Mato Anthony R, Riccio Brett E, Qin Li, Heitjan Daniel F, Carroll Martin, Loren Alison, Porter David L, Perl Alexander, Stadtmauer Edward, Tsai Donald, Gewirtz Alan, Luger Selina M
Hematologic Malignancies Program, Division of Hematology and Oncology, Department of Internal Medicine, University of Pennsylvania Medical Center, Philadelphia, PA 19104, USA.
Leuk Lymphoma. 2006 May;47(5):877-83. doi: 10.1080/10428190500404662.
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.
肿瘤溶解综合征(TLS)定义为在快速肿瘤破坏情况下发生的代谢紊乱。在急性髓系白血病(AML)中,TLS的发生率、风险分层、监测和管理策略很大程度上基于病例系列以及其他恶性肿瘤的数据。进行了一项单中心回顾性队列研究,以估计接受髓系白血病诱导化疗的患者群体中TLS的发生率并确定TLS的预测因素。该研究纳入了194例年龄在18 - 86岁之间、患有AML或晚期骨髓增生异常综合征并接受原发性髓系白血病诱导化疗的患者。19例患者(9.8%)发生了TLS。在单变量分析中,化疗前尿酸(P < 0.0001)、肌酐(P = 0.0025)、乳酸脱氢酶(LDH)(P = 0.0001)、白细胞(P = 0.0058)、性别(P = 0.0064)和慢性粒单核细胞白血病病史(P = 0.0292)升高是显著的预测因素。在多变量分析中,LDH(P = 0.0042)、尿酸(P < 0.0001)和性别(P = 0.0073)仍然是TLS的显著预测因素。然后基于这些因素使用评分系统设计了一个预测模型。该分析可能为制定该患者群体中TLS监测和管理的首个循证指南奠定基础。