Association of the Hospital Volume of Frail Surgical Patients Cared for with Outcomes after Elective, Major Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Frailty is a risk factor for adverse postoperative outcomes. Hospitals that perform higher volumes of surgery have better outcomes than low-volume providers. We hypothesized that frail patients undergoing elective surgery at hospitals that cared for a higher volume of similarly frail patients would have improved outcomes. METHODS: We conducted a retrospective, population-based cohort study using linked administrative data in Ontario, Canada. We identified all adult major, elective noncardiac surgery patients who were frail according to the validated Johns Hopkins Adjusted Clinical Groups (ACG) frailty-defining diagnoses indicator. Hospitals were categorized into frailty volume quintiles based on volumes of frail surgical patients cared for. Multilevel, multivariable modeling measured the association of frailty volume with 30-day survival (primary outcome), complications, failure to rescue (secondary outcomes), and costs (tertiary outcome). RESULTS: Of 63,381 frail patients, 708 (1.1%) died after surgery. The thirty-day mortality rate in the lowest volume quintile was 1.1% compared to 0.9% in the highest. After adjustment for surgical risk, demographic characteristics, comorbidities, and clustering within hospitals, we found a significant association between frailty volume and improved survival (highest volume vs. lowest volume quintile: hazard ratio 0.51; 95% CI, 0.35 to 0.74; P < 0.0001). Although complication rates did not vary significantly between hospitals, failure-to-rescue rates were inversely related to volume. CONCLUSIONS: Frail patients have reduced survival and increased failure to rescue when they undergo operations at hospitals having a lower volume of frail surgical patients. Concentration of perioperative care in centers that frequently treat high-risk frail patients could improve population outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".