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

Sarega Gurudas, Manjula Nugawela, A. Toby Prevost, Thirunavukkarasu Sathish, Rohini Mathur, Jim Rafferty Orcid Logo, Kevin Blighe, Ramachandran Rajalakshmi, Anjana R. Mohan, Jebarani Saravanan, Azeem Majeed, Viswanthan Mohan, David Owens Orcid Logo, John Robson, Sobha Sivaprasad, (the ORNATE India Study Group)

Scientific Reports, Volume: 11, Issue: 1

Swansea University Authors: Jim Rafferty Orcid Logo, David Owens Orcid Logo

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DOI (Published version): 10.1038/s41598-021-93096-w

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 CK...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2021
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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&#x2013;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&#x2013;1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82&#x2013;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. 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spelling 2021-10-25T16:17:20.5743854 v2 58154 2021-09-30 Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes 52effe759a718bd36eb12cdd10fe1a09 0000-0002-1667-7265 Jim Rafferty Jim Rafferty true false 2fd4b7c3f82c6d3bd546eff61ff944e9 0000-0003-1002-1238 David Owens David Owens true false 2021-09-30 HDAT 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. Journal Article Scientific Reports 11 1 Springer Science and Business Media LLC 2045-2322 1 7 2021 2021-07-01 10.1038/s41598-021-93096-w COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University Global Challenges Research Fund and UK Research and Innovation through the Medical Research Council grant number MR/P027881/1 2021-10-25T16:17:20.5743854 2021-09-30T11:21:31.6414495 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Sarega Gurudas 1 Manjula Nugawela 2 A. Toby Prevost 3 Thirunavukkarasu Sathish 4 Rohini Mathur 5 Jim Rafferty 0000-0002-1667-7265 6 Kevin Blighe 7 Ramachandran Rajalakshmi 8 Anjana R. Mohan 9 Jebarani Saravanan 10 Azeem Majeed 11 Viswanthan Mohan 12 David Owens 0000-0003-1002-1238 13 John Robson 14 Sobha Sivaprasad 15 (the ORNATE India Study Group) 16 58154__21296__8b82638c2f8e4cb5b01c85de835d5eca.pdf 58154.pdf 2021-10-25T16:16:02.3182584 Output 1839125 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
spellingShingle Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
Jim Rafferty
David Owens
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
author_id_str_mv 52effe759a718bd36eb12cdd10fe1a09
2fd4b7c3f82c6d3bd546eff61ff944e9
author_id_fullname_str_mv 52effe759a718bd36eb12cdd10fe1a09_***_Jim Rafferty
2fd4b7c3f82c6d3bd546eff61ff944e9_***_David Owens
author Jim Rafferty
David Owens
author2 Sarega Gurudas
Manjula Nugawela
A. Toby Prevost
Thirunavukkarasu Sathish
Rohini Mathur
Jim Rafferty
Kevin Blighe
Ramachandran Rajalakshmi
Anjana R. Mohan
Jebarani Saravanan
Azeem Majeed
Viswanthan Mohan
David Owens
John Robson
Sobha Sivaprasad
(the ORNATE India Study Group)
format Journal article
container_title Scientific Reports
container_volume 11
container_issue 1
publishDate 2021
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-021-93096-w
publisher Springer Science and Business Media LLC
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description 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.
published_date 2021-07-01T04:14:27Z
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score 11.012678

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