The Effectiveness of Early Enteral Nutrition on Clinical Outcomes in Critically Ill Sepsis Patients: A Systematic Review.
Sun Jae MoonRyoung-Eun KoChi-Min ParkGee Young SuhJin Seub HwangChi Rayng ChungPublished in: Nutrients (2023)
The optimal timing of enteral nutrition (EN) in sepsis patients is controversial among societal guidelines. We aimed to evaluate the evidence of early EN's impact on critically ill sepsis patients' clinical outcomes. We searched the MEDLINE, Embase, CINAHL, Cochrane Library, ClinicalTrials.gov, and ICTRP databases on 10 March 2023. We included studies published after 2004 that compared early EN versus delayed EN in sepsis patients. We included randomized controlled trials (RCTs), non-RCTs, cohort studies, and case-control studies. Forest plots were used to summarize risk ratios (RRs), including mortality and mean difference (MD) of continuous variables such as intensive care unit (ICU) length of stay and ventilator-free days. We identified 11 eligible studies with sample sizes ranging from 31 to 2410. The RR of short-term mortality from three RCTs was insignificant, and the MD of ICU length of stay from two RCTs was -2.91 and -1.00 days (95% confidence interval [CI], -5.53 to -0.29 and -1.68 to -0.32). Although the RR of intestinal-related complications from one RCT was 3.82 (95% CI, 1.43 to 10.19), indicating a significantly higher risk for the early EN group than the control group, intestinal-related complications of EN reported in five studies were inconclusive. This systematic review did not find significant benefits of early EN on mortality in sepsis patients. Evidence, however, is weak due to inconsistent definitions, heterogeneity, risk of bias, and poor methodology in the existing studies.
Keyphrases
- intensive care unit
- end stage renal disease
- systematic review
- ejection fraction
- newly diagnosed
- chronic kidney disease
- case control
- randomized controlled trial
- acute kidney injury
- peritoneal dialysis
- physical activity
- prognostic factors
- cardiovascular events
- machine learning
- cardiovascular disease
- type diabetes
- deep learning
- acute respiratory distress syndrome
- molecular dynamics
- meta analyses
- patient reported