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Abstract：
BACKGROUND: Current treatment of acute leukemia is based on anthracycline chemotherapy. Anthracyclines, despite improving patient survival, have serious cardiotoxicity and therefore cardiac monitoring should be a priority. The purpose of this study is to explore the possible early predictors of anthracycline-induced subclinical cardiotoxicity(AISC)in acute leukemia patients.
METHODS: We conducted a prospective observational study involving 51 patients with acute leukemia treated with anthracycline. Demographic data, clinical variables, echocardiography variables and biochemical variables were collected at baseline and after 3 cycles of chemotherapy. Patients were divided into the AISC and No-AISC groups according to changes of global longitudinal peak systolic strain. Regression models and receiver operating characteristic curve analysis were used to explore the relationship between the variables and AISC.
RESULTS: 17 of the patients suffered subclinical cardiotoxicity after 3 cycles of anthracycline treatment. Multiple logistic regression analysis showed a significant association of DBil (OR 0.612, 95% CI 0.409-0.916, p = 0.017), TBil (OR 0.841, 95% CI 0.717-0.986, p = 0.033), PLT (OR 1.012, 95% CI 1.002-1.021, p = 0.016) and Glu (OR 1.873, 95% CI 1.009-3.475, p = 0.047) with the development of AISC. After 3 cycles of chemotherapy, there was a significant difference in PLT between the AISC and NO-AISC groups. Moreover, the dynamic changes in PLT from baseline to after 3 cycles of chemotherapy were each statistically significant in the AISC and NO-AISC groups. The combination of PLT and N-terminal pro-B-type natriuretic peptide (NT-proBNP) had the highest area under curves (AUC) for the diagnosis of AISC than PLT and NT-proBNP alone (AUC = 0.713, 95%CI: 0.56-0.87, P = 0.017).
CONCLUSIONS: Total bilirubin (TBil), direct bilirubin (DBil), platelets (PLT) and blood glucose (Glu) are independent influencing factors for AISC in acute leukemia patients receiving anthracycline therapy. Bilirubin may be a protective factor and PLT may be a contributing factor for AISC. The combination of baseline PLT and baseline NT-proBNP shows satisfactory predictive ability for AISC in acute leukemia cases treated with 3 cycles of chemotherapy.
METHODS: We conducted a prospective observational study involving 51 patients with acute leukemia treated with anthracycline. Demographic data, clinical variables, echocardiography variables and biochemical variables were collected at baseline and after 3 cycles of chemotherapy. Patients were divided into the AISC and No-AISC groups according to changes of global longitudinal peak systolic strain. Regression models and receiver operating characteristic curve analysis were used to explore the relationship between the variables and AISC.
RESULTS: 17 of the patients suffered subclinical cardiotoxicity after 3 cycles of anthracycline treatment. Multiple logistic regression analysis showed a significant association of DBil (OR 0.612, 95% CI 0.409-0.916, p = 0.017), TBil (OR 0.841, 95% CI 0.717-0.986, p = 0.033), PLT (OR 1.012, 95% CI 1.002-1.021, p = 0.016) and Glu (OR 1.873, 95% CI 1.009-3.475, p = 0.047) with the development of AISC. After 3 cycles of chemotherapy, there was a significant difference in PLT between the AISC and NO-AISC groups. Moreover, the dynamic changes in PLT from baseline to after 3 cycles of chemotherapy were each statistically significant in the AISC and NO-AISC groups. The combination of PLT and N-terminal pro-B-type natriuretic peptide (NT-proBNP) had the highest area under curves (AUC) for the diagnosis of AISC than PLT and NT-proBNP alone (AUC = 0.713, 95%CI: 0.56-0.87, P = 0.017).
CONCLUSIONS: Total bilirubin (TBil), direct bilirubin (DBil), platelets (PLT) and blood glucose (Glu) are independent influencing factors for AISC in acute leukemia patients receiving anthracycline therapy. Bilirubin may be a protective factor and PLT may be a contributing factor for AISC. The combination of baseline PLT and baseline NT-proBNP shows satisfactory predictive ability for AISC in acute leukemia cases treated with 3 cycles of chemotherapy.
摘要：
背景：目前急性白血病的治疗基于蒽环类化疗。蒽环类药物,尽管提高了患者的生存率，有严重的心脏毒性，因此心脏监测应优先考虑。目的探讨急性白血病患者蒽环类药物诱发亚临床心脏毒性(AISC)的可能早期预测因子。
方法：我们进行了一项前瞻性观察性研究，涉及51例接受蒽环类药物治疗的急性白血病患者。人口统计数据,临床变量，在基线和化疗3个周期后收集超声心动图变量和生化变量。根据整体纵向峰值收缩期应变的变化将患者分为AISC组和No-AISC组。使用回归模型和受试者工作特征曲线分析来探索变量与AISC之间的关系。
结果：17名患者在3个周期的蒽环类药物治疗后出现亚临床心脏毒性。多因素logistic回归分析显示DBil呈显著相关(OR0.612,95%CI0.409-0.916,p=0.017),TBil（OR0.841，95%CI0.717-0.986，p=0.033），随着AISC的发展，PLT(OR1.012，95%CI1.002-1.021，p=0.016)和Glu(OR1.873，95%CI1.009-3.475，p=0.047)。化疗3个周期后，AISC组和NO-AISC组之间的PLT存在显着差异。此外，AISC组和NO-AISC组从基线到化疗3个周期后PLT的动态变化均有统计学意义.PLT和N末端B型利钠肽前体（NT-proBNP）联合诊断AISC的曲线下面积（AUC）比单独使用PLT和NT-proBNP最高（AUC=0.713，95CI：0.56-0.87，P=0.017）。
结论：总胆红素（TBil），直接胆红素（DBil），血小板（PLT）和血糖（Glu）是接受蒽环类药物治疗的急性白血病患者AISC的独立影响因素。胆红素可能是AISC的保护因素，PLT可能是AISC的促成因素。基线PLT和基线NT-proBNP联合对接受3个周期化疗的急性白血病患者AISC显示出令人满意的预测能力。
方法：我们进行了一项前瞻性观察性研究，涉及51例接受蒽环类药物治疗的急性白血病患者。人口统计数据,临床变量，在基线和化疗3个周期后收集超声心动图变量和生化变量。根据整体纵向峰值收缩期应变的变化将患者分为AISC组和No-AISC组。使用回归模型和受试者工作特征曲线分析来探索变量与AISC之间的关系。
结果：17名患者在3个周期的蒽环类药物治疗后出现亚临床心脏毒性。多因素logistic回归分析显示DBil呈显著相关(OR0.612,95%CI0.409-0.916,p=0.017),TBil（OR0.841，95%CI0.717-0.986，p=0.033），随着AISC的发展，PLT(OR1.012，95%CI1.002-1.021，p=0.016)和Glu(OR1.873，95%CI1.009-3.475，p=0.047)。化疗3个周期后，AISC组和NO-AISC组之间的PLT存在显着差异。此外，AISC组和NO-AISC组从基线到化疗3个周期后PLT的动态变化均有统计学意义.PLT和N末端B型利钠肽前体（NT-proBNP）联合诊断AISC的曲线下面积（AUC）比单独使用PLT和NT-proBNP最高（AUC=0.713，95CI：0.56-0.87，P=0.017）。
结论：总胆红素（TBil），直接胆红素（DBil），血小板（PLT）和血糖（Glu）是接受蒽环类药物治疗的急性白血病患者AISC的独立影响因素。胆红素可能是AISC的保护因素，PLT可能是AISC的促成因素。基线PLT和基线NT-proBNP联合对接受3个周期化疗的急性白血病患者AISC显示出令人满意的预测能力。
