Does Enhanced Recovery After Surgery (ERAS) Reduce ICU Admission Rates and Post-Operative Sepsis in High-Risk Gastrointestinal Resections? A Systematic Review of Multi-Modal Perioperative Care
Bibliographic record
Abstract
Background: Enhanced Recovery After Surgery (ERAS) protocols are gaining more and more popularity in major gastrointestinal resections to improve perioperative outcomes. Although ERAS is proven to be effective in terms of complications and length of stay, its effect on unplanned admissions to the intensive care unit (ICU) and post-operative sepsis among high risk patients is not understood Purpose: The purpose of this systematic review is to please evaluate the effectiveness of ERAS practices in the prevention of ICU admissions and post-operative sepsis in patients who have major elective surgery of the gastrointestinal tract (colon, gastric, and esophageal resections).Methods: A systematic search of publications published after 2015 in PubMed, Embase, Cochrane Library, and Web of Science was performed according to PRISMA 2020. The eligible studies were randomized controlled trials and observational studies comparing the ERAS to the traditional perioperative care. The results compared included unintentional ICU hospitalization, post-operative sepsis, length of stay, and perioperative morbidity. The quality of studies and the risk of bias were measured with the help of Cochrane RoB 2 and the Newcastle- Ottawa Scale instruments (Wisely & Barclay, 2016; Holubar et al., 2017).Findings: Twenty-three studies that used high-risk gastrointestinal surgery patients were used. There is some evidence indicating that ERAS protocols may be linked to a decrease in unplanned ICU admissions, and they are not linked to an increase in a post-operative sepsis rate. Secondary outcomes were reduced hospitalization and better perioperative physiological outcomes. ERAS heterogeneity in terms of components and design was observed.Conclusion: ERAS practice seems to effectively and safely decrease the necessity of ICU care level in patients at high risk undergone gastrointestinal surgery and still not increase the risk of post-surgical sepsis. Perioperative care in a selected high-risk patient can be safely delivered via standardized, protocolized protocols and high-risk patients can recover in a ward-based setup. Large multi-center studies should be done in the future to support these results
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 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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.008 |
| 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".