Anesthesiology · 2026
Multiplanar Ultrasonographic Assessment of Gastric Volume: A Prospective Observational Study.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Liu Hao, He Yuewen, Ding Longfei, Wu Tong, Zhang Yuhao, Chen Lu, Wang Yong, Ma Wuhua
Аннотация
BACKGROUND: Preoperative gastric ultrasonography serves as an objective tool for evaluating the risk of pulmonary aspiration. However, measurements obtained from gastric ultrasonography at the abdominal aorta plane and the inferior vena cava (IVC) plane may vary substantially. This study aims to identify the measurement plane that most closely reflects the actual gastric volume, thereby informing clinical practice.
METHODS: All participants initially underwent gastric ultrasonography at both the abdominal aorta and IVC planes after a fasting period, representing the low gastric volume state. Subsequently, they consumed apple juice at a volume of 2.3 ml/kg, after which gastric ultrasonography was repeated to represent the high gastric volume state. The predicted ingested volume was calculated as the predicted gastric volume after ingestion minus the baseline predicted gastric volume. Comparisons were made between abdominal aorta plane and IVC plane measurements in both low gastric volume and high gastric volume. The agreement between the predicted ingested volume and the actual ingested volume was assessed for the abdominal aorta plane, IVC plane, higher-measured gastric volume plane, and lower-measured gastric volume plane, along with their detection rates for high aspiration risk.
RESULTS: Ultimately, 196 volunteers were included in the final statistical analysis. The results showed that in both low and high gastric volume states, gastric ultrasonography measurements at the abdominal aorta plane and the IVC plane differed significantly ( P < 0.001). Among all measurement planes, only the higher-measured gastric volume plane showed no statistically significant difference between the predicted and actual gastric volume, with the smallest bias (bias = -4.27 ml, P = 0.076).
CONCLUSIONS: Notable differences exist between gastric ultrasonography measurement planes. When applying existing predictive models, measurements obtained from the higher-measured gastric volume plane provide greater accuracy. Differentiating between measurement planes during model development may enhance the predictive accuracy of such models.
METHODS: All participants initially underwent gastric ultrasonography at both the abdominal aorta and IVC planes after a fasting period, representing the low gastric volume state. Subsequently, they consumed apple juice at a volume of 2.3 ml/kg, after which gastric ultrasonography was repeated to represent the high gastric volume state. The predicted ingested volume was calculated as the predicted gastric volume after ingestion minus the baseline predicted gastric volume. Comparisons were made between abdominal aorta plane and IVC plane measurements in both low gastric volume and high gastric volume. The agreement between the predicted ingested volume and the actual ingested volume was assessed for the abdominal aorta plane, IVC plane, higher-measured gastric volume plane, and lower-measured gastric volume plane, along with their detection rates for high aspiration risk.
RESULTS: Ultimately, 196 volunteers were included in the final statistical analysis. The results showed that in both low and high gastric volume states, gastric ultrasonography measurements at the abdominal aorta plane and the IVC plane differed significantly ( P < 0.001). Among all measurement planes, only the higher-measured gastric volume plane showed no statistically significant difference between the predicted and actual gastric volume, with the smallest bias (bias = -4.27 ml, P = 0.076).
CONCLUSIONS: Notable differences exist between gastric ultrasonography measurement planes. When applying existing predictive models, measurements obtained from the higher-measured gastric volume plane provide greater accuracy. Differentiating between measurement planes during model development may enhance the predictive accuracy of such models.