Glomerular hyperfiltration is an independent predictor of postoperative outcomes: A <scp>NSQIP</scp><scp>multi‐specialty</scp> surgical cohort analysis
Bibliographic record
Abstract
AIM: While high estimated glomerular filtration rate (eGFR) has been associated with increased overall mortality, its effect on postoperative outcomes is relatively understudied. We sought to investigate the association between high eGFR and 30-day postoperative outcomes using a multi-specialty surgical cohort. METHODS: Using the National Surgical Quality Improvement Program database, we selected adult for whom eGFR could be calculated using the 2021 Chronic Kidney Disease Epidemiology Collaboration equation. Based on sex-specific distributions of eGFR stratified by age quintiles, we classified patients into low (<5th percentile), normal (5-95th percentile) and high eGFR (>95th percentile). The primary outcome was a composite of any 30-day major adverse outcomes, including: death, reoperation, cardiac arrest, myocardial infarction and stroke. Secondary outcomes included 30-day infectious complications, venous thromboembolism (VTE), bleeding requiring transfusion, prolonged length of stay and unplanned readmission. After matching for demographic differences, comorbidity burden and operative characteristics, logistic regression models were used to evaluate the association between extremes of eGFR and the outcomes of interest. RESULTS: Of 1 668 447 patients, 84 115 (5.07%) had a high eGFR. High eGFR was not associated with major adverse outcomes (odds ratio [OR] 1.00 [95% confidence interval (CI): 0.97, 1.03]); however, it was associated with reoperation (OR 1.04 [95% CI: 1.00,1.08]), infectious complications (OR 1.14 [95% CI: 1.11, 1.16]), VTE (OR 1.15 [95% CI: 1.09, 1.22]) and prolonged length of stay (OR 1.19 [95% CI: 1.16, 1.21]). CONCLUSION: Our findings support an association between high eGFR and adverse 30-day postoperative outcomes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".