MATHEMATICAL MODELING OF THE RATE OF PROGRESSION OF CHRONIC KIDNEY DISEASE IN TYPE 2 DIABETES MELLITUS

The aim: development and clinical substantiation of experimental mathematical models of the rate of progression of chronic kidney disease (CKD) in type 2 diabetes mellitus (DM) using the original diagnostic parameter glomerular filtration rate reduction index (RI_GFR). Material and methods: a cross-sectional observational study of clinical status indicators in a population sample of patients with type 2 diabetes was performed, significant predictors of a high rate of CKD progression were identified by regression analysis, three variants of experimental mathematical models with different combinations of arguments with an emphasis on modifiable factors were constructed. Results: the method of one-dimensional logistic regression analysis revealed indicators of clinical status that have a significant impact on the rate of progression of CKD on the scale of changes in RI_GFR by 1 ml/min/1.73 m2 and on the binary classification of outcomes in the groups of "slow" and "fast" decrease in filtration function of the kidneys with a threshold value of RI_GFR of 4.21 ml/min/1.73 m2 per year: age, body mass index (BMI), glycemia at the reception, duration of diabetes at the time of consultation, experience of insulin therapy, acute myocardial infarction in the anamnesis, pulse on the popliteal artery, concomitant retinopathy, risk group of hypertension, treatment with sulfonylureas and calcium antagonists. Using multidimensional logistic regression, three types of experimental mathematical models were constructed, including various combinations of predictors that demonstrated high values of diagnostic significance. Conclusions: mathematical modeling of the progression of CKD in type 2 diabetes with the use of the RI_GFR diagnostic index allows us to get new ideas about the patterns of development of the pathological process; an experimental mathematical model, including modifiable drug factors that a doctor can influence during treatment (administration of sulfonylureas and calcium antagonists), showed the following characteristics: sensitivity 55.6%; specificity 85.3%, AUC 0.76 (0.65; 0.86), which ensures high quality prediction with an accuracy of 77.5%.