Impact of Personalized Parenteral Nutrition on Inflammatory Markers and Clinical Outcomes in Critically Ill Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and aims: Personalized parenteral nutrition (PPN) is a customized strategy to address the individual metabolic and nutritional requirements of the critically ill patients, especially in cases where enteral nutrition (EN) is not possible. However, available evidence regarding its effectiveness and safety is still inconclusive. This systematic review and meta-analysis aim to evaluate the impact of PPN on the clinical outcome of critically ill patients, including its effect on their length of stay in the intensive care unit (ICU), morbidity, and mortality. Methodology: A systematic literature search was conducted in PubMed, EMBASE, and Cochrane databases. Information pertinent to the question was retrieved from the selected studies by using a structured data extraction form. Included studies were those that had assessed the impact on clinical outcomes of PPN in critically ill patients. Data were synthesized using a random-effects meta-analysis model. An odds ratio (OR) with 95% confidence intervals (CIs) was used as the pooled effect size. Results: = 81%). Conclusion: Personalized parenteral nutrition has demonstrated improved benefits in tailored nutritional support for critically ill patients. The evidence, however, has shown mixed clinical outcomes, and its effect on mortality and morbidity has been inconclusive. Future research is needed to optimize the composition of PPN formulations and evaluate the long-term effects of this intervention. How to cite this article: Gatar O, Arishi AA, Sultan MA, Gatar MM. Impact of Personalized Parenteral Nutrition on Inflammatory Markers and Clinical Outcomes in Critically Ill Patients: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2025;29(12):1040-1049.
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 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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".