MULTIFACTOR REGRESSION MODEL FOR PREDICTING THE RISK OF ADVERSE COURSE OF RHEUMATOID ARTHRITIS ASSOCIATED WITH BORRELIA BURGDORFERI
Smiian S.I., Yuskevych V.V., Sverstiuk A.S., Slaba U.S., Makhovska O.S.
Summary. Rheumatoid arthritis (RA) associated with Borrelia burgdorferi (B. burgdorferi) is an autoimmune-infectious disease based on an excessive pro-inflammatory immune response. Risk predictors stratification of arthritis unfavourable course is a perspective of modern scientific research with the aim of preventing disability, long-term incapacity for work, and improving the quality of life in patients. Purpose. To stratify the risk coefficient of adverse course of rheumatoid arthritis associated with B. burgdorferi (RCACRA(Bb)) according to the created mathematical prediction model. Materials and methods. We examined 126 patients: 39 with RA associated with B. burgdorferi, 44 with LA, and 43 with isolated RA to build a prognostic model of RCACRA(Bb) using multivariate regression analysis. ANOVA analysis was used to assess the acceptability of the model, and the Nigelkirk’s test (R2) was used to check the quality of the predictive model. Results. We created a mathematical model for predicting RCACRA(Bb) using the data of multivariate regression analysis, taking into account the most significant factors (p<0.05): age, sex , response to treatment, Baker’s cyst (BC), dysbiotic changes of the intestine (DСI), duration of arthritis symptoms before diagnosis (ASD before the diagnosis), number of swollen joints (SJC), tender joints count (TJC) , C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), visual analog pain scale (VAS), disease activity score (DAS 28), functional status — Health Assessment Questionnaire (HAQ-DI), psychological component of health using the Short Form Medical Outcomes Study (PCH-SF36), tumor necrosis factor-alpha (TNF-alpha), interleukin-10 (IL-10), rheumatoid factor (RF). In order to classify RCACRA(Bb), patients with arthritis the following criteria are proposed: RCACRA(Bb) ≤18 — low risk, if 18 < RCACRA(Bb) ≤ 34 – medium risk, if 34 < RCACRA (Bb) ≤ — high risk. The calculated coefficient of determination of the model for determining RCACRA (Bb) (R2=0.992) indicates its high quality. Conclusions. The developed algorithm and mathematical model for predicting RCACRA (Bb), proposed for the first time, are highly informative and qualitative and make it possible to timely diagnose RA associated with B. burgdorferi, prescribe adequate therapy, achieve remission, and prevent high disease activity. In the future, the obtained results can be used as a design of an information-diagnostic system for evaluating and forecasting ACRARA(Bb), developing as a result of impact of a number of factors, which will provide an opportunity to carry out timely medical measures in advance in order to prevent the development of this pathology.
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