Global Validation of a Model to Predict Reduced Estimated GFR in People With Type 2 Diabetes Without Diagnosis of CKD.

INTRODUCTION

A minimal-resource model for predicting reduced kidney function among people with type 2 diabetes and no diagnosis of chronic kidney disease (CKD) stages 3 to 5 was previously developed in a UK population to pre-screen for undiagnosed CKD. This study aims to evaluate the performance of the model on a global population and assess its adequacy with and without regional adjustment.

METHODS

A retrospective observational study was performed using data collected from the iCaReMe global registry (NCT03549754) and the DISCOVER study (NCT02322762 and NCT02226822). Patients were grouped by their World Health Organization classified region. An estimated glomerular filtration rate (eGFR) <60 ml/min per 1.73 m was the marker of reduced kidney function. A regional-intercept recalibration was applied to adjust for regional variation. Discrimination and calibration were evaluated for the UK-developed and recalibrated models.

RESULTS

A total of 14,180 patients (46 countries, 6 regions) were identified with type 2 diabetes, no previous diagnosis of CKD stages 3 to 5, and had a serum creatinine measurement or eGFR recorded. The UK model underestimated risk when applied globally and was deemed inadequate. The model with regional adjustment achieved the target sensitivity (80.5%; 95% confidence interval [CI]: 78.8%-82.3%) and demonstrated a relative improvement of 51.5% (95% CI: 48.1%-55.1%) in the positive predictive value (PPV), compared to a screen-all approach.

CONCLUSION

The regional-adjusted model demonstrated adequate performance globally. Incorporating the model within practice could help clinicians to risk-stratify and prioritize patients at high risk. This could enable improved efficiency via risk-tailored screening, particularly in lower-middle-income countries (LMICs).