A preoperative scoring model to predict intraoperative high end-tidal carbon dioxide in endoscopic resection of gastric submucosal tumors

Intraoperative high end-tidal carbon dioxide (ETCO₂) during endoscopic resection (ER) of gastric submucosal tumors (SMTs) may cause respiratory compromise. Preoperative identification of risk factors and development of a scoring model to predict intraoperative high ETCO₂ may aid in preoperative planning. A total of 1,368 patients were retrospectively enrolled and divided into a training cohort (TC, n = 690), an internal validation cohort (IVC, n = 296), and an external validation cohort (EVC, n = 382). Preoperative variables were compared between the high ETCO₂ and normal groups in the TC. Multivariate logistic regression was performed to identify independent risk factors for intraoperative high ETCO₂ and to construct a simplified scoring system. The model’s performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy in the TC, IVC, and EVC. Intraoperative high ETCO₂ was documented in 341 (24.9%) patients. Multivariate analysis identified age ≥ 65 years (1 point), BMI ≥ 24.0 kg/m² (1 point), tumor size ≥ 3.0 cm (1 point), and invasion depth beyond muscularis propria (2 points) as independent predictors, which were incorporated to build a weighted scoring model. The model showed good discrimination, with AUCs of 0.832 (IVC) and 0.807 (EVC) and high NPVs of 90.0% and 88.7%. The incidence of intraoperative high ETCO₂ increased significantly with risk stratification: low-risk (score 0–1), intermediate-risk (score 2–3), and high-risk (score 4–5) groups exhibited rates of 11.0%, 52.7%, 85.7% (TC); 10.0%, 42.1%, 89.5% (IVC); and 11.3%, 39.3%, 85.7% (EVC), respectively. This preoperative scoring model based on four readily available clinical variables shows good discrimination for predicting intraoperative high ETCO₂. It may help raise preoperative awareness and guide the intensity of intraoperative monitoring. However, prospective validation is required before clinical implementation.