Wang Jia-Bin, Zhong Qing, Wang Wei, Desiderio Jacopo, Chen Shi, Liu Zhi-Yu, Chen Qi-Yue, Li Ping, Xie Jian-Wei, Liu Feng-Qiong, Zheng Chao-Hui, Peng Jun-Sheng, Zhou Zhi-Wei, Parisi Amilcare, Huang Chang-Ming
Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
Division of Gastric Cancer, Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China.
J Surg Oncol. 2019 Sep;120(4):685-697. doi: 10.1002/jso.25637. Epub 2019 Jul 17.
How to best evaluate the disease-specific survival (DSS) of gastric cancer (GC) survivors over time is unclear.
Clinicopathological data from 22 265 patients who underwent curative intend resection for GC were retrospectively analyzed. Changes in the patients' 3-year conditional disease-specific survival (CS3) were analyzed. We used time-dependent Cox regression to analyze which variables had long-term effects on DSS and devised a dynamic predictive model based on the length of survival.
Based on 1-, 3-, and 5-year survivorships, the CS3 of the population increased gradually from 62% to 68.1%, 83.7%, and 90.6%, respectively. Subgroup analysis showed that the CS3 of patients who had poor prognostic factors initially demonstrated the greatest increase in postoperative survival time (eg, N3b: 26.6%-84.1%, Δ57.5% vs N0: 84.1%-93.3%, Δ9.2%). Time-dependent Cox regression analysis showed the following predictor variables constantly affecting DSS: age, the number of examined lymph nodes (LNs), T stage, N stage, and site (P < .05). These variables served as the basis for a dynamic prediction model.
The influence of prognostic factors on DSS and CS3 changed dramatically over time. We developed an effective model for predicting the DSS of patients with GC based on the length of survival time.
目前尚不清楚如何最好地评估胃癌(GC)幸存者随时间推移的疾病特异性生存率(DSS)。
回顾性分析22265例行根治性意向性切除的GC患者的临床病理资料。分析患者3年条件性疾病特异性生存率(CS3)的变化。我们使用时间依赖性Cox回归分析哪些变量对DSS有长期影响,并基于生存时长设计了一个动态预测模型。
基于1年、3年和5年生存率,总体人群的CS3分别从62%逐渐增至68.1%、83.7%和90.6%。亚组分析显示,最初具有不良预后因素的患者的CS3术后生存时间增加幅度最大(例如,N3b:26.6%-84.1%,Δ57.5%;而N0:84.1%-93.3%,Δ9.2%)。时间依赖性Cox回归分析显示以下预测变量持续影响DSS:年龄、检查的淋巴结(LN)数量、T分期、N分期和部位(P<0.05)。这些变量作为动态预测模型的基础。
预后因素对DSS和CS3的影响随时间显著变化。我们基于生存时长建立了一个有效的预测GC患者DSS的模型。