Perioperative optimization in major abdominal surgery: impact of hemodynamic and metabolic management protocols in patients with critical comorbidities: a systematic review
Bibliographic record
Abstract
Introduction: patients with critical comorbidities undergoing major abdominal surgery face elevated risks of complications due to reduced physiological reserve. Hemodynamic and metabolic optimization strategies, including goal-directed fluid therapy and glycemic or nutritional protocols, may improve perioperative outcomes. This systematic review evaluates the effectiveness of such interventions in high-risk patients.Methodology: a systematic search was performed in PubMed, Google Scholar and Cochrane Library up to May 2025. Eligible studies included randomized controlled trials and cohort observational studies. Risk of bias was assessed using the Cochrane RoB 2.0 tool for RCTs and the Newcastle–Ottawa Scale for observational studies. Two reviewers independently screened and extracted data.Results: seventeen studies evaluated hemodynamic and metabolic optimization in high-risk abdominal surgery. Hemodynamic strategies—such as goal-directed fluid therapy guided by cardiac index or stroke volume variation—were associated with reductions in 30-day mortality (15.5% vs. 21.8%, p=0.005), complications, ICU admissions, and length of stay in several trials. However, outcomes were inconsistent across studies, with some showing no significant benefits. Metabolic optimization, including glycemic control and individualized nutrition, improved nitrogen balance, body composition, glycemic targets, and reduced liver dysfunction. Malnutrition was linked to increased complications and prolonged hospital stay. While many interventions showed promising results, variability in study designs and outcomes limits definitive conclusions.Conclusion: hemodynamic and metabolic optimization may improve outcomes in high-risk abdominal surgery, though effects vary. Multimodal strategies targeting fluid balance, glycemic control, and nutrition appear beneficial.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".