Rapid Advancement in Enteral Nutrition Does Not Affect Systemic Inflammation and Insulin Homeostasis Following Pediatric Cardiopulmonary Bypass Surgery*
Bibliographic record
Abstract
OBJECTIVES: To determine impact of enteral nutrition delivery on the relationship among inflammation, insulin resistance, and outcomes following pediatric cardiopulmonary bypass surgery. DESIGN: Pilot, randomized study analyzed according to intention-to-treat analysis. SETTING: Pediatric cardiac ICU. PATIENTS: Infants (≤ 6 mo) undergoing cardiopulmonary bypass. INTERVENTIONS: Patients randomly assigned to receive rapid escalation to enteral nutrition reaching goal feeds by 27 hours or standard feeding practice reaching goal feeds by 63 hours. Feeds were initiated on the first postoperative day. MEASUREMENTS AND MAIN RESULTS: Fifty patients were randomized equally to study arms. Patients were a median (interquartile range) of 16 days old (7-110 d old), undergoing biventricular surgery (88%) with a median cardiopulmonary bypass time of 125 minutes (105-159 min). Serial blood samples were drawn before and after cardiopulmonary bypass, cardiac ICU admission, and every 12 hours (up to 96 hr) for glucose, insulin, and cytokines (interleukin-1α, interleukin-6, interleukin-8, interleukin-10, and tumor necrosis factor-α) levels. Glucose-insulin ratio was calculated to quantify insulin resistance. Patient characteristics, time to enteral nutrition initiation, enteral nutrition interruptions, and insulin administration were similar across intervention arms. FF reached goal feeds at similar intervals as standard feeding (39 hr [30-60 hr] vs 60 hr [21-78 hr]; p = 0.75). No difference in cytokine, insulin, or glucose-insulin ratio was noted between groups. Higher inflammation was associated with increased glucose-insulin ratio and higher risk of adverse events. In multivariable models of interleukin-8, FF was associated with increased glucose-insulin ratio (estimate of effect [95% CI], 0.152 [0.033-0.272]; p = 0.013). Although higher interleukin-8 was associated with an elevated risk of adverse event, this relationship was possibly mitigated by FF (odds ratio [95% CI], 0.086 [0.002-1.638]; p = 0.13). CONCLUSIONS: A FF strategy was not associated with changes to early enteral nutrition delivery. Inflammation, insulin resistance, and morbidity were similar, but FF may modify the relationship between inflammation and adverse event. Multicenter nutrition studies are possible and necessary in this vulnerable population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".