Prevalence, Risk Factors, Clinical Consequences, and Treatment of Enteral Feed Intolerance During Critical Illness
Bibliographic record
Abstract
BACKGROUND: We aimed to determine the incidence of enteral feed intolerance and factors associated with intolerance and to assess the influence of intolerance on nutrition and clinical outcomes. METHODS: We conducted a retrospective analysis of data from an international observational cohort study of nutrition practices among 167 intensive care units (ICUs). Data were collected on nutrition adequacy, ventilator-free days (VFDs), ICU stay, and 60-day mortality. Intolerance was defined as interruption of enteral nutrition (EN) due to gastrointestinal (GI) reasons (large gastric residuals, abdominal distension, emesis, diarrhea, or subjective discomfort). Logistic regression was used to determine risk factors for intolerance and their clinical significance. A sensitivity analysis restricted to sites specifying a gastric residual volume ≥200 mL to identify intolerance was also conducted. RESULTS: Data from 1,888 ICU patients were included. The incidence of intolerance was 30.5% and occurred after a median 3 days from EN initiation. Patients remained intolerant for a mean (±SD) duration of 1.9 ± 1.3 days . Intolerance was associated with worse nutrition adequacy vs the tolerant (56% vs 64%, P < .0001), fewer VFDs (2.5 vs 11.2, P < .0001), increased ICU stay (14.4 vs 11.3 days, P < .0001), and increased mortality (30.8% vs 26.2, P = .04). The sensitivity analysis demonstrated that intolerance remained associated with negative outcomes. Although mortality was greater among the intolerant patients, this was not statistically significant. CONCLUSIONS: Intolerance occurs frequently during EN in critically ill patients and is associated with poorer nutrition and clinical outcomes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".