A systematic review of risk prediction models for feeding intolerance in patients receiving enteral nutrition
10.16151/j.1007-810x.2024.02.007
- VernacularTitle:肠内营养病人喂养不耐受风险预测模型的系统评价
- Author:
Xiao-Jie CHEN
1
;
Xia DUAN
;
Wei-Yan ZHENG
;
Li TAO
Author Information
1. 上海交通大学医学院附属仁济医院护理部,上海200127;同济大学医学院,上海200092
- Keywords:
Feeding intolerance;
Risk prediction;
Model;
Systematic review
- From:
Parenteral & Enteral Nutrition
2024;31(2):107-113
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review the current status of research on risk prediction models for feeding intolerance (FI) in patients receiving enteral nutrition (EN), and to provide a reference for medical workers to select, apply, and calibrate models, or to construct related prediction models. Methods:A literature search was conducted in the China National Knowledge Infrastructure (CNKI), VIP, WanFang, Chinese Biomedical Literature Database (CBM), Cochrane Library, PubMed, Embase, Web of Science, and CINAHL databases published on risk prediction models for FI in patients receiving EN. The search time was limited from the database establishment to February 28, 2023. Two researchers independently reviewed the literature, extracted relevant information, and evaluated the bias and applicability of the included studies. Results:A total of 10 studies were included, involving 14 models. The area under the receiver operating characteristic curve (AUC) of the included models ranged from 0.70 to 0.889. The top three predictors in the included models were age, mechanical ventilation, and albumin level, with albumin level being a protective factor. Conclusion:The occurrence of FI in patients receiving EN is related to advanced age, mechanical ventilation, and low albumin level. The existing risk prediction models have a high risk of bias. In the future, appropriate machine learning algorithms should be selected, and large-sample, multicenter studies should be conducted to construct FI risk prediction models with universal applicability. Targeted preventive measures should be implemented to reduce the risk of FI.
