Abstract
BackgroundVarious prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort.MethodsA nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting.ResultsSix relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565–0.605) to 0.786 (0.765–0.806) for CKD and 0.657 (0.610–0.703) to 0.760 (0.705–0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975–1.024) to 1.009 (0.929–1.090). Hosmer–Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low’s of 0.114 for CKD and Elley’s of 0.025 for ESRD.ConclusionsAll models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low’s (developed in Singapore) and Elley’s model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand.Graphical abstract
Highlights
Chronic Kidney Disease (CKD) is a major worldwide health burden and the most common microvascular complication of type 2 diabetes (T2D) [1, 2]
We focused on external validation of existing models of CKDESRD risk predictions in T2D, supplemented with the addition of routine clinical factors to potentially increase the discriminatory power in our local population [18]
We focused on prognostic factors identified through our systematic review, including demographics, biomarkers, comorbidities, medication usage, and clinical features; the latter included diabetes duration, body mass index (BMI; kg/m2), waist and hip circumference, systolic/diastolic (SBP/DBP) blood pressure, pulse, smoking, alcohol consumption, dietary control measures, physical activity, dyslipidaemia, hypertension, and family history of diabetes (FHD, presence of T2D in 1 st-degree relatives)
Summary
Chronic Kidney Disease (CKD) is a major worldwide health burden and the most common microvascular complication of type 2 diabetes (T2D) [1, 2]. Various prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). Their generalisability and predictive performance in different populations remain largely unvalidated. We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low’s of 0.114 for CKD and Elley’s of 0.025 for ESRD. Low’s (developed in Singapore) and Elley’s model (developed in New Zealand), outperformed the other models evaluated These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand
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