Chen Cunte, Wang Peipei, Wang Caixia
Department of Hematology.
Department of Oncology, Guangzhou First People's Hospital, Guangzhou Medical University and Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
Medicine (Baltimore). 2019 May;98(21):e15804. doi: 10.1097/MD.0000000000015804.
Acute myeloid leukemia (AML) is hematopoietic malignancy. This study was designed to develop an individualized prognostic nomogram to predict cancer-specific survival (CSS) and overall survival (OS) of AML.The clinical data of AML patients (n = 58,882) diagnosed from 1973 to 2014 were obtained from the Surveillance, Epidemiology, and End Results database. The patients were divided into training cohort (n = 29,441) and validation cohort (n = 29,441). The prognostic nomograms were designed with clinical variables selected by multivariate Cox regression model in training cohort. The concordance index (C-index), calibration curve, and receiver operating characteristic curve were used to assess the performance of the nomograms.The predictors in nomogram for CSS were AML subtypes, age, sex, region, marital status, and chemotherapy, whereas the predictors for OS were AML subtypes, age, sex, region, race, marital status, and chemotherapy. The C-indexes of the nomograms in internal validation for CSS and OS were 0.712 and 0.703, respectively, whereas the C-indexes in external validation for CSS and OS were 0.712 and 0.705, respectively. The area under the curve of receiver operating characteristic curves for CSS and OS were 0.799 (95% confidence interval: 0.792-0.806) and 0.809 (95% confidence interval: 0.803-0.816), respectively.The individualized prognostic nomogram could perform relatively accurate prediction of outcome in adult patients with AML.
急性髓系白血病(AML)是一种造血系统恶性肿瘤。本研究旨在开发一种个体化预后列线图,以预测AML患者的癌症特异性生存(CSS)和总生存(OS)。从监测、流行病学和最终结果数据库中获取了1973年至2014年诊断的AML患者(n = 58882)的临床数据。患者被分为训练队列(n = 29441)和验证队列(n = 29441)。在训练队列中,采用多变量Cox回归模型选择临床变量来设计预后列线图。使用一致性指数(C指数)、校准曲线和受试者工作特征曲线来评估列线图的性能。CSS列线图的预测因素为AML亚型、年龄、性别、地区、婚姻状况和化疗,而OS的预测因素为AML亚型、年龄、性别、地区、种族、婚姻状况和化疗。CSS和OS内部验证中列线图的C指数分别为0.712和0.703,而CSS和OS外部验证中的C指数分别为0.712和0.705。CSS和OS的受试者工作特征曲线下面积分别为0.799(95%置信区间:0.792 - 0.806)和0.809(95%置信区间:0.803 - 0.816)。个体化预后列线图能够对成年AML患者的预后进行相对准确的预测。