Hu Jinjiu, Zhang Qiaoying, Wan Xin, Zhang Hui, Shen Qiao, Li Fei, Cai Ye, Meng Yuqian, Liu Peng, Zheng Xianlan
Department of Nursing, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
Pediatric Intensive Care Unit, Children's Hospital of Chongqing Medical University, Chongqing, China.
Crit Care Res Pract. 2025 Jun 23;2025:1049746. doi: 10.1155/ccrp/1049746. eCollection 2025.
Bedside ultrasonography is capable of evaluating gastric residual volume (GRV) and facilitating the identification of feeding intolerance (FI) among critically ill pediatric patients; however, a specialized predictive model tailored to this demographic has yet to be established. This study aims to develop a predictive model for the estimation of GRV using ultrasonography in this specific patient group. This prospective observational study included critically ill pediatric patients receiving enteral nutrition (EN). Clinical data, including gender, age, weight, height, gastric antrum cross-sectional area (CSA) in supine and right lateral positions, and qualitative grading system scores (Grade 0-2), were collected. GRV was measured by suctioning gastric contents under real-time ultrasound guidance, which was considered the actual GRV. The predictive models for GRV were developed using linear regression analysis. The agreement between predicted and actual GRV values was assessed using Bland-Altman analysis. A total of 108 children were included in the analysis. Significant differences ( < 0.05) were observed in GRV, GRV per kilogram, supine and right lateral decubitus (RLD) CSA among grades. Spearman correlation analysis revealed strong correlations between RLD CSA ( = 0.88, < 0.001) and qualitative grading system scores ( = 0.86, < 0.001) with suctioned GRV. A predictive model was developed using RLD CSA and qualitative grading system scores as predictors: GRV (mL) = -12.9 + 10.3 (RLD CSA [cm]) + 3.3 × Grade 1 + 10.1 × Grade 2. This model demonstrated an adjusted coefficient of determination ( ) of 0.878, Akaike's information criterion (AIC) of 873.43, and Bayesian information criterion (BIC) of 884.06. Bland-Altman analysis showed a mean difference of 0.1 mL/kg between predicted and suctioned GRV, with 95% limits of agreement (LoA) ranging from -1.65 to 1.87 mL/kg. The results suggest that ultrasound-based monitoring can predict GRV in critically ill children. In addition, the qualitative grading system can differentiate between high and low GRV, potentially serving as a rapid screening tool for identifying patients with high GRV.
床旁超声检查能够评估危重症儿科患者的胃残余量（GRV），并有助于识别喂养不耐受（FI）；然而，尚未建立针对这一人群的专门预测模型。本研究旨在为这一特定患者群体开发一种利用超声检查估算GRV的预测模型。这项前瞻性观察性研究纳入了接受肠内营养（EN）的危重症儿科患者。收集了临床数据，包括性别、年龄、体重、身高、仰卧位和右侧卧位时的胃窦横截面积（CSA）以及定性分级系统评分（0 - 2级）。通过在实时超声引导下抽吸胃内容物来测量GRV，这被视为实际GRV。使用线性回归分析建立GRV的预测模型。采用Bland - Altman分析评估预测的GRV值与实际GRV值之间的一致性。共有108名儿童纳入分析。各等级之间在GRV、每千克GRV、仰卧位和右侧卧位（RLD）CSA方面观察到显著差异（<0.05）。Spearman相关分析显示，RLD CSA（r = 0.88，P < 0.001）和定性分级系统评分（r = 0.86，P < 0.001）与抽吸的GRV之间存在强相关性。以RLD CSA和定性分级系统评分为预测因子建立了预测模型：GRV（mL）= -12.9 + 10.3（RLD CSA [cm]）+ 3.3×1级 + 10.1×2级。该模型的调整决定系数（R²）为0.878，赤池信息准则（AIC）为873.43，贝叶斯信息准则（BIC）为884.06。Bland - Altman分析显示，预测的GRV与抽吸的GRV之间平均差异为0.1 mL/kg，95%一致性界限（LoA）为 -1.65至1.87 mL/kg。结果表明，基于超声的监测可预测危重症儿童的GRV。此外，定性分级系统可区分高GRV和低GRV，有可能作为识别高GRV患者的快速筛查工具。
