Asia Pacific journal of clinical nutrition · 2025
Systematic evaluation of risk prediction models for feeding intolerance in ICU patients during enteral nutrition.
СтатьяМетаанализСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Huang Xianqiao, Zhong Liming, Li Chao, Tang Yu
Аннотация
BACKGROUND AND OBJECTIVES: It has been found that ICU patients may encounter various complications during enteral nutrition (EN). Of these, feeding intolerance (FI) is a common issue that often necessitates the reduction or cessation of EN. This study aims to evaluate risk prediction models for feeding intolerance (FI) in critically ill patients receiving EN by searching major public databases.
METHODS AND STUDY DESIGN: We searched for relevant studies in Embase, PubMed, Web of Science, Chinese Biomedical Database (CBM), China National Knowledge Infrastructure (CNKI), Wanfang Data, and cqvip.com up until January 2024. Two researchers independently conducted the screening and data extraction processes, and the quality of the literature was assessed using bias risk assessment tools.
RESULTS: A total of 13 references were included, and the subjects included patients with sepsis, pancreatitis or cerebral apoplexy; the incidence of FI was 35.2%-49.3%. The studies discussed the predictive performance of various models, with 11 studies reporting on their accuracy and calibration. The models demonstrated the area under the curve (AUC) of the receiver operating characteristic (ROC) curve or the concordance index (C-index) between 0.70 and 0.91, sensitivity from 0.81 to 0.93, and specificity from 0.68 to 0.83.
CONCLUSIONS: There is a critical need for risk prediction models for FI in critically ill patients on EN that are both internally and externally validated and exhibit high performance.
METHODS AND STUDY DESIGN: We searched for relevant studies in Embase, PubMed, Web of Science, Chinese Biomedical Database (CBM), China National Knowledge Infrastructure (CNKI), Wanfang Data, and cqvip.com up until January 2024. Two researchers independently conducted the screening and data extraction processes, and the quality of the literature was assessed using bias risk assessment tools.
RESULTS: A total of 13 references were included, and the subjects included patients with sepsis, pancreatitis or cerebral apoplexy; the incidence of FI was 35.2%-49.3%. The studies discussed the predictive performance of various models, with 11 studies reporting on their accuracy and calibration. The models demonstrated the area under the curve (AUC) of the receiver operating characteristic (ROC) curve or the concordance index (C-index) between 0.70 and 0.91, sensitivity from 0.81 to 0.93, and specificity from 0.68 to 0.83.
CONCLUSIONS: There is a critical need for risk prediction models for FI in critically ill patients on EN that are both internally and externally validated and exhibit high performance.