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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association

October 21, 2026 · via Europe PMC

Beyond 30-day mortality: causes and risk factors of half-year postdischarge mortality after gastric cancer surgery.

Park K, Lee HJ, Yang HK, +6 more, Park DJ.

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Most deaths in the first 6 months after gastrectomy occur after discharge, so 30-day mortality badly underestimates risk.

What was studied

Retrospective study of 4802 gastrectomy patients at Seoul National University Hospital (2013-2019) with registry-linked follow-up of at least 5 years and Cox analysis of risk factors across time intervals.

Key findings

  • Six-month mortality was 1.2%, with 89.5% of deaths after discharge, versus 0.1% at 30 days and 0.5% at 90 days.
  • Early deaths related to emergency surgery and complications; mid-term deaths to psychiatric disorders and comorbidities.
  • Low BMI predicted death from 6 months; later older age, renal or hepatic disease, non-R0 resection and tumor stage dominated.

Clinical relevance

Highlights a window beyond 30 days when high-risk patients need surveillance and support.

Who should care

Surgical Gastroenterology

Limitations

Single high-volume center retrospective study, with few early deaths limiting precision and limited generalizability.

Extend postoperative monitoring and nutritional and psychiatric support for high-risk gastrectomy patients beyond 30 days.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Background

Conventional 30-day or in-hospital mortality underestimates the true burden of postoperative death after gastrectomy. This study aimed to characterize post-discharge mortality and its evolving determinants following gastric cancer surgery.

Methods

We retrospectively analyzed patients who underwent gastrectomy for primary gastric cancer at Seoul National University Hospital between 2013 and 2019. Mortality data were updated through national death registry linkage with complete follow-up of at least 5 years. In-hospital deaths were reviewed using medical records, while out-of-hospital deaths were classified using death certificates and family interviews. Cox regression analyses were performed across sequential postoperative intervals up to 5 years.

Results

Among 4802 patients included in the analysis, overall mortality within 6 months was 1.2% (n = 57), of which 89.5% occurred after discharge. Thirty-day and 90-day mortality were 0.1% and 0.5%, respectively, demonstrating substantial underestimation by conventional short-term endpoints. Early mortality was primarily associated with emergency surgery and postoperative complications. Mid-term mortality was strongly linked to psychiatric disorders and systemic comorbidities, while low body mass index emerged as a significant predictor from 6 months onward. With longer follow-up, older age, renal or hepatic disease, multiple comorbidities, and incomplete (non-R0) resection became dominant risk factors. Tumor stage showed a strong association with mortality at 3 years.

Conclusions

Postoperative mortality after gastrectomy is a dynamic process extending far beyond hospital discharge and shaped by distinct risk factors over time. Because most early deaths occur after discharge, improved survival requires surveillance strategies that target high-risk patients beyond the conventional 30-day window.

DOI

10.1007/s10120-026-01750-x

PMID

42905367

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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