A Two-Center Study of a Prognostic Model Related to Acute Kidney Injury After Allogeneic Hematopoietic Stem Cell Transplantation in Children: Development of a New Predictive Dynamic Nomogram.

OBJECTIVES

The purpose of this study was to develop a straightforward, easy-to-use online dynamic nomogram for the identification of children who are at high risk of developing acute kidney injury (AKI) after allogeneic hematopoietic stem cell transplantation (allo-HSCT).

METHODS

This was a two-center study in which 242 children in Henan Provincial Children's Hospital composed the training cohort, and 115 children in the First Affiliated Hospital of Zhengzhou University composed the validation cohort. Kaplan-Meier survival analysis was used to compare survival between children with nonacute kidney injury (NAKI) and children with AKI. Multivariate logistic regression analysis was used to identify risk factors for AKI in children who underwent HSCT. The selected variables were utilized to construct nomograms, which were validated via the concordance index (C-index), decision curve analysis, calibration curve analysis, and receiver operating characteristic (ROC) curve analysis.

RESULTS

Cumulative survival was significantly lower in children with AKI than in children without kidney injury (p < 0.01). Eight variables were included in the nomogram: hepatic veno-occlusive disease (HVOD), graft-versus-host disease (GVHD), ferritin, C-reactive protein (CRP), Cytomegalovirus infection (CMV), thrombotic microangiopathy (TMA), human leukocyte antigen (HLA), and nephrotoxic drugs. The nomogram calibration curves in the training and validation cohorts were highly comparable to the standard curves. The areas under the curve (AUCs) of the prediction model were 0.963 and 0.910 in the training cohort and validation cohort, respectively. The decision curve analysis (DCA) revealed that the model had a significant clinical benefit.

CONCLUSIONS

The occurrence of AKI affects the prognosis of children who undergo HSCT. We developed a dynamic online nomogram for predicting AKI in children who underwent allo-HSCT on the basis of eight variables. The predictive value and clinical benefit of the nomogram model were acceptable.