Fig. 1
From: Variability of risk factors and diabetes complications
(Reproduced with permission from Ref. [36])
Recursive partitioning techniques (RECPAM) analysis of developing albuminuria in a cohort of 4231 patients with T2D followed up for a median of 3.4 years and with 5 subsequent measurements of risk factors [36]. The tree-growing algorithm resumes the hazard of developing albuminuria according to a multivariable Cox regression analysis. At each step, the method proceeds forward using the covariate with the highest difference in risk. The algorithm proceeds until user-defined conditions are met. Variables used to build the model were quartiles of variability in HbA1c, systolic blood pressure (SBP) and diastolic blood pressure (DBP), serum uric acid (UA), total, high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides., while additional baseline parameters were considered in the model as global variables, i.e. age, gender, duration of diabetes, smoking, hypertension, baseline HbA1c, blood pressure, UA, lipid parameters and estimated glomerular filtration rate (eGFR) values. The variable determining patient’s assignment to the subsequent group is evidenced on the branch proceeding to the following subgroup, while rectangles represent the REPCAM class. The numbers in the circles and rectangles represent the patients who develop albuminuria compared with the total number of patients in the subgroup, respectively