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@article{Gurudas:2021:10.1038/s41598-021-93096-w,
author = {Gurudas, S and Nugawela, M and Prevost, AT and Sathish, T and Mathur, R and Rafferty, JM and Blighe, K and Rajalakshmi, R and Mohan, AR and Saravanan, J and Majeed, A and Mohan, V and Owens, DR and Robson, J and Sivaprasad, S},
doi = {10.1038/s41598-021-93096-w},
journal = {Scientific Reports},
pages = {1--11},
title = {Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes},
url = {http://dx.doi.org/10.1038/s41598-021-93096-w},
volume = {11},
year = {2021}
}

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TY  - JOUR
AB  - 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 t
AU  - Gurudas,S
AU  - Nugawela,M
AU  - Prevost,AT
AU  - Sathish,T
AU  - Mathur,R
AU  - Rafferty,JM
AU  - Blighe,K
AU  - Rajalakshmi,R
AU  - Mohan,AR
AU  - Saravanan,J
AU  - Majeed,A
AU  - Mohan,V
AU  - Owens,DR
AU  - Robson,J
AU  - Sivaprasad,S
DO  - 10.1038/s41598-021-93096-w
EP  - 11
PY  - 2021///
SN  - 2045-2322
SP  - 1
TI  - Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-021-93096-w
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000687302800087&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.nature.com/articles/s41598-021-93096-w
UR  - http://hdl.handle.net/10044/1/91642
VL  - 11
ER  -

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