Kidney Failure Prediction: Multicenter External Validation of KFRE Model in Patients with CKD Stages 3-4 in Peru

ABSTRACT Background To externally validate the 4-variable Kidney Failure Risk Equation (KFRE) in the Peruvian population for predicting kidney failure at 2 and 5 years. Methods We included patients from 17 primary care centers from the Health’s Social Security of Peru. Patients older than 18 years, diagnosed with chronic kidney disease (CKD) stage 3a-3b-4 and 3b-4, between January 2013 and December 2017. Patients were followed until they developed kidney failure, died, were lost, or ended the study (December 31, 2019), whichever came first. Performance of the KFRE model was assessed based on discrimination and calibration measures considering the competing risk of death. Results We included 7519 patients in stages 3a-4 and 2,798 patients in stages 3b-4. KFRE discrimination at 2 and 5 years was high, with Time-Dependent Area Under the Curve (AUC-td) and C-index > 0.8 for all populations. Regarding calibration in-the-large, the Observed-to-Expected (O/E) ratio and the calibration intercept indicated that KFRE underestimates the overall risk at two years and overestimates it at 5-years in all populations. Conclusions The 4-variable KFRE models have good discrimination but poor calibration in the Peruvian population. The model underestimates the risk of kidney failure in the short term and overestimates it in the long term. SIGNIFICANCE STATEMENT The Kidney Failure Risk Equation (KFRE) is a widely used prediction model for kidney failure risk assessment in patients with chronic kidney disease (CKD). However, its performance in Latin American populations remains unclear, particularly in primary care settings. This study externally validated the KFRE in Peruvian CKD patients, demonstrating high discrimination but revealing miscalibration that could lead to adverse patient outcomes resulting from over- or under-estimation of risk. These results underscore the need for model updating and further research to optimize the KFRE’s use in clinical practice in Latin America, provide valuable insights for applying the KFRE in Latin American settings, and highlight the importance of continuous evaluation and refinement of prediction models in diverse populations.