Prediction model of 3-year survival after endoscopic submucosal dissection for early gastric cancer in elderly patients aged ≥ 85 years: EGC-2 model

Tohoku GI Endoscopy Group

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Purpose: Little is known about the prognostic factors for survival after endoscopic submucosal dissection (ESD) in elderly patients with early gastric cancer (EGC). The aim of this study is to determine prognostic factors and a prediction model of 3-year survival after ESD for EGC in patients aged ≥ 85 years. Methods: We retrospectively evaluated the clinical outcomes of 740 patients with EGC aged ≥ 85 years, who were treated by ESD at 30 institutions in Japan. Overall survival (OS) and disease-specific survival (DSS) were calculated with the Kaplan–Meier method. Prediction models for 3-year OS after ESD were estimated using the Cox proportional hazards model based on Uno’s C-statistics. Results: During the follow-up period, 309 patients died of any cause and 10 patients died of gastric cancer. OS and DSS after 3 years were 82.7% and 99.2%, respectively. No significant differences in OS were found among curability categories. The Cox proportional hazards model revealed the geriatric nutritional risk index (GNRI) and the Charlson comorbidity index (CCI) to be predictors of 3-year survival. We established a final model (EGC-2 model) expressed by GNRI − (2.2×CCI) with a cutoff value of 96. The overall survival rate was significantly lower in the model value < 96 group than in the model value ≥ 96 group (P < 0.001). Conclusions: The prediction model using GNRI and CCI will be useful to support decision-making for the treatment of EGC in elderly patients aged ≥ 85 years.

Original languageEnglish
JournalJournal of Cancer Research and Clinical Oncology
DOIs
Publication statusAccepted/In press - 2022
Externally publishedYes

Keywords

  • Elderly patients
  • Endoscopic submucosal dissection
  • Gastric cancer
  • Prognostic factors

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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