Predicting the efficacy of rehabilitation in patients with type 2 diabetes and diabetic polyneuropathy
Authors: Bakaliuk T.H. , Makarchuk N.R. , Stelmakh H.O. , Pankiv V.I. , Kamyshna I.I. 
Published: June 2024

Background. Predicting the effectiveness of rehabilitation in patients with diabetic polyneuropathy (DPN) and type 2 diabetes mellitus is of great importance in modern clinical practice. Given the prevalence of diabetes and its complications, including DPN, the development of predictive models will allow for personalized treatment approaches, optimization of rehabilitation programs, and improvement in the quality of life for patients. Integrating state-of-the-art data analysis methods and molecular-biological approaches into predictive models will contribute to the development of innovative rehabilitation strategies and improve treatment outcomes in this important patient population. The purpose of the study was to propose a multifactorial regression mathematical model for predicting the effectiveness of diabetic polyneuropathy rehabilitation. Materials and methods. Ninety-five patients with type 2 diabetes and DPN were examined to construct a predictive model of rehabilitation effectiveness using multiple regression analysis. The quality of the model was evaluated using the Nagelkerke criterion (R2). Results. The analysis revealed several significant associations between various factors and the effectiveness of rehabilitation in DPN patients. Specifically, an increase in age was associated with a predicted decrease in rehabilitation effectiveness by 0.103. Moreover, each increase in the duration of diabetes mellitus was associated with an expected decrease in rehabilitation effectiveness, ranging from 1.341 to 3.732 depending on the duration range. Similarly, changes in tobacco smoking, employment status, body mass index, glycated hemoglobin levels, mobility, self-care, usual activities, pain/discomfort, anxiety/depression, sensory sensitivities, DN4 scores, and lipid profile were all significantly associated with variations in rehabilitation effectiveness. The regression model demonstrated high explanatory power, with an observed correlation coefficient (rxy) of 0.997, indicating a strong functional relationship. Furthermore, the model was statistically significant (p < 0.001), sugges­ting that the identified predictors collectively explain 99.5 % of the observed variance in rehabilitation effectiveness. These findings underscore the importance of considering multiple factors when predicting rehabilitation outcomes in DPN patients and highlight the potential utility of the developed model in clinical practice. Conclusions. The proposed mathematical model for predicting the effectiveness of rehabilitation in type 2 diabetes patients with DPN demonstrates high acceptability, quality, and effectiveness. The application of this model, considering 99.5 % of DPN factors, will enhance the accuracy and timeliness of rehabilitation, improve treatment outcomes, facilitate regular monitoring of patients at high risk of complications, promote the development of informational leaflets and adapted programs for DPN prevention in type 2 diabetes patients, and facilitate the creation of relevant medical calculators and informational systems.