A predictive model for the detection of tumor lysis syndrome during AML induction therapy

Anthony R. Mato, Brett E. Riccio, Li Qin, Daniel F. Heitjan, Martin Carroll, Alison Loren, David L. Porter, Alexander Perl, Edward Stadtmauer, Donald Tsai, Alan Gewirtz, Selina M. Luger

Research output: Contribution to journalArticle

47 Scopus citations

Abstract

Tumor lysis syndrome (TLS) is defined by metabolic derangements occurring in the setting of rapid tumor destruction. In acute myelogenous leukemia (AML), TLS frequency, risk stratification, monitoring, and management strategies are based largely on case series and data from other malignancies. A single-center, retrospective cohort study was conducted to estimate TLS incidence and identify TLS predictive factors in a patient population undergoing myeloid leukemia induction chemotherapy. This study included 194 patients, aged 18-86 years, with AML or advanced myelodysplastic syndrome undergoing primary myeloid leukemia induction chemotherapy. Nineteen patients (9.8%) developed TLS. In univariate analysis, elevated pre-chemotherapy values for uric acid (P < 0.0001), creatinine (P = 0.0025), lactate dehydrogenase (LDH) (P = 0.0001), white blood cell (P = 0.0058), gender (P = 0.0064) and chronic myelomonocytic leukemia history (P = 0.0292) were significant predictors. In multivariate analysis, LDH (P=0.0042), uric acid (P < 0.0001) and gender (P = 0.0073) remained significant TLS predictors. A predictive model was then designed using a scoring system based on these factors. This analysis may lay the groundwork for the development of the first evidence-based guidelines for TLS monitoring and management in this patient population.

Original languageEnglish (US)
Pages (from-to)877-883
Number of pages7
JournalLeukemia and Lymphoma
Volume47
Issue number5
DOIs
StatePublished - May 1 2006

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Keywords

  • Acute myeloid leukemia
  • Acute renal failure
  • Hyperuricemia
  • Tumor lysis syndrome
  • Urate nephropathy

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Cancer Research

Cite this

Mato, A. R., Riccio, B. E., Qin, L., Heitjan, D. F., Carroll, M., Loren, A., Porter, D. L., Perl, A., Stadtmauer, E., Tsai, D., Gewirtz, A., & Luger, S. M. (2006). A predictive model for the detection of tumor lysis syndrome during AML induction therapy. Leukemia and Lymphoma, 47(5), 877-883. https://doi.org/10.1080/10428190500404662