Development and validation of a novel nomogram to predict diabetic kidney disease in patients with type 2 diabetic mellitus and proteinuric kidney disease.

PURPOSE

Differentiating between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) in patients with Type 2 diabetes mellitus (T2DM) is important due to implications on treatment and prognosis. Clinical methods to accurately distinguish DKD from NDKD are lacking. We aimed to develop and validate a novel nomogram to predict DKD in patients with T2DM and proteinuric kidney disease to guide decision for kidney biopsy.

METHODS

A hundred and two patients with Type 2 Diabetes Mellitus (T2DM) who underwent kidney biopsy from 1st January 2007 to 31st December 2016 were analysed. Univariate and multivariate analyses were performed to identify predictive variables and construct a nomogram. The discriminative ability of the nomogram was assessed by calculating the area under the receiver operating characteristic curve (AUROC), while calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plot. Internal validation of the nomogram was assessed using bootstrap resampling.

RESULTS

Duration of T2DM, HbA1c, absence of hematuria, presence of diabetic retinopathy and absence of positive systemic biomarkers were found to be independent predictors of DKD in multivariate analysis and were represented as a nomogram. The nomogram showed excellent discrimination, with a bootstrap-corrected C statistic of 0.886 (95% CI 0.815-0.956). Both the calibration curve and the Hosmer-Lemeshow goodness-of-fit test (p = 0.242) showed high degree of agreement between the prediction and actual outcome, with the bootstrap bias-corrected curve similarly indicating excellent calibration.

CONCLUSIONS

A novel nomogram incorporating 5 clinical parameters is useful in predicting DKD in type 2 diabetes mellitus patients with proteinuric kidney disease.