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Background <p>Gastric surgery is a critical intervention for conditions, such as gastric cancer, obesity, and peptic ulcer disease. Despite advances in surgical techniques and perioperative care, postoperative complications, including elevated mortality, remain a major concern. Glycemic variability (GV), calculated as the coefficient of variation of blood glucose levels during the ICU stay, has emerged as a potential predictor of adverse outcomes in critically ill patients. This study aimed to investigate the association between GV and postoperative mortality in patients undergoing gastric surgery.</p> Methods <p>Data were obtained from the MIMIC-IV database, which contains anonymized health records of ICU patients admitted to Beth Israel Deaconess Medical Center. The cohort included adult patients admitted to the ICU following gastric surgery. GV was assessed using the coefficient of variation of all recorded blood glucose measurements during the ICU stay. The primary outcome was 30-day all-cause in-hospital mortality; the secondary outcome was 90-day mortality. Associations between GV and outcomes were analyzed using Cox proportional hazards models and Kaplan–Meier survival analysis. In addition, machine learning models were developed to evaluate the predictive value of GV.</p> Results <p>A total of 1099 patients were included. Higher GV was significantly associated with increased 30-day and 90-day mortality (HR 1.15, 95% CI 1.09–1.21; and HR 1.14, 95% CI 1.09–1.20, respectively). Threshold analysis identified inflection points at GV = 20.24 for 30-day mortality and GV = 33.96 for 90-day mortality. The stacking ensemble model incorporating GV achieved strong predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.83.</p> Conclusions <p>GV is a significant and independent predictor of postoperative mortality in gastric surgery patients. The observed threshold effects suggest that maintaining GV below critical levels may improve early outcomes. These findings highlight the prognostic value of GV and support its potential as a target for postoperative management. Further prospective, multicenter studies are warranted to validate these results and guide clinical practice.</p>

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Prognostic significance of postoperative glycemic variability after gastric surgery: a retrospective cohort study and development of a mortality prediction model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Yuanshuo Ge,
  • Guangdong Wang,
  • Yun Huang,
  • Beilin Luo,
  • Yaxin Zhang

摘要

Background

Gastric surgery is a critical intervention for conditions, such as gastric cancer, obesity, and peptic ulcer disease. Despite advances in surgical techniques and perioperative care, postoperative complications, including elevated mortality, remain a major concern. Glycemic variability (GV), calculated as the coefficient of variation of blood glucose levels during the ICU stay, has emerged as a potential predictor of adverse outcomes in critically ill patients. This study aimed to investigate the association between GV and postoperative mortality in patients undergoing gastric surgery.

Methods

Data were obtained from the MIMIC-IV database, which contains anonymized health records of ICU patients admitted to Beth Israel Deaconess Medical Center. The cohort included adult patients admitted to the ICU following gastric surgery. GV was assessed using the coefficient of variation of all recorded blood glucose measurements during the ICU stay. The primary outcome was 30-day all-cause in-hospital mortality; the secondary outcome was 90-day mortality. Associations between GV and outcomes were analyzed using Cox proportional hazards models and Kaplan–Meier survival analysis. In addition, machine learning models were developed to evaluate the predictive value of GV.

Results

A total of 1099 patients were included. Higher GV was significantly associated with increased 30-day and 90-day mortality (HR 1.15, 95% CI 1.09–1.21; and HR 1.14, 95% CI 1.09–1.20, respectively). Threshold analysis identified inflection points at GV = 20.24 for 30-day mortality and GV = 33.96 for 90-day mortality. The stacking ensemble model incorporating GV achieved strong predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.83.

Conclusions

GV is a significant and independent predictor of postoperative mortality in gastric surgery patients. The observed threshold effects suggest that maintaining GV below critical levels may improve early outcomes. These findings highlight the prognostic value of GV and support its potential as a target for postoperative management. Further prospective, multicenter studies are warranted to validate these results and guide clinical practice.