Analysis of Prognostic Factors and Design of Prognosis Model for Patients with Stage IV Gastric Cancer Following First-Line Palliative Chemotherapy

BACKGROUND: This study was to investigate the prognostic factors of patients with advanced gastric cancer and described a sample model to better differentiate the patients who could better benefit from palliative chemotherapy. PATIENTS AND METHODS: In this retrospective study, 112 gastric cancer patients at stage IV following first-line chemotherapy were enrolled from July 2013 to September 2019. The clinical factors including age, sex, ECOG, pathologic types, metastatic sites, blood indexes, response of first-line chemotherapy, and survival were collected. The treatment responses were evaluated using the response evaluation criteria in solid tumors (RECIST). The survival curves were drawn by the Kaplan–Meier method, and the independent prognostic factors of overall survival (OS) were analyzed by Cox proportional hazards regression model. RESULTS: In this study, the median overall survival (mOS) of gastric cancer patients was 10.5 months, the disease remission rate (PR) was 21.4%, and the disease control rate (DCR) was 86.6%. Multivariate analysis identified 5 independent prognostic factors: peritoneal metastasis [P = 0.002; hazard risk (HR), 2.394; 95% CI 1.394–4.113], hemoglobin <90g/L [P = 0.001; hazard risk (HR), 2.674; 95% CI 1.536–4.655], LDH ≥225 U/L [P = 0.033; hazard risk (HR), 1.818; 95% CI 1.409–3.150], and 3 times higher level of CEA [P = 0.006; hazard risk (HR), 2.123; 95% CI 1.238–3.640] along with CA199 [P = 0.005; hazard risk (HR), 2.544; 95% CI 1.332–4.856] than upper limit of normal. Based on the obtained data, a prognostic index was constructed, dividing the patients into three risk groups: low (n = 67), intermediate (n = 35), and high-risk group (n = 10). The mOS for low, intermediate, and high-risk groups was 13.9 months (95% CI 10.7–17.1), 8.1 months (95% CI 5.7–10.4), and 3.9 months (95% CI 2.6–5.3), respectively, whereas the 1-year survival rate was 56.4%, 20.0%, and 0.0%, respectively (P < 0.001). CONCLUSION: This model should facilitate the prediction of treatment outcomes and then individualized treatment of advanced gastric cancer patients.