The comparison of three different acute kidney injury classification systems after congenital heart surgery
Abstract
Background
We aimed to compare the frequency of acute kidney injury (AKI) and its effects on mortality and morbidity with different classification systems in pediatric patients who had surgery under cardiopulmonary bypass for congenital heart disease.
Methods
This study included children younger than 18 years old who were followed up in the pediatric cardiac intensive care unit between September 1 and December 1, 2020, after congenital heart surgery with cardiopulmonary bypass. Each case was categorized postoperatively in terms of AKI using Pediatric-Modified Risk, Injury, Failure, Loss, and End-Stage (pRIFLE), Acute Kidney Injury Network (AKIN), and Kidney Disease: Improving Global Outcomes (KDIGO). Hospital mortality (developed within the first 30 days postoperatively) and morbidity (longer than 7 days intensive care unit stay) were compared by three model classes. Results were evaluated statistically.
Results
One hundred patients were included in the study. The median age was 3 months (1 day-180 months). Acute kidney injury was diagnosed in 49% of the cases according to the pRIFLE classification. It was diagnosed in 31% of the patients by AKIN classification. It was diagnosed in 41% of the patients with the KDIGO criteria. Morbidity was observed in 25% (n = 25) of all cases. The morbidity predictor was 0.800 for pRIFLE, 0.747 for AKIN and 0.853 for KDIGO by receiver operating characteristics analysis. All three categories predicted morbidity significantly (P < 0.001). Mortality was 10% (n = 10) for all groups. The mortality predictor was 0.783 for pRIFLE, 0.717 for AKIN and 0.794 for KDIGO by receiver operating characteristics analysis, and all three categories predicted mortality significantly (P < 0.001).
Conclusions
Regardless of the three methods used, AKI was commonly detected in pediatric patients undergoing congenital heart surgery. pRIFLE classification diagnosed more patients with AKI than AKIN and KDIGO. The KDIGO and pRIFLE classifications were better in predicting hospital mortality.