Prognostic value of health-related quality-of-life data in predicting survival in patients with anaplastic oligodendrogliomas, from a phase III EORTC brain cancer group study
Prognostic value of health-related quality-of-life data in predicting survival in patients with anaplastic oligodendrogliomas, from a phase III EORTC brain cancer group study
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DOI：
10.1200/jco.2007.11.1476
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发表时间：
2007-12-20
影响因子：
45.3
通讯作者：
van den Bent, Martin J.
中科院分区：
文献类型：
--
作者：
Mauer, Murielle E. L.;Taphoorn, Martin J. B.;van den Bent, Martin J.
目的探讨基线症状和健康相关生活质量(HRQOL)在预测脑癌患者生存中的价值。方法采用COX比例风险回归模型，对247例间变性少突胶质细胞瘤患者进行基线HRQOL评分(来自欧洲癌症研究与治疗组织(EORTC)生活质量问卷C30和EORTC脑肿瘤模块)，以确定其与总体生存的关系。结果经典分析控制了主要的临床预后因素，选择了情绪功能(P=.0016)、沟通障碍(P=.0261)、未来的不确定性(P=.0481)和腿部无力(P=.0001)作为有统计学意义的预后因素。然而，有几个问题质疑这些发现的有效性，没有发现一个模型比所有其他模型更可取。C指数估计模型正确预测随机选择的一对患者中哪位患者存活时间更长的概率，R-2系数衡量模型解释的变异性比例，当将选定的或全部HRQOL评分添加到临床因素中时，没有表现出明显的改善。结论虽然经典技术导致肯定的结果，但更精细的分析表明，基线HRQOL评分对预测生存的临床因素增加的相对较少。这些结果可能对未来使用HRQOL作为癌症患者的预后因素有一定的指导意义。
Purpose This is one of a few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in patients with brain cancer.Patients and Methods Baseline HRQOL scores ( from the European Organisation for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire C30 and the EORTC Brain Cancer Module) were examined in 247 patients with anaplastic oligodendrogliomas to determine the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap resampling procedure and the computation of C indexes and R-2 coefficients were used to explore the stability of the models as well as better assess the potential benefit of using HRQOL to predict survival in clinical practice and research.Results Classical analysis controlled for major clinical prognostic factors selected emotional functioning (P=.0016), communication deficit (P=.0261), future uncertainty (P=.0481), and weakness of legs (P=.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings and no single model was found to be preferable over all others. C indexes, which estimate the probability of a model to correctly predict which patient among a randomly chosen pair of patients will survive longer, and R-2 coefficients, which measure the proportion of variability explained by the model, did not exhibit major improvement when adding selected or all HRQOL scores to clinical factors.Conclusion While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor for patients with cancer.