The development and validation of a risk score for predicting microalbuminuria in type 2 diabetic patients

Abstract

Objective: To develop and validate a prognostic scoring scheme for the prediction of microalbuminuria in type 2 diabetic patients of Thai descent. Methods: The clinical information from type 2 diabetic patients who were treated at community hospitals was used to develop a prediction model (derivation set). The model evaluated at a tertiary hospital (validation set). A stepwise logistic regression model was used to identify the independent risk variables from the derivation set and a simple point scoring system was derived from the beta-coefficients. The risk scoring scheme was validated by the validation set. Results: The risk scoring scheme is based on six risk predictors: the duration of diabetes, age at the onset of diabetes, systolic blood pressure, low density lipoprotein levels, creatinine levels, and alcohol consumption. The total score ranged from 0 to 11.5. The likelihood of microalbuminuria in patients with low risk (scores ≤ 2) was 0.28, with moderate risk (scores 2.5 to 5.5) was 0.86, and high risk (scores ≥ 6) was 7.36. The area under the ROC curve of the derivation set and validation set were 0.768 (95% CI 0.73 - 0.81) and 0.758 (95% CI 0.70 - 0.80), respectively. Conclusion: Our scoring system is a simple and reasonably accurate method for predicting the future presence of microalbuminuria in type 2 diabetic patients

Predictive modeling using a nationally representative database to identify patients at risk of developing microalbuminuria

“…Eghan et al [25] in a cross-sectional study including diabetes subjects found that BUN, among other variables, was a predictor of microalbuminuria. Zakerdish et al found in a study that albuminuria in diabetes subjects is correlated with BUN level, HbA1c, and duration of diabetes mellitus; however, no model was proposed to calculate the probability of subjects developing microalbuminuria [26] Two other models proposed by Scheven et al [18] and by Mongkolsomlit et al [16] did not consider the inclusion of BUN measures in the development of their corresponding models.…”

“…Nevertheless, the findings generally agree with those of previous studies. A study conducted in 2012 by Mongkolsomlit et al [16] included diabetic subjects in one Thai hospital. The researchers collected patient variables (e.g., age, gender, body mass index, glycemic control, blood pressure, dyslipidemia) through a case-control study design.…”

“…However, some intervals (below the most normal zero-assigned range) were assigned negative values. Therefore, a subject could obtain a final score anywhere between −5 and 18 (Table 2) [15][16][17].…”

Association between glycemic control and albuminuria among Peruvian adults with diabetes mellitus 2: a cross-sectional analytical study

“…As described in the literature, we entered potential confounding variables into the multivariable model using an epidemiological approach. 29 Additionally, we evaluated collinearity between the variables before entering them into the multivariable model.…”