A Visual “STAGED” Nomogram to Predict the Risk of Full Stomach in Adult Outpatients Undergoing Elective Sedated Upper Endoscopy
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: A full stomach during elective sedated upper endoscopy (UE) is highly associated with aspiration. Aspiration is related to increased morbidity and mortality. We aimed to develop a nomogram model to predict the risk of having a full stomach in adult outpatients undergoing elective sedated UE. Methods: Data of 1171 adult outpatients undergoing elective sedated UE were collected retrospectively between July 2021 and February 2022. Univariable analysis, multivariable logistic regression, and Boruta were used to identify independent risk factors associated with full stomach. Using the identified risk factors, a nomogram was developed. The area under the receiver operating characteristic (AUROC), decision curve analysis (DCA), and calibration curve (CC) were used to evaluate the model’s performance. Results: After exclusion, 990 patients were enrolled in this study. In the training set, 305 patients (38.5%) had a full stomach, and 71 patients (35.9%) had a full stomach for the testing set. The AUROC (95% Cl) was 0.819 (0.788-0.85) and 0.823(0.759-0.887) for the training and testing set, respectively. The variables incorporated in the development of our STAGED model were Sex = female (OR: 2.147), fasting Time (OR: 0.839), Age (OR: 0.971), GERD (OR: 15.61), diabEtes (OR: 8.614) and Diet (OR: 3.691). DCA and CIC analyses showed that our model had a significant net benefit compared to the "treat all or "treat none" strategies and had high predictive value, making it clinically useful. Conclusion: The “STAGED” nomogram model was successfully developed and made easily accessible online via a visual web-based calculator. Summary of the patient’s inclusion along with the study design, the methodology used for model development, the implementation of the model, and model performance.
Authors: Bongani Mbambara, Asha Khatib Iddi, Chernor Sulaiman Bah, Yuzhan Jin, Ndagijimana Djuma, Xinlong Zhang, Rui Ding, Zhixiang Li, Yuqing Yan, Jianjun Zou, Yanna Si
Language: English
Subfield: Gastroenterology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Awards: G2399677825, G7368289860
OpenAlex ID: W4411113773
