Greater Nutrient Intake Is Associated With Lower Mortality in Western and Eastern Critically Ill Patients With Low BMI: A Multicenter, Multinational Observational Study
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of feeding adequacy by NUTrition Risk in the Critically Ill (NUTRIC) groups in critically ill patients with body mass index (BMI) <20. Our purpose was to assess whether adequacy of protein/energy intake impacts mortality in patients with BMI <20 in Western/Eastern intensive care units (ICUs) and high/low NUTRIC groups. METHODS: Data from the International Nutrition Survey 2013-2014 were dichotomized into Western/Eastern ICU settings; BMI <20 or ≥20; and high (≥5)/low (<5) NUTRIC groups. Association of BMI <20 with 60-day mortality was compared in unadjusted and adjusted (Western/Eastern, age, medical/surgical admission, high/low NUTRIC group) logistic regression models. The impact of adequacy of protein/energy on 60-day mortality relationship was tested using general estimating equations in high/low NUTRIC groups, in unadjusted and adjusted models. RESULTS: Western (n = 4274) patients had higher mean BMI (27.9 ± 7.7 versus (vs) 23.4 ± 4.9, P < 0.0001) than Eastern (n = 1375), respectively. BMI <20 was associated with greater mortality (adjusted odds ratio [OR] 1.30, 95% confidence interval [CI] 1.07-1.57), with no interaction between BMI group and Western/Eastern ICU site. Among patients with BMI <20 and high NUTRIC score, 10% greater protein and energy adequacy was associated with 5.7% and 5.5% reduction in 60-day mortality, respectively. Results were not significantly different between Western and Eastern ICUs. CONCLUSIONS: The benefit of greater protein/energy intake in high-NUTRIC patients was observed regardless of geographic origin or low BMI, suggesting a consistent response to nutrition support in this group. Clinical guidelines and research projects focused on improving care in high-risk critically ill patients can be applied across geographic boundaries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".