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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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Autores principales: Saputro, Sigit Ari, Pattanateepapon, Anuchate, Pattanaprateep, Oraluck, Aekplakorn, Wichai, McKay, Gareth J., Attia, John, Thakkinstian, Ammarin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300508/
https://www.ncbi.nlm.nih.gov/pubmed/34997924
http://dx.doi.org/10.1007/s40620-021-01220-w
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author Saputro, Sigit Ari
Pattanateepapon, Anuchate
Pattanaprateep, Oraluck
Aekplakorn, Wichai
McKay, Gareth J.
Attia, John
Thakkinstian, Ammarin
author_facet Saputro, Sigit Ari
Pattanateepapon, Anuchate
Pattanaprateep, Oraluck
Aekplakorn, Wichai
McKay, Gareth J.
Attia, John
Thakkinstian, Ammarin
author_sort Saputro, Sigit Ari
collection PubMed
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: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40620-021-01220-w.
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spelling pubmed-93005082022-07-22 External validation of prognostic models for chronic kidney disease among type 2 diabetes Saputro, Sigit Ari Pattanateepapon, Anuchate Pattanaprateep, Oraluck Aekplakorn, Wichai McKay, Gareth J. Attia, John Thakkinstian, Ammarin J Nephrol original Article 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: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40620-021-01220-w. Springer International Publishing 2022-01-08 2022 /pmc/articles/PMC9300508/ /pubmed/34997924 http://dx.doi.org/10.1007/s40620-021-01220-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle original Article
Saputro, Sigit Ari
Pattanateepapon, Anuchate
Pattanaprateep, Oraluck
Aekplakorn, Wichai
McKay, Gareth J.
Attia, John
Thakkinstian, Ammarin
External validation of prognostic models for chronic kidney disease among type 2 diabetes
title 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_short 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
topic original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300508/
https://www.ncbi.nlm.nih.gov/pubmed/34997924
http://dx.doi.org/10.1007/s40620-021-01220-w
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