Predicting the risk of chronic kidney disease among type 2 diabetes mellitus patients in a primary care setting: An evaluation of the QKidney model

Introduction: Diabetes mellitus is a major risk factor for chronic kidney disease (CKD). Thus, making routine screening among the diabetic group is necessary in order to reduce the burden of the disease. As such, various risk prediction models including QKidney model have been developed for early detection of CKD. However, the Qkidney model has not been validated in Malaysia. This study aimed to evaluate the performance of QKidney model in predicting a 5-year risk of developing CKD in a cohort of type 2 diabetes mellitus (T2DM) patients in the primary care setting. Methods: A total of 377 T2DM patients attended the primary care clinic at the town of Rawang, aged 30-74 years old, and free of CKD outcomes at baseline were recruited and followed-up for 5 years. Their CKD risk was calculated using the QKidney model. The predictive performance of QKidney model was assessed through discrimination and calibration analyses. Results: At the end of the 5-year follow-up, a total median QKidney score was 3.9% (IQR: 5.9). The median QKidney score of male participants (7.3%) was significantly higher than that of the females (3.0%) (p < 0.001). The QKidney model has a moderate discrimination in which the area under the receiver operating characteristic curve was 0.748, with good calibration (χ2 = 13.039, p = 0.111). Conclusion: It was found that the QKidney model had a moderate discriminative ability with good calibration. When taken together, it was suggested that the QKidney model could be utilized to predict a moderate-to-severe CKD risk in Malaysians with T2DM. © 2019 UPM Press. All rights reserved