The Impact of Perioperative Changes in Resting Energy Expenditure on Clavien-Dindo Grade II and Above Complications in Gastric Cancer Patients

Objectives: This study evaluates the predictive efficacy of combining resting energy expenditure (REE) with the Prognostic Nutritional Index (PNI) for severe postoperative complications in gastric cancer.While PNI is widely used, it does not fully account for metabolic status.Since REE reflects dynamic metabolism, integrating it with PNI may enhance prediction accuracy, providing evidence for personalized postoperative management.Methods: This prospective single-center study included 153 gastric cancer patients undergoing radical surgery at Jiangsu Province People's Hospital (January-November 2024).Patients (113 males, 40 females) underwent laparoscopic (114), robotic ( 38), or open (1) surgery, with total (63), proximal (24), and distal (66) gastrectomies.Inclusion criteria required patients aged !18 years, diagnosed with gastric cancer, and eligible for surgery and REE measurement.Collected data included weight, height, serum albumin, and lymphocyte count.PNI was calculated using serum albumin and lymphocyte count.Postoperative complications were graded via Clavien-Dindo (22 Grade II, 3 Grade III, and 1 Grade IV cases).REE was measured using indirect calorimetry (CCM metabolic cart, MedGraphics, USA).Logistic regression assessed the predictive role of REE, PNI, and other factors, with ROC curve analysis evaluating model efficacy.Results: REE, lymphocyte count, and serum albumin were independent predictors of Grade III+ complications.Compared to PNI alone (AUC0.633),integrating preoperative and postoperative Day 1 REE differences improved predictive accuracy (AUC0.786,P<0.05).This enhancement was particularly significant in elderly or malnourished patients.Conclusions: Combining REE with PNI significantly improves severe postoperative complication prediction in gastric cancer, especially in high-risk patients with increased metabolic burden.Future studies should expand sample sizes to validate this model across diverse populations and explore its application in perioperative nutritional support and complication management.