Risk Prediction Models for Enteral Nutrition Aspiration in Adult Inpatients: A Systematic Review and Critical Appraisal

ABSTRACT

Objective

To systematically identify and appraise existing risk prediction models for EN aspiration in adult inpatients.

Data Sources

A systematic search was conducted across PubMed, Web of Science Core Collection, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Biomedical Literature Database (CBM) and VIP Database from inception to 1 March 2025.

Study Design

Systematic review of observational studies.

Review Methods

Two researchers independently performed literature screening and data extraction using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and the clinical applicability of the included models.

Results

A total of 17 articles, encompassing 29 prediction models, were included. The incidence of aspiration was 9.45%–57.00%. Meta-analysis of high-frequency predictors identified the following significant predictors of aspiration: history of aspiration, depth of endotracheal intubation, impaired consciousness, sedation use, nutritional risk, mechanical ventilation and gastric residual volume (GRV). The area under the curve (AUC) was 0.771–0.992. Internal validation was performed in 12 studies, while both internal and external validation were conducted in 5 studies. All studies demonstrated a high risk of bias, primarily attributed to retrospective design, geographic bias (all from different parts of China), inadequate data analysis, insufficient validation strategies and lack of transparency in the research process.

Conclusion

Current risk prediction models for enteral nutrition-associated aspiration show moderate to high discriminative accuracy but suffer from critical methodological limitations, including retrospective design, geographic bias (all models derived from Chinese cohorts, limiting global generalisability) and inconsistent outcome definitions.

Implications for Clinical Practice

Recognising the high bias of existing models, prospective multicentre data and standardised diagnostics are needed to develop more accurate and clinically applicable predictive models for enteral nutrition malabsorption.

Patient or Public Contribution

Not applicable.

Trial Registration

PROSPERO: CRD420251016435