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Background <p>Postoperative complications remain a major challenge in elderly gastric cancer patients undergoing radical gastrectomy. Sleep disturbance is frequent, but its prognostic significance has not been established.</p> Methods <p>We retrospectively analyzed 485 patients aged ≥ 60&#xa0;years who underwent radical gastrectomy between June 2020 and June 2023. Sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI) one day before surgery and at postoperative day 30. Nutritional risk was assessed by the Geriatric Nutritional Risk Index (GNRI), and sarcopenia was defined by the Asian Working Group for Sarcopenia 2019 criteria. Thirty-day postoperative complications were classified by Clavien–Dindo grade. Multivariable proportional-odds and binary logistic regression were applied. Model performance was evaluated using c-index/AUC, Brier score, and calibration after 1000-bootstrap validation.</p> Results <p>Among 485 patients (median age 67&#xa0;years; 61.2% male), 160 (33.0%) developed complications, including 21 (4.3%) grade III–V events. Sarcopenia and nutritional risk were present in 30.9% and 27.2%, respectively. Higher preoperative PSQI independently predicted greater complication severity (per-point OR 1.36, 95% CI 1.22–1.52, <i>P</i> &lt; 0.001). Additional predictors included total gastrectomy, open approach, low GNRI, sarcopenia, ASA ≥ III, Charlson index ≥ 3, and stage III disease. Postoperative PSQI also correlated with complication severity (OR 1.41, 95% CI 1.26–1.58), but this was considered exploratory due to temporal overlap. Predictive models showed good discrimination (AUC 0.74–0.79) and calibration after validation.</p> Conclusions <p>Preoperative sleep quality independently predicts postoperative complications after radical gastrectomy. Integrating sleep, nutritional, and sarcopenia assessments improves risk stratification and highlights targets for perioperative optimization.</p>

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Preoperative sleep quality predicts postoperative morbidity in elderly gastric cancer patients undergoing radical gastrectomy: integration with nutritional and sarcopenia assessment

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Shengjie Pan,
  • Gang Wang

摘要

Background

Postoperative complications remain a major challenge in elderly gastric cancer patients undergoing radical gastrectomy. Sleep disturbance is frequent, but its prognostic significance has not been established.

Methods

We retrospectively analyzed 485 patients aged ≥ 60 years who underwent radical gastrectomy between June 2020 and June 2023. Sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI) one day before surgery and at postoperative day 30. Nutritional risk was assessed by the Geriatric Nutritional Risk Index (GNRI), and sarcopenia was defined by the Asian Working Group for Sarcopenia 2019 criteria. Thirty-day postoperative complications were classified by Clavien–Dindo grade. Multivariable proportional-odds and binary logistic regression were applied. Model performance was evaluated using c-index/AUC, Brier score, and calibration after 1000-bootstrap validation.

Results

Among 485 patients (median age 67 years; 61.2% male), 160 (33.0%) developed complications, including 21 (4.3%) grade III–V events. Sarcopenia and nutritional risk were present in 30.9% and 27.2%, respectively. Higher preoperative PSQI independently predicted greater complication severity (per-point OR 1.36, 95% CI 1.22–1.52, P < 0.001). Additional predictors included total gastrectomy, open approach, low GNRI, sarcopenia, ASA ≥ III, Charlson index ≥ 3, and stage III disease. Postoperative PSQI also correlated with complication severity (OR 1.41, 95% CI 1.26–1.58), but this was considered exploratory due to temporal overlap. Predictive models showed good discrimination (AUC 0.74–0.79) and calibration after validation.

Conclusions

Preoperative sleep quality independently predicts postoperative complications after radical gastrectomy. Integrating sleep, nutritional, and sarcopenia assessments improves risk stratification and highlights targets for perioperative optimization.