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Record W2572959123 · doi:10.1097/ccm.0000000000002083

Greater Protein and Energy Intake May Be Associated With Improved Mortality in Higher Risk Critically Ill Patients: A Multicenter, Multinational Observational Study*

2017· article· en· W2572959123 on OpenAlexaff
Charlene Compher, Jesse Chittams, Therese Sammarco, Michele Nicolò, Daren K. Heyland

Bibliographic record

VenueCritical Care Medicine · 2017
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsClinical Evaluation Research UnitKingston General Hospital
Fundersnot available
KeywordsMedicineOdds ratioHazard ratioInternal medicineCohort studyObservational studyIntensive careConfidence intervalProspective cohort studyLogistic regressionCritically illLower riskIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Controversy exists about the value of greater nutritional intake in critically ill patients, possibly due to varied patient nutritional risk. The objective of this study was to investigate whether clinical outcomes vary by protein or energy intake in patients with risk evaluated by the NUTrition Risk in the Critically Ill score. DESIGN: Prospective observational cohort. SETTING: A total of 202 ICUs. PATIENTS: A total of 2,853 mechanically ventilated patients in ICU greater than or equal to 4 days and a subset of 1,605 patients in ICU greater than or equal to 12 days. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In low-risk (NUTrition Risk in the Critically Ill, < 5) and high-risk (NUTrition Risk in the Critically Ill, ≥ 5) patients, mortality and time to discharge alive up to day 60 were assessed relative to nutritional intake over the first 12 days using logistic regression and Cox proportional hazard regression, respectively. In high-risk but not low-risk patients, mortality was lower with greater protein (4-d sample: odds ratio, 0.93; 95% CI, 0.89-0.98; p = 0.003 and 12-d sample: odds ratio, 0.90; 95% CI, 0.84-0.96; p = 0.003) and energy (4-d sample: odds ratio, 0.93; 95% CI, 0.89-0.97; p < 0.001 and 12-d sample: odds ratio, 0.88; 95% CI, 0.83-0.94; p < 0.001) intake. In the 12-day sample, there was significant interaction among NUTrition Risk in the Critically Ill category, mortality, and protein and energy intake, whereas in the 4-day sample, the test for interaction was not significant. In high-risk but not low-risk patients, time to discharge alive was shorter with greater protein (4-d sample: hazard ratio, 1.05; 95% CI, 1.01-1.09; p = 0.01 and 12-d sample: hazard ratio, 1.09; 95% CI, 1.03-1.16; p = 0.002) and energy intake (4-d sample: hazard ratio, 1.05; 95% CI, 1.01-1.09; p = 0.02 and 12-d sample: hazard ratio, 1.09; 95% CI, 1.03-1.16; p = 0.002). In the 12-day sample, there was significant interaction among NUTrition Risk in the Critically Ill category, time to discharge alive, and protein and energy intake, whereas in the 4-day sample, the test for interaction was not significant. CONCLUSIONS: Greater nutritional intake is associated with lower mortality and faster time to discharge alive in high-risk, longer stay patients but not significantly so in nutritionally low-risk patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.370
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations266
Published2017
Admission routes1
Has abstractyes

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