# Recalibrating the kidney failure risk equation for a Mediterranean European population: reducing age and sex inequality

## TL;DR

This study recalibrates a kidney failure risk equation for a Mediterranean European population to reduce age and sex bias in predicting kidney failure.

## Contribution

The study recalibrates the Kidney Failure Risk Equation for a Mediterranean European primary care population, addressing age and sex disparities.

## Key findings

- Original KFRE-4 overestimated kidney failure risk in this population.
- Recalibrated models like FG-RRT improved prediction by considering death as a competing risk.
- Recalibrating for kidney failure prediction reduced the influence of age and sex.

## Abstract

Chronic kidney disease (CKD) patients may develop kidney failure (KF), receiving renal replacement therapy (RRT) in some cases. The Kidney Failure Risk Equation (KFRE-4), predicting RRT risk, is widely validated but not in a primary care Mediterranean European population. We aim to recalibrate KFRE-4 accordingly, considering death as a competing risk, to improve performance. Additionally, we recalibrate KFRE-4 for predicting KF, including all patients reaching CKD stage 5, not just those on RRT.

Retrospective cohort study including individuals aged ≥50 years with confirmed glomerular filtration rate (eGFR) <60 mL/min/1.73m2 and measured albumin-to-creatinine ratio (ACR). Dataset was split into training and test sets. New KFRE-4 models were developed in the training set and performance was evaluated in the test set: Base hazard adapted-KFRE (Basic-RRT), Cox reestimation (Cox- RRT), Fine and Gray RRT reestimation (FG-RRT), and Fine and Gray KF reestimation (FG-KF).

Among 165,371 primary care patients (58.1% female; mean age 78.1 years; mean eGFR 47.3 mL/min/1.73m2, median ACR 10.1 mg/g), original KFRE-4 showed good discrimination but poor calibration, overestimating RRT risk. Basic-RRT showed poorer performance. Cox-RRT and FG-RRT, enhancing the influence of old age and female sex, diminished overprediction. FG-RRT, considering death as a competing risk, resulted the best RRT model. Age and sex had less impact on KF prediction.

A fully tailored recalibration model diminished RRT overprediction. Considering death as a competing event optimizes performance. Recalibrating for KF prediction offers a more inclusive approach in primary care, addressing the needs of women and elderly.