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Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
,
Kevin Blighe,
Ramachandran Rajalakshmi,
Anjana R. Mohan,
Jebarani Saravanan,
Azeem Majeed,
Viswanthan Mohan,
David Owens ,
John Robson,
Sobha Sivaprasad,
(the ORNATE India Study Group)
Scientific Reports, Volume: 11, Issue: 1
Swansea University Authors:
Jim Rafferty , David Owens
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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...
| Published in: | Scientific Reports |
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| ISSN: | 2045-2322 |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58154 |
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| 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. |
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| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
Global Challenges Research Fund and UK Research and Innovation through the Medical Research Council grant number MR/P027881/1 |
| Issue: |
1 |