Early enteral nutrition in critically ill patients: ESICM clinical practice guidelines
Bibliographic record
Abstract
PURPOSE: To provide evidence-based guidelines for early enteral nutrition (EEN) during critical illness. METHODS: We aimed to compare EEN vs. early parenteral nutrition (PN) and vs. delayed EN. We defined "early" EN as EN started within 48 h independent of type or amount. We listed, a priori, conditions in which EN is often delayed, and performed systematic reviews in 24 such subtopics. If sufficient evidence was available, we performed meta-analyses; if not, we qualitatively summarized the evidence and based our recommendations on expert opinion. We used the GRADE approach for guideline development. The final recommendations were compiled via Delphi rounds. RESULTS: We formulated 17 recommendations favouring initiation of EEN and seven recommendations favouring delaying EN. We performed five meta-analyses: in unselected critically ill patients, and specifically in traumatic brain injury, severe acute pancreatitis, gastrointestinal (GI) surgery and abdominal trauma. EEN reduced infectious complications in unselected critically ill patients, in patients with severe acute pancreatitis, and after GI surgery. We did not detect any evidence of superiority for early PN or delayed EN over EEN. All recommendations are weak because of the low quality of evidence, with several based only on expert opinion. CONCLUSIONS: We suggest using EEN in the majority of critically ill under certain precautions. In the absence of evidence, we suggest delaying EN in critically ill patients with uncontrolled shock, uncontrolled hypoxaemia and acidosis, uncontrolled upper GI bleeding, gastric aspirate >500 ml/6 h, bowel ischaemia, bowel obstruction, abdominal compartment syndrome, and high-output fistula without distal feeding access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".