No Cover Image

Journal article 65 views 17 downloads

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

  • 58154.pdf

    PDF | Version of Record

    © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License

    Download (1.75MB)

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...

Full description

Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58154
Tags: Add Tag
No Tags, Be the first to tag this record!
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.
College: Swansea University Medical School
Funders: Global Challenges Research Fund and UK Research and Innovation through the Medical Research Council grant number MR/P027881/1
Issue: 1