Prediction models for the survival in patients with intestinal-type gastric adenocarcinoma: a retrospective cohort study based on the SEER database.

OBJECTIVE

To explore the influencing factors of survival in intestinal-type gastric adenocarcinoma (IGA) and set up prediction model for the prediction of survival of patients diagnosed with IGA.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

A total of 2232 patients with IGA who came from the Surveillance, Epidemiology, and End Results database.

PRIMARY AND SECONDARY OUTCOME MEASURES

Patients' overall survival (OS) rate and cancer-specific survival (CSS) at the end of follow-up.

RESULTS

Of the total population, 25.72% survived, 54.93% died of IGA and 19.35% died of other causes. The median survival time of patients was 25 months. The result showed that age, race, stage group, T stage, N stage, M stage, grade, tumour size, radiotherapy, number of lymph nodes removed and gastrectomy were independent prognostic factors of OS risk for patients with IGA; age, race, race, stage group, T stage, N stage, M stage, grade, radiotherapy and gastrectomy were associated with CSS risk for patients with IGA. In view of these prognostic factors, we developed two prediction models for predicting the OS and CSS risk for patients with IGA separately. For the developed OS-related prediction model, the C-index was 0.750 (95% CI: 0.740 to 0.760) in the training set, corresponding to 0.753 (95% CI: 0.736 to 0.770) in the testing set. Likewise, for the developed CSS-related prediction model, the C-index was 0.781 (95% CI: 0.770 to 0.793) in the training set, corresponding to 0.785 (95% CI: 0.766 to 0.803) in the testing set. The calibration curves of the training set and testing set revealed a good agreement between model predictions in the 1-year, 3-year and 5-year survival for patients with IGA and actual observations.

CONCLUSION

Combining demographic and clinicopathological features, two prediction models were developed to predict the risk of OS and CSS in patients with IGA, respectively. Both models have good predictive performance.