Analysis of urinary urea nitrogen in critically ill surgical patients: clinical variability and utility for caloric estimation
Bibliographic record
Abstract
Introduction: Background: critical illness is associated with loss of muscle mass, adversely affecting patient outcomes. The estimation of caloric and protein targets allows to tailor nutrition. This study explores the utility of weekly urinary urea nitrogen (UUN) measurements in critically-ill surgical patients for estimating nutritional needs. Methods: in this retrospective study, we analyzed weekly UUN measurements in patients admitted to a surgical intensive care unit (SICU) at a tertiary medical center. We compared UUN-derived EE calculations (UEE) with measured EE (MEE) obtained from indirect calorimetry (IC) and the predictive EE (PEE) using the Harris-Benedict equation. We also explored factors influencing UUN levels, and developed a predictive model for EE using UUN. Results: a total of 1,720 measurements from 892 patients were included in the final analysis. The study found significant variability in UUN levels, influenced primarily by urine output (R2 = 0.1584). The NPC:N ratio that was found to correlate best between MEE and UEE was 98.65. A moderate correlation was observed between UUN and both MEE and PEE, however the addition of UUN to classical variables of predictive models resulted in a marginal 1.6 % increase in R2 value. A statistically significant increase in UUN was observed between the first and second weeks of ICU admission (mean difference = -1.465, 95 % CI: -2.634 to -0.296, p = 0.004). Conclusions: while routine collection of UUN can reflect energy expenditure to some extent, their utility is limited by significant variability and therefore offers little added benefit in adjusting nutritional support for critically-ill surgical patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".