Systematic review of the prediction model for aspiration risk in enteral nutrition patients
10.3760/cma.j.cn115682-20241127-06513
- VernacularTitle:肠内营养患者误吸风险预测模型的系统评价
- Author:
Yumei DENG
1
;
Changxiu LI
;
Jing ZHOU
;
Wenlin ZHOU
;
Jimei LUO
;
Bingxue ZHOU
;
Lina MA
Author Information
1. 遵义医科大学护理学院，贵州 563000
- Publication Type:Journal Article
- Keywords:
Systematic review;
Enteral nutrition;
Aspiration;
Prediction model;
Evidence-based nursing
- From:
Chinese Journal of Modern Nursing
2025;31(29):3989-3997
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review and evaluate prediction models for aspiration risk in enteral nutrition patients, providing a reference for the development and application of future models.Methods:Literature related to prediction models for aspiration risk in enteral nutrition patients was searched in China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, Web of Science, Cochrane Library, Embase, and PubMed, with the search period covering from the inception of the databases to August 30, 2024. Two researchers independently conducted literature screening and data extraction, and the PROBAST tool was used to assess the risk of bias and applicability of the included studies.Results:A total of 18 studies were included, involving 24 prediction models, with sample sizes ranging from 103 to 512 and an event rate of 9.46% to 49.87%. The top six predictive variables reported most frequently were baseline age, history of aspiration, length of nasogastric tube insertion, nutritional risk, impaired consciousness, and Acute Physiology and Chronic Health Evaluation-Ⅱscore. The area under the receiver operating characteristic curve of the models ranged from 0.756 to 0.992. Twelve studies reported model calibration, six studies conducted internal validation only, one study conducted external validation only, and four studies performed both internal and external validation. The overall applicability of the 18 studies was good, but the risk of bias was high, mainly due to improper handling of continuous variables and missing data.Conclusions:The aspiration risk prediction models developed in various studies for enteral nutrition patients show good applicability but carry a high risk of bias. Future efforts should focus on further optimizing the model construction process, internal and external validation, and result analysis to provide more reliable and scientific tools for clinical aspiration risk assessment.
