A Comparative Analysis of Equations to Estimate Patient Energy Requirements Following Cardiopulmonary Bypass for Correction of Congenital Heart Disease
Bibliographic record
Abstract
BACKGROUND: No consensus exists on the optimal method to estimate resting energy expenditure (REE) in critically ill children following cardiopulmonary bypass (CPB). This study assesses the accuracy of REE estimation equations in children with congenital heart disease following CPB and tests the feasibility of using allometric scaling as an alternative energy prediction equation. METHODS: A retrospective analysis of a pediatric cohort following CPB (n = 107; median age 5.2 months, median weight 5.65 kg) who underwent serial measures (median 5 measurements) of REE using indirect calorimetry for 72 hours following CPB. We estimated REE using common estimation methods (Dietary Reference Intake, Harris Benedict, Schofield, World Health Organization [WHO]) as well as novel allometric equations. We compared estimated with measured REE to determine accuracy of each equation using overall discrepancy, calculated as a time-weighted average of the absolute deviation. RESULTS: All equations incorrectly estimated REE at all time points following CPB, with overestimation error predominating. WHO had the lowest discrepancy at 10.7 ± 8.4 kcal/kg/d. The allometric equation was inferior, with an overall discrepancy of 16.9 ± 10.4. There is a strong nonlinear relationship between body surface area and measured REE in this cohort, which is a key source of estimation error using linear equations. CONCLUSION: In a cohort of pediatric patients with congenital heart disease following CPB, no currently utilized clinical estimation equation reliably estimated REE. Allometric scaling proved inferior in estimating REE in children following CPB. Indirect calorimetry remains the ideal method of determining REE after CPB until nonlinear methods can be derived due to overestimation using linear equations.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".