SynthesisFrontiers in public health2026
Risk prediction models for enteral feeding intolerance in critically ill patients: a systematic review.
Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: To systematically evaluate existing risk prediction models for enteral feeding intolerance (EFI) in critically ill patients, with the aim of providing evidence-based references for clinical practice. Methods: PubMed, Web of Science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Biology Medicine Disc (CBM), and VIP Database were systematically searched from database inception to November 20, 2025. Studies that developed or validated risk prediction models for enteral feeding intolerance in adult critically ill patients were included. Data extraction was conducted using the CHARMS checklist, and the risk of bias and applicability of the included studies were assessed using the PROBAST tool. Results: A total of 25 studies were included, involving 25 risk prediction models for enteral feeding intolerance. Sample sizes ranged from 118 to 1,584 patients, and the incidence of EFI ranged from 26.25 to 61.3%. APACHE II score, blood glucose, mechanical ventilation, age, albumin, and intra-abdominal pressure were relatively frequently reported predictors, but no single predictor was consistently identified across all models. Conclusion: Existing risk prediction models for enteral feeding intolerance in critically ill patients demonstrate certain predictive value; however, the methodological quality varies considerably, and external validation is insufficient. Future studies should conduct large-scale, multicenter, prospective research and adopt standardized methodological procedures to develop and validate prediction models, thereby improving their clinical applicability and generalizability. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261281618, identifier (CRD420261281618).
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