Cancer-Specific Survival Analysis in Patients with Gastric Cancer: Based on Competing Risk Model [post]

Gaopei Zhu, Yuhang Zhu, Juan Li, Weijing Meng, Xiaoxuan Wang, Jianing Feng, Fuyan Shi, Enxue Tao, Suzhen Wang
2020 unpublished
BackgroundCompeting risk events are prone to cause bias in the estimation of all-cause mortality. In order to eliminate the impact of competing events on survival analysis, we constructed a competing risk model. Besides, we attempted to build nomograms to predict gastric cancer-specific mortality (GCSM) and other-cause mortality (OCM).MethodsThe competing risk model was constructed to evaluate all-cause mortality, GCSM and OCM, by using the gastric cancer data from 2004 to 2013 in the
more » ... ce, Epidemiology, and End Results Program (SEER) dataset. Nomograms were used to predict the risk of individual dying from gastric cancer and other causes based on competing risk model.ResultsA total of 15299 cases were screened out. The 1-year, 5-year, and 8-year survival probabilities were 48.9 %, 22.1 %, and 16.4 % for all-cause mortality, respectively. Univariate and multivariate analyses showed that sex, race, marital status, age at diagnosis, malignant, tumor diameter and TNM staging were all significant prognostic factors of gastric cancer. The GCSM and OCM models showed the risk of death treated by radiotherapy decreased from 0.689 to 0.494 after considering competing risk events. Furthermore, the nomograms showed good accuracy for GCSM prediction of the 1-,5-,8-year, the AUC values of the nomograms were 0.801 [95% CI, 0.793–0.808], 0.820 [95% CI, 0.810–0.829] and 0.823 [95% CI, 0.808–0.844]. The AUC values of the nomograms for predicting 1-, 5-, and 8-year OCM were 0.784 [95% CI, 0.778–0.792], 0.755 [95% CI, 0.748–0.765] and 0.747 [95% CI, 0.739–0.759].ConclusionsOverall, the prognosis of patients with Gastric cancer is poor. The competing risk model could accurately evaluate the probability of dying from gastric cancer and other causes. Nomograms showed relatively good performance and could be considered as convenient individualized predictive tools for predicting GCSM and OCM.
doi:10.21203/ fatcat:5jvlv2ea7vbt5gjyvdlfrtdnw4