Abstract：
UNASSIGNED: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML) model to estimate gastric fluid volume more accurately.
UNASSIGNED: We retrospectively analyzed the clinical data and POCUS data (D1: craniocaudal diameter, D2: anteroposterior diameter) of 1386 patients undergoing elective sedated gastrointestinal endoscopy (GIE) at Nanjing First Hospital to predict gastric fluid volume using ML techniques, including six different ML models and a stacking model. We evaluated the models using the adjusted Coefficient of Determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the importance of the variables. Finally, a web calculator was constructed to facilitate its clinical application.
UNASSIGNED: The stacking model (Linear regression + Multilayer perceptron) performed best, with the highest adjusted R2 of 0.718 (0.632 to 0.804). The mean prediction bias was 4 ml (MAE: 4.008 (3.68 to 4.336)), which is better than that of the linear model. D1 and D2 ranked high in the SHAP plot and performed better in the right lateral decubitus (RLD) than in the supine position. The web calculator can be accessed at https://cheason.shinyapps.io/Stacking_regressor/.
UNASSIGNED: The stacking model and its web calculator can serve as practical tools for accurately estimating gastric fluid volume in patients undergoing elective sedated GIE. It is recommended that anesthesiologists measure D1 and D2 in the patient\'s RLD position.
UNASSIGNED: We retrospectively analyzed the clinical data and POCUS data (D1: craniocaudal diameter, D2: anteroposterior diameter) of 1386 patients undergoing elective sedated gastrointestinal endoscopy (GIE) at Nanjing First Hospital to predict gastric fluid volume using ML techniques, including six different ML models and a stacking model. We evaluated the models using the adjusted Coefficient of Determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the importance of the variables. Finally, a web calculator was constructed to facilitate its clinical application.
UNASSIGNED: The stacking model (Linear regression + Multilayer perceptron) performed best, with the highest adjusted R2 of 0.718 (0.632 to 0.804). The mean prediction bias was 4 ml (MAE: 4.008 (3.68 to 4.336)), which is better than that of the linear model. D1 and D2 ranked high in the SHAP plot and performed better in the right lateral decubitus (RLD) than in the supine position. The web calculator can be accessed at https://cheason.shinyapps.io/Stacking_regressor/.
UNASSIGNED: The stacking model and its web calculator can serve as practical tools for accurately estimating gastric fluid volume in patients undergoing elective sedated GIE. It is recommended that anesthesiologists measure D1 and D2 in the patient\'s RLD position.
摘要：
■当前对胃液体积的护理点超声（POCUS）评估主要依靠传统的线性方法，这通常会受到中等精度的影响。这项研究旨在开发一种先进的机器学习（ML）模型，以更准确地估计胃液量。
■我们回顾性分析了临床数据和POCUS数据（D1：头尾直径，D2：前后径）在南京第一医院接受择期镇静胃肠内窥镜检查（GIE）的1386例患者使用ML技术预测胃液量，包括六个不同的ML模型和一个堆叠模型。我们使用调整后的确定系数(R2)评估了模型，平均绝对误差(MAE)和均方根误差(RMSE)。Shapley加法扩张（SHAP）方法用于解释变量的重要性。最后,构建了一个网络计算器，以促进其临床应用。
■堆叠模型（线性回归多层感知器）表现最好，调整后的最高R2为0.718（0.632至0.804）。平均预测偏倚为4毫升（MAE：4.008（3.68至4.336）），比线性模型更好。D1和D2在SHAP图中排名较高，并且在右侧卧位（RLD）中的表现要好于仰卧位。可以在https://cheason访问Web计算器。shinyapps.io/Stacking_regressor/.
■堆叠模型及其网络计算器可作为实用工具，用于准确估计接受选择性镇静GIE的患者的胃液量。建议麻醉师在患者的RLD位置测量D1和D2。
■我们回顾性分析了临床数据和POCUS数据（D1：头尾直径，D2：前后径）在南京第一医院接受择期镇静胃肠内窥镜检查（GIE）的1386例患者使用ML技术预测胃液量，包括六个不同的ML模型和一个堆叠模型。我们使用调整后的确定系数(R2)评估了模型，平均绝对误差(MAE)和均方根误差(RMSE)。Shapley加法扩张（SHAP）方法用于解释变量的重要性。最后,构建了一个网络计算器，以促进其临床应用。
■堆叠模型（线性回归多层感知器）表现最好，调整后的最高R2为0.718（0.632至0.804）。平均预测偏倚为4毫升（MAE：4.008（3.68至4.336）），比线性模型更好。D1和D2在SHAP图中排名较高，并且在右侧卧位（RLD）中的表现要好于仰卧位。可以在https://cheason访问Web计算器。shinyapps.io/Stacking_regressor/.
■堆叠模型及其网络计算器可作为实用工具，用于准确估计接受选择性镇静GIE的患者的胃液量。建议麻醉师在患者的RLD位置测量D1和D2。
