Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes

Abstract Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of s...

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Autores principales:	Sarega Gurudas, Manjula Nugawela, A. Toby Prevost, Thirunavukkarasu Sathish, Rohini Mathur, J. M. Rafferty, Kevin Blighe, Ramachandran Rajalakshmi, Anjana R. Mohan, Jebarani Saravanan, Azeem Majeed, Viswanthan Mohan, David R. Owens, John Robson, Sobha Sivaprasad, the ORNATE India Study Group
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ed6372bb348e41e0939b2808d1e378b6
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spelling	oai:doaj.org-article:ed6372bb348e41e0939b2808d1e378b62021-12-02T18:18:58ZDevelopment and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes10.1038/s41598-021-93096-w2045-2322https://doaj.org/article/ed6372bb348e41e0939b2808d1e378b62021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93096-whttps://doaj.org/toc/2045-2322Abstract Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.Sarega GurudasManjula NugawelaA. Toby PrevostThirunavukkarasu SathishRohini MathurJ. M. RaffertyKevin BligheRamachandran RajalakshmiAnjana R. MohanJebarani SaravananAzeem MajeedViswanthan MohanDavid R. OwensJohn RobsonSobha Sivaprasadthe ORNATE India Study GroupNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sarega Gurudas Manjula Nugawela A. Toby Prevost Thirunavukkarasu Sathish Rohini Mathur J. M. Rafferty Kevin Blighe Ramachandran Rajalakshmi Anjana R. Mohan Jebarani Saravanan Azeem Majeed Viswanthan Mohan David R. Owens John Robson Sobha Sivaprasad the ORNATE India Study Group Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
description	Abstract Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.
format	article
author	Sarega Gurudas Manjula Nugawela A. Toby Prevost Thirunavukkarasu Sathish Rohini Mathur J. M. Rafferty Kevin Blighe Ramachandran Rajalakshmi Anjana R. Mohan Jebarani Saravanan Azeem Majeed Viswanthan Mohan David R. Owens John Robson Sobha Sivaprasad the ORNATE India Study Group
author_facet	Sarega Gurudas Manjula Nugawela A. Toby Prevost Thirunavukkarasu Sathish Rohini Mathur J. M. Rafferty Kevin Blighe Ramachandran Rajalakshmi Anjana R. Mohan Jebarani Saravanan Azeem Majeed Viswanthan Mohan David R. Owens John Robson Sobha Sivaprasad the ORNATE India Study Group
author_sort	Sarega Gurudas
title	Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
title_short	Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
title_full	Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
title_fullStr	Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
title_full_unstemmed	Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
title_sort	development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ed6372bb348e41e0939b2808d1e378b6
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Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes

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