文章摘要
韩磊,崔平,唐明霜,张敏,崔慧杰,曾子倩,陈思宇,刘姗姗,宋斌,谷冬晴,王新,张本.胆道系统肿瘤患者生存预测模型的构建及验证研究[J].中华流行病学杂志,2019,40(11):1461-1469
胆道系统肿瘤患者生存预测模型的构建及验证研究
Prediction model for survival in patients with biliary tract cancer: a development and validation study
收稿日期:2019-05-06  出版日期:2019-11-26
DOI:10.3760/cma.j.issn.0254-6450.2019.11.022
中文关键词: 胆道系统肿瘤;队列研究;生存率;预后因素;预测模型
英文关键词: Biliary tract cancer;Cohort study;Survival rate;Prognostic factor;Prediction model
基金项目:国家自然科学基金(81673255,81874283,81903393,81903398);国家"千人计划"青年项目;重庆市自然科学基金面上项目(cstc2019jcyj-msxmX0466);陆军军医大学临床医学科研人才培养计划(2018XLC1004);陆军军医大学拔尖人才培养计划基金(SWH2018BJKJ-12);陆军军医大学第一附属医院重大领域技术创新计划(SWH2016ZDCX1012)
作者单位E-mail
韩磊 陆军军医大学第一附属医院医务处, 重庆 400038  
崔平 济宁医学院公共卫生学院 272067  
唐明霜 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
张敏 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
崔慧杰 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
曾子倩 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
陈思宇 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
刘姗姗 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
宋斌 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
谷冬晴 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
王新 陆军军医大学第一附属医院流行病学中心, 重庆 400038  
张本 陆军军医大学第一附属医院流行病学中心, 重庆 400038 benzhang@vip.163.com 
摘要点击次数: 1884
全文下载次数: 762
中文摘要:
      目的 调查胆道系统肿瘤患者的生存率及其影响因素,并构建预后风险预测模型对生存概率进行预测。方法 选取美国监测、流行病学和结果项目(SEER)收集的2010-2015年诊断的14 005例胆道系统肿瘤(包括胆囊癌、肝外胆管癌和壶腹癌)患者作为观察人群。运用多因素Cox回归模型调查胆道系统肿瘤预后因素,并构建预测列线图对其1、3和5年总生存率进行预测并进行模型预测区分能力和标定能力评价。同时,选取SEER 2004-2009年诊断的11 953例患者作为验证队列对模型的外部预测准确性进行验证。结果 胆道系统肿瘤患者1、3和5年总生存率分别为41.9%、20.4%和15.3%。年龄>50周岁、黑人/印第安/阿拉斯加人种、较高的TNM分期及较差组织学分化程度是患者死亡的危险因素,而已婚状态、亚裔和太平洋岛人种、有医疗保险和原发部位手术是保护因素,性别与预后无关。预测模型C统计量为0.73(95%CI:0.72~0.74),标准曲线显示胆道系统肿瘤患者1、3和5年预测生存概率和实际生存概率交互曲线均与45°对角线接近。验证队列的结果与建模队列相似,预测模型C统计量0.70(95%CI:0.69~0.72),提示该预测模型具有较高的外部适用性。胆囊癌、肝外胆管癌和壶腹癌与总体胆道系统肿瘤结果一致。结论 胆道系统肿瘤患者生存率相对较差,基于预后因素构建的生存预测模型具有较高的预测准确性。未来可将该预测模型应用于临床,指导患者的个体化治疗。
英文摘要:
      Objective The aim of the present study was to investigate the survival rate and its prognostic factors for patients with biliary tract cancer, and then a prognostic risk prediction model was constructed to predict the survival probability of patients. Methods A total of 14 005 patients with biliary tract cancer (including gallbladder cancer, extrahepatic bile duct cancer, and ampulla of Vater cancer), who were diagnosed between 2010 and 2015 in the US National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) were included in the development cohort. The prognostic risk factors of biliary tract cancer were investigated using multivariate Cox regression models. The predictive nomograms were then constructed to predict the overall survival probability of 1, 3, and 5 years, and the predictive discrimination and calibration ability of the nomograms were further evaluated. Meanwhile, 11 953 patients who were diagnosed during 2004 to 2009 from SEER Program were then selected to validate the external predictive accuracy of the prediction models. Results The 1, 3 and 5-year cumulative survival rates of patients with biliary tract cancer were 41.9%, 20.4% and 15.3%, respectively, in the development cohort. Age greater than 50 years, African Americans and Native Americans and Alaska Natives, higher T, N and M stage and poor histological differentiation grade were risk factors for death, while married status, Asia-Pacific Islanders, insured status and surgery on primary site were protective factors. Gender was not significantly associated with the overall survival. The C statistic of the prediction model was 0.73 (95%CI:0.72-0.74), and the calibration curve showed that the interaction curves of predictive and actual survival rates of 1, 3 and 5 years were close to the 45 degree diagonal. Results in the validation cohort were similar with those in the construction cohort, with a C statistic of 0.70 (95%CI:0.69-0.72), indicating high external applicability of the prediction model. Findings from gallbladder cancer, extrahepatic bile duct cancer, and ampulla of Vater cancer are in consistent with the overall biliary tract cancer. Conclusions The survival rate of patients with biliary tract cancer is relatively poor, and the survival prediction model based on prognostic factors has high prediction accuracy. In the future, this prognostic prediction model could be applied to clinical practice to guide individualized treatment for patients with biliary tract cancer.
查看全文   Html全文     查看/发表评论  下载PDF阅读器
关闭