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.
Highlights
Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced
The development cohort was extracted from a fully anonymised primary care dataset consisting of 105,533 people with type 2 diabetes (T2D) of multi-ethnic origin registered with 134 general practices (GP) in inner London(London cohort)[19]
In this study we developed 3 risk models for predicting the onset of stage 3 chronic kidney disease (CKD) in multiethnic persons with T2D within an economically and socially deprived region of inner London
Summary
Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. 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 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. There is a paucity of resource-friendly CKD risk models containing predictors that do not require costly technical or laboratory expertise and resources (table S1) that can be applied in L MICs17,18. We considered these limiting factors and aimed to build resource-driven stage 3 CKD predictive models that could be applied globally and in resource-constrained environments
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