Tumor lysis syndrome in children with hematological malignancies: a nephrology perspective in resource-limited settings.

[BACKGROUND] Tumor lysis syndrome (TLS) is a major metabolic emergency in pediatric oncology and a leading cause of acute kidney injury (AKI) in children with hematological malignancies. Early identification of children at risk for severe AKI remains challenging.

[METHODS] This retrospective study included 50 children with laboratory or clinical TLS diagnosed according to the Howard-Pui classification. Serial biochemical parameters were analyzed over a 10-day period. AKI severity was classified using the pediatric Risk, Injury, Failure, Loss, End-stage renal disease (pRIFLE) criteria based on changes in estimated glomerular filtration rate (eGFR). Clinical characteristics, biochemical trends (especially phosphate and uric acid), and outcomes were compared between children with mild (pRIFLE 0 [no AKI]/R/I) and severe (pRIFLE-F) AKI.

[RESULTS] Twenty-seven patients had acute lymphoblastic leukemia (ALL), and 23 had lymphoma, mainly Burkitt's lymphoma. Clinical TLS accounted for 86% of cases, and 60% of children developed severe AKI. Severe AKI was significantly associated with spontaneous TLS onset, prolonged TLS duration, increased need for kidney therapy, intensive care admission, and higher mortality. Static demographic characteristics, malignancy type, tumor burden, and radiological findings did not differ between AKI severity groups. While hyperphosphatemia and hyperuricemia were common, dynamic phosphate changes showed the strongest association with AKI severity. The daily rise in serum phosphate before AKI onset demonstrated good discriminatory performance for predicting severe AKI (AUC 0.839), outperforming changes in uric acid.

[CONCLUSION] In pediatric TLS, AKI severity is the main determinant of clinical outcome. Dynamic phosphate kinetics (Delta phosphorus), rather than static biochemical thresholds, represent a robust early biomarker for identifying children at risk of severe AKI and may improve risk stratification, particularly in resource-limited settings.