Chen Juan, Su Yushuang, Li Tianlong, Mao Xiaorong, Jiang Qinghua, Yang Qin, Wen Qing, Pu Zaichun, Liu Mengting
Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, West Second Section, 1st Ring Road, Qingyang District, Chengdu, 610072, Sichuan Province, China.
Sichuan Cancer Hospital and Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Sci Rep. 2025 Jul 19;15(1):26273. doi: 10.1038/s41598-025-12252-8.
In recent years, numerous researchers have developed risk prediction models for aspiration in patients with nasogastric enteral nutrition (EN). Nevertheless, comprehensive and systematic comparative studies are lacking. This study systematically review and evaluate the studies on aspiration risk prediction models in patients with nasogastric EN. A computer search was conducted from the database establishment to May 10, 2025. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluation tool was used to assess the quality of the included studies, and the meta-analysis was conducted using Stata 17 software to analyze the prediction factors included in the models and the area under the curve (AUC) values of the validated models. Eleven studies were included, with a total of 22 aspiration risk prediction models for patients with nasogastric EN. The AUC ranged from 0.809 to 0.992. The PROBAST evaluation results showed that all 11 included studies had a high risk of bias. The most common predictive factors included the number of diseases, history of aspiration, use of sedative, depth of tube placement, amount of gastric residue, APACHE II score, consciousness disturbance, nutritional risk, age. The pooled AUC value of the four validated models was 0.92 (95% confidence interval: 0.90-0.93), indicating an excellent level of discrimination. The study protocol has been registered with PROSPERO (registration number: CRD42024594672).
近年来，众多研究人员针对鼻胃管肠内营养（EN）患者的误吸情况开发了风险预测模型。然而，缺乏全面且系统的比较研究。本研究系统回顾并评估了关于鼻胃管EN患者误吸风险预测模型的研究。从数据库建立至2025年5月10日进行了计算机检索。使用预测模型偏倚风险评估工具（PROBAST）评估纳入研究的质量，并使用Stata 17软件进行荟萃分析，以分析模型中包含的预测因素以及验证模型的曲线下面积（AUC）值。纳入了11项研究，共有22个针对鼻胃管EN患者的误吸风险预测模型。AUC范围为0.809至0.992。PROBAST评估结果显示，所有11项纳入研究均存在高偏倚风险。最常见的预测因素包括疾病数量、误吸史、镇静剂使用情况、导管置入深度、胃残余量、急性生理与慢性健康状况评分系统II（APACHE II）评分、意识障碍、营养风险、年龄。四个验证模型的合并AUC值为0.92（95%置信区间：0.90 - 0.93），表明具有出色的区分水平。本研究方案已在国际前瞻性系统评价注册库（PROSPERO）注册（注册号：CRD42024594672）。
