PREDICTION OF COMORBIDITY DEVELOPMENT IN PATIENTS WITH GOUT
Summary. Gout is a hyperuricemic metabolic condition that has a causal paradigm with increasing burden of comorbidities. This indicates that a promising task is determination and modification of risk factors for comorbidity development in gout. Purpose. To create an algorithm and a mathematical prognostic model of a risk coefficient for comorbidity development (RCC) in gouty subjects. Materials and methods. We examined 136 patients with gout and the different gout-specifically modified Rheumatic Diseases Comorbidity Index (mRDCI): 20 — with mRDCI 0, 28 — with mRDCI 1–2, 62 — with mRDCI 3–4, 26 — with mRDCI ≥5, as well as 31 healthy volunteers to propose an approach to predicting the risk of comorbidity development (RCD) in gout according to the mathematical model obtained by multivariate regression analysis. The coefficient of determination R2 was calculated to determine the quality of the prognostic model. ANOVA analysis was performed to evaluate the acceptability of the model. Results. Multivariate regression analysis revealed the most significant comorbidity development risk factors in gouty subjects (p<0.05): delayed diagnosis of gout, gout duration, Gout Activity Score, monitoring of serum uric acid within the preceding year, Health Assessment Questionnaire Disability Index, Mental Component Summary Score using the 36-Item Short Form Health Survey Questionnaire, body mass index, estimated glomerular filtration rate, serum levels of low-density lipoprotein cholesterol, C-reactive protein, interleukin (IL)-6, IL-8, and adiponectin/leptin ratio. Prognostic model was created to determine the RCC using regression coefficients B of the above mentioned predictors. In order to stratify RCD in gouty subjects, the following classification of RCC was proposed: no risk at RCC ≤3; low risk at 3<RCC≤9; medium risk at 9<RCC<18; high risk at 18≤RCC<30; critical risk at RCC ≥30. The significant level of acceptability of the prognostic model RCC is confirmed by the results of the ANOVA analysis (p<0.001). The calculated R2 of the model for determining RCC (R2=0.98) demonstrates its high quality. Conclusions. The proposed original algorithm and mathematical model for predicting the development of comorbidity in gouty subjects have high acceptability and quality. The prognostic model allows timely determining and monitoring of patients with a critical risk of comorbidity development, and contribute to the creation of adapted preventive programs of comorbid pathology in gout.
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