Prediction of Early Death After Induction Therapy for Newly Diagnosed Acute Myeloid Leukemia With Pretreatment Risk Scores: A Novel Paradigm for Treatment Assignment
Prediction of Early Death After Induction Therapy for Newly Diagnosed Acute Myeloid Leukemia With Pretreatment Risk Scores: A Novel Paradigm for Treatment Assignment
复制标题
DOI：
10.1200/jco.2011.35.7525
复制
发表时间：
2011-11-20
影响因子：
45.3
通讯作者：
Estey, Elihu H.
中科院分区：
文献类型：
--
作者：
Walter, Roland B.;Othus, Megan;Estey, Elihu H.
目的急性髓系白血病 (AML) 的预后随着年龄的增长而恶化，至少部分原因是老年患者的治疗相关死亡率 (TRM) 较高。因此，强化 AML 治疗方案的资格通常基于年龄作为 TRM 的隐含主要预测因素，尽管其他健康和疾病相关因素调节了这种年龄效应。 患者和方法 我们使用 3,365 名各年龄段成年人对新诊断的 AML 进行强化化疗的每周估计危险率，根据经验定义了 TRM。我们使用接受者操作特征曲线下面积 (AUC) 来量化年龄和其他协变量对 2,238 名患者的子集 TRM 的相对影响。在这种方法中，AUC 为 1.0 表示完美预测，而 AUC 为 0.5 类似于抛硬币。结果无论年龄如何，治疗开始后 4 周后死亡风险就会下降，这表明在此期间死亡的患者构成了一个性质不同的群体。表现状态 (PS) 和年龄是 TRM 最重要的个体预测因素（AUC 分别为 0.75 和 0.65）。然而，多成分模型在预测 TRM（AUC 为 0.83）方面比单独的 PS 或年龄更准确。从此类多成分模型中消除年龄仅对其预测准确性产生很小的影响（AUC 为 0.82）。结论这些数据表明，年龄主要是其他协变量的替代变量，这些协变量本身显着增加了预测准确性，从而挑战了使用年龄作为 AML 中强化治疗意向治疗分配的主要或唯一依据的明智性。
PurposeOutcome in acute myeloid leukemia (AML) worsens with age, at least in part because of higher treatment-related mortality (TRM) in older patients. Eligibility for intensive AML treatment protocols is therefore typically based on age as the implied principal predictor of TRM, although other health-and disease-related factors modulate this age effect.Patients and MethodsWe empirically defined TRM using estimated weekly hazard rates in 3,365 adults of all ages administered intensive chemotherapy for newly diagnosed AML. We used the area under the receiver operator characteristic curve (AUC) to quantify the relative effects of age and other covariates on TRM in a subset of 2,238 patients. In this approach, an AUC of 1.0 denotes perfect prediction, whereas an AUC of 0.5 is analogous to a coin flip.ResultsRegardless of age, risk of death declined once 4 weeks had elapsed from treatment start, suggesting that patients who die during this time comprise a qualitatively distinct group. Performance status (PS) and age were the most important individual predictors of TRM (AUCs of 0.75 and 0.65, respectively). However, multicomponent models were significantly more accurate in predicting TRM (AUC of 0.83) than PS or age alone. Elimination of age from such multicomponent models only minimally affected their predictive accuracy (AUC of 0.82).ConclusionThese data suggest that age is primarily a surrogate for other covariates, which themselves add significantly to predictive accuracy, thus challenging the wisdom of using age as primary or sole basis for assignment of intensive, curative intent treatment in AML.