Nutrition therapy in the older critically ill patients: A scoping review
Bibliographic record
Abstract
INTRODUCTION: There is a lack of guidelines or formal systematic synthesis of evidence for nutrition therapy in older critically ill patients. This study is a scoping review to explore the state of evidence in this population. METHOD: MEDLINE and Embase were searched from inception until 9 February 2022 for studies that enrolled critically ill patients aged ≥60 years and investigated any area of nutrition therapy. No language or study design restrictions were applied. RESULTS: Thirty-two studies (5 randomised controlled trials) with 6 topics were identified: (1) nutrition screening and assessments, (2) muscle mass assessment, (3) route or timing of nutrition therapy, (4) determination of energy and protein requirements, (5) energy and protein intake, and (6) pharmaconutrition. Topics (1), (3) and (6) had similar findings among general adult intensive care unit (ICU) patients. Skeletal muscle mass at ICU admission was significantly lower in older versus young patients. Among older ICU patients, low muscularity at ICU admission increased the risk of adverse outcomes. Predicted energy requirements using weight-based equations significantly deviated from indirect calorimetry measurements in older vs younger patients. Older ICU patients required higher protein intake (>1.5g/kg/day) than younger patients to achieve nitrogen balance. However, at similar protein intake, older patients had a higher risk of azotaemia. CONCLUSION: Based on limited evidence, assessment of muscle mass, indirect calorimetry and careful monitoring of urea level may be important to guide nutrition therapy in older ICU patients. Other nutrition recommendations for general ICU patients may be used for older patients with sound clinical discretion.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".