Hospital Quality Metrics for Radical Cystectomy: Disease Specific and Correlated to Mortality Outcomes
Bibliographic record
Abstract
PURPOSE: Measuring quality is a high priority for health care systems globally. Despite the high perioperative morbidity, mortality, expenditures and performance variation of radical cystectomy there is a paucity of validated bladder cancer quality metrics. We aimed to create a hospital quality scoring system for radical cystectomy which is disease specific and associated with patient centered outcomes. MATERIALS AND METHODS: We used the National Cancer Database to identify hospitals where radical cystectomy was performed from 2004 to 2014. Mixed effects models were constructed to assess variation in hospital performance across 7 quality indicators. Indirect standardization was used to case mix adjust hospital performance. We assessed associations between quality indicators as well as the novel BC-QS (Bladder Cancer Quality Score) composite hospital quality metric with 30-day, 90-day and overall mortality using logistic and Cox regression, respectively. RESULTS: At 1,200 facilities radical cystectomy was performed in a total of 48,341 patients from 2004 to 2014. Mixed effects models demonstrated significant between hospital variation across all quality indicators after case mix adjustment. The composite BC-QS metric was composed of the hospital positive margin rate, the lymph node dissection rate and the neoadjuvant chemotherapy rate. Better BC-QS performance was associated with lower 30-day and 90-day mortality (adjusted OR 0.78, 95% CI 0.64-0.96, and OR 0.84, 95% CI 0.72-0.97, respectively) and overall mortality (HR 0.86, 95% CI 0.81-0.92). Hospitals with a higher BC-QS had higher volume and more were affiliated with an academic institution than hospitals with a lower BC-QS (p <0.0001). CONCLUSIONS: The BC-QS captures variations in the hospital performance of radical cystectomy and it shows an association of higher quality with lower patient mortality. Our validation of this quality metric provides support for its potential use by policy makers and payers in efforts to measure hospital quality for high cost surgeries.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".