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Validation of a Risk Prediction Model for Early Chronic Kidney Disease in Patients with Type 2 Diabetes: Data from the German/Austrian DPV Registry.

AIMS
Chronic kidney disease (CKD) is characterized by impaired kidney function and/or elevated urine albumin excretion and is the major cause of end-stage kidney disease in patients with type 2 diabetes (T2D). Early detection and prevention delays disease progression. This study aimed to validate a recently proposed risk prediction model for CKD in T2D.


MATERIALS AND METHODS
Subjects from the German/Austrian Diabetes Prospective Follow-up (DPV) registry with T2D, normoalbuminuria, an estimated glomerular filtration rate of ≥60 mL/min/1.73 m2 , and aged 39-75 were included. Prognostic factors included age, body mass index (BMI), smoking status, and hemoglobin A1C (HbA1c). Subjects were categorized into low, moderate, high, and very high-risk groups. Outcome was CKD occurrence.


RESULTS
Subjects (n=10,922) had mean age of 61 years, 6 years' diabetes duration, 31.7 kg/m2 BMI, 6.9% HbA1c (52 mmol/mol); 9.1% had diabetic retinopathy; and 16.3% were smokers. After follow-up (~59 months), 37.4% subjects developed CKD. The area under the curve (AUC; unadjusted base model) was 0.58 (95% CI 0.57-0.59). After adjustment for diabetes and follow-up duration, the AUC was 0.69 (95% CI 0.68-0.70), indicating improved discrimination. After follow-up, 15.0%, 20.1%, 27.7%, and 40.2% patients in the low, moderate, high, and very high-risk groups, respectively, had developed CKD. Increasing risk score correlated with increasing cumulative risk of incident CKD over a median of 4.5 years of follow-up (p<0.0001).


CONCLUSIONS
The predictive model achieved moderate discrimination but good calibration in a German/Austrian T2D population suggesting that the model may be relevant for determining CKD risk. This article is protected by copyright. All rights reserved.