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

the ORNATE India Study Group

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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.

Original languageEnglish
Article number13654
Number of pages11
JournalScientific Reports
Issue number1
Publication statusPublished - 1 Jul 2021
Externally publishedYes

Bibliographical note

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This work was funded by Global Challenges Research Fund and UK Research and Innovation through the Medical Research Council grant number MR/P027881/1. The research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. Azeem Majeed is supported by the NIHR NW London Applied Research Collaboration. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We are grateful to general practitioners and Clinical Effectiveness Group in east London for access to unselected whole population data.

Publisher Copyright:
© 2021, The Author(s).


  • Diagnosis
  • Disease prevention
  • Diseases
  • Endocrinology
  • Health services
  • Medical research
  • Nephrology
  • Public health
  • Risk factors

ASJC Scopus subject areas

  • General


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