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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Main Author: Saputro S.A.
Other Authors: Mahidol University
Format: Article
Published: 2023
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/85783
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spelling 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
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Medicine
spellingShingle Medicine
Saputro S.A.
External validation of prognostic models for chronic kidney disease among type 2 diabetes
description 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.]
author2 Mahidol University
author_facet Mahidol University
Saputro S.A.
format Article
author Saputro S.A.
author_sort 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
title_full_unstemmed External validation of prognostic models for chronic kidney disease among type 2 diabetes
title_sort external validation of prognostic models for chronic kidney disease among type 2 diabetes
publishDate 2023
url https://repository.li.mahidol.ac.th/handle/123456789/85783
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