Association between perioperative fluid management and patient outcomes: a multicentre retrospective study
Bibliographic record
Abstract
BACKGROUND: Postoperative complications increase hospital length of stay and patient mortality. Optimal perioperative fluid management should decrease patient complications. This study examined associations between fluid volume and noncardiac surgery patient outcomes within a large multicentre US surgical cohort. METHODS: Adults undergoing noncardiac procedures from January 1, 2012 to December 31, 2017, with a postoperative length of stay ≥24 h, were extracted from a large US electronic health record database. Patients were segmented into quintiles based on recorded perioperative fluid volumes with Quintile 3 (Q3) serving as the reference. The primary outcome was defined as a composite of any complications during the surgical admission and a postoperative length of stay ≥7 days. Secondary outcomes included in-hospital mortality, respiratory complications, and acute kidney injury. RESULTS: A total of 35 736 patients met the study criteria. There was a U-shaped pattern with highest (Q5) and lowest (Q1) quintiles of fluid volumes having increased odds of complications and a postoperative length of stay ≥7 days (Q5: odds ratio [OR] 1.51 [95% confidence interval {CI}: 1.30-1.74], P<0.001; Q1: OR 1.20 [95% CI: 1.04-1.38], P=0.011) compared with Q3. Patients in Q5 had greater odds of more severe acute kidney injury compared with Q3 (OR 1.52 [95% CI: 1.22-1.90]; P<0.001) and respiratory complications (OR 1.44 [95% CI: 1.17-1.77]; P<0.001). CONCLUSIONS: Both very high and very low perioperative fluid volumes were associated with an increase in complications after noncardiac surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".