Excel. RESULTS. Hierarchical clustering resulted in 2 dendrograms, which yielded the same number of clusters – 4. The cluster analysis identified 4 clusters with significant differences (p<0.05) in treatment costs with formative indicators: gender, age of patients, duration of disease, number of bed-days, number of hospitalizations, disease activity level, number of comorbidities and compli- cations, previous use of biologic agents, total number of medications pre- scribed, use of Acellbia, Benlista, other biologic agents, and immunoglobulins during hospitalization. General multiple regression model for all patients with SARDs and separate multiple regression models for each cluster were con- structed using statistically significant factors. CONCLUSIONS. Factors affecting the treatment costs for patients with SARDs include the number of days of hospitalization, the degree of disease activity, the number of medications prescribed, and the use of biologic agents and immunoglobulins during hospitalization.

Gerasimova D.A., Gerasimova E.., Evsikova M.., Zakharova O.V., Lobuteva L.A., Popovich I.G., Puzikova A.I. 804

To determine the feasibility of using cluster analysis to manage treatment costs of systemic autoimmune rheumatic diseases (SARDs). MATERIALS AND METHODS. The object of the study was the case histories of patients hospitalized in 2020. A total of 954 case histories of patients with SARDs were analyzed, among them systemic scleroderma – 411 patients (43.1%), systemic lupus erythematosus – 263 (27.5%), rheumatoid arthritis seropositive and seronegative – 103 (10.8%), systemic vasculitis associated with anti-neutrophil cytoplasmic antibodies – 98 (10.3%), idiopathic inflam- matory myopathies (polymyositis, dermatomyositis) – 57 (6%), Sjögren’s disease – 22 (2.3%). Hierarchical cluster analysis by weighted pairwise mean using Euclidean distance and Chebyshev distance, and non-hierarchical clus- tering by k-means were performed for the sample of case histories in the STA- TISTICA 13.3. Multiple regression model with a dependent factor of treatment costs was built in MS Excel. RESULTS. Hierarchical clustering resulted in 2 dendrograms, which yielded the same number of clusters – 4. The cluster analysis identified 4 clusters with significant differences (p<0.05) in treatment costs with formative indicators: gender, age of patients, duration of disease, number of bed-days, number of hospitalizations, disease activity level, number of comorbidities and compli- cations, previous use of biologic agents, total number of medications pre- scribed, use of Acellbia, Benlista, other biologic agents, and immunoglobulins during hospitalization. General multiple regression model for all patients with SARDs and separate multiple regression models for each cluster were con- structed using statistically significant factors. CONCLUSIONS. Factors affecting the treatment costs for patients with SARDs include the number of days of hospitalization, the degree of disease activity, the number of medications prescribed, and the use of biologic agents and immunoglobulins during hospitalization.