# Optimization of Kidney Disease: Improving Global Outcomes Criteria for AKI for Pediatric Population

## TL;DR

This study creates and validates new criteria for diagnosing acute kidney injury in children, showing better performance in predicting mortality than existing methods.

## Contribution

The study introduces a pediatric-specific adaptation of KDIGO criteria for AKI that accounts for age and sex differences in children.

## Key findings

- pKDIGO criteria showed higher AUCs for predicting mortality compared to other definitions in the BCH cohort.
- The risk of death increased with higher AKI stages defined by pKDIGO in both general wards and ICUs.
- pKDIGO outperformed KDIGO, mKDIGO, pROCK, and pRIFLE in identifying AKI and predicting in-hospital death in children.

## Abstract

Accurate detection and staging of acute kidney injury (AKI) is important in clinical practice to aid timely management. The main purpose of this study is to establish a pediatric version of Kidney Disease: Improving Global Outcomes (KDIGO, pKDIGO) criteria for pediatric population.

The pKDIGO criteria defined AKI following the principles of KDIGO, in which the threshold of absolute increase in serum creatinine (SCr) or absolute decrease in estimated glomerular filtration rate (GFR, eGFR) to diagnose AKI has been revised to eliminate the impacts of age and sex of children. Then, AKI defined by pKDIGO were compared with that defined by KDIGO, modified KDIGO (mKDIGO), pediatric reference change value optimized for AKI in children (pROCK), and pediatric Risk for renal dysfunction, Injury to the kidney, Failure of kidney function, Loss of kidney function, and End-stage renal disease (RIFLE, pRIFLE) based on 2 retrospective cohorts in China: Beijing Children’s Hospital (BCH) cohort and intensive care units (ICUs) of the Children’s Hospital of Zhejiang University School of Medicine (ICU) cohort. The performance of different AKI definitions was compared based on the area under the receiver operating characteristic curves (AUCs) for predicting the in-hospital death.

Total of 57,229 children in the BCH cohort and 8276 children in the ICU cohort were used to evaluate the performance of pKDIGO. In the BCH cohort, AUCs for predicting mortality by AKI defined based on pKDIGO (AUC = 0.75, 0.72–0.78) were higher than that defined by other definitions. The risk of death increases with higher stage of AKI defined by pKDIGO. Similar results were observed in the ICU cohort.

The pKDIGO criteria showed a better ability to identify patients with AKI and predict in-hospital death in children, both in general wards and ICUs.