Development and validation of machine learning predictive models for gastric volume based on ultrasonography: A multicentre study

Abstract

Study objective

Aspiration of gastric contents is a serious complication associated with anaesthesia. Accurate prediction of gastric volume may assist in risk stratification and help prevent aspiration. This study aimed to develop and validate machine learning models to predict gastric volume based on ultrasound and clinical features.

Methods

This cross-sectional multicentre study was conducted at two hospitals and included adult patients undergoing gastroscopy under intravenous anaesthesia. Patients from Centre 1 were prospectively enrolled and randomly divided into a training set (Cohort A, n = 415) and an internal validation set (Cohort B, n = 179), while patients from Centre 2 were used as an external validation set (Cohort C, n = 199). The primary outcome was gastric volume, which was measured by endoscopic aspiration immediately following ultrasonographic examination. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and eight machine learning models were developed and evaluated using Bland-Altman analysis. The models' ability to predict medium-to-high and high gastric volumes was assessed. The top-performing models were externally validated, and their predictive performance was compared with the traditional Perlas model.

Main results

Among the 793 enrolled patients, the number and proportion of patients with high gastric volume were as follows: 23 (5.5 %) in the development cohort, 10 (5.6 %) in the internal validation cohort, and 3 (1.5 %) in the external validation cohort. Eight models were developed using age, cross-sectional area of gastric antrum in right lateral decubitus (RLD-CSA) position, and Perlas grade, with these variables selected through LASSO regression. In internal validation, Bland-Altman analysis showed that the Perlas model overestimated gastric volume (mean bias 23.5 mL), while the new models provided accurate estimates (mean bias −0.1 to 2.0 mL). The models significantly improved prediction of medium-high gastric volume (area under the curve [AUC]: 0.74–0.77 vs. 0.63) and high gastric volume (AUC: 0.85–0.94 vs. 0.74). The best-performing adaptive boosting and linear regression models underwent externally validation, with AUCs of 0.81 (95 % confidence interval [CI], 0.74–0.89) and 0.80 (95 %CI, 0.72–0.89) for medium-high and 0.96 (95 %CI, 0.91–1) and 0.96 (95 %CI, 0.89–1) for high gastric volume.

Conclusions

We propose a novel machine learning-based predictive model that outperforms Perlas model by incorporating the key features of age, RLD-CSA, and Perlas grade, enabling accurate prediction of gastric volume.