External validation of prognostic models for chronic kidney disease among type 2 diabetes
Background: Various 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 progno...
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th-mahidol.857832023-06-19T00:48:45Z External validation of prognostic models for chronic kidney disease among type 2 diabetes Saputro S.A. Mahidol University Medicine Background: Various 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. Methods: A 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. Results: Six 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. Conclusions: All 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: [Figure not available: see fulltext.] 2023-06-18T17:48:45Z 2023-06-18T17:48:45Z 2022-07-01 Article Journal of Nephrology Vol.35 No.6 (2022) , 1637-1653 10.1007/s40620-021-01220-w 17246059 11218428 34997924 2-s2.0-85122691941 https://repository.li.mahidol.ac.th/handle/123456789/85783 SCOPUS |
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Medicine Saputro S.A. External validation of prognostic models for chronic kidney disease among type 2 diabetes |
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Background: Various 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. Methods: A 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. Results: Six 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. Conclusions: All 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: [Figure not available: see fulltext.] |
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Saputro S.A. |
title |
External validation of prognostic models for chronic kidney disease among type 2 diabetes |
title_short |
External validation of prognostic models for chronic kidney disease among type 2 diabetes |
title_full |
External validation of prognostic models for chronic kidney disease among type 2 diabetes |
title_fullStr |
External validation of prognostic models for chronic kidney disease among type 2 diabetes |
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External validation of prognostic models for chronic kidney disease among type 2 diabetes |
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external validation of prognostic models for chronic kidney disease among type 2 diabetes |
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2023 |
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https://repository.li.mahidol.ac.th/handle/123456789/85783 |
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