Leveraging vascular quality initiative data to improve hospital length of stay for patients undergoing endovascular aneurysm repair
Bibliographic record
Abstract
Background: The Society for Vascular Surgery Vascular Quality Initiative (SVS-SVQI) is a database that provides insight into standards of care and highlights opportunities for quality improvement by benchmarking institutional data against local, regional and national trends. Endovascular aneurysm repair (EVAR) is a frequently performed vascular operation. Postoperative length of stay in hospital (LOS) varies among institutions. We reviewed the morbidity and mortality of patients who underwent EVAR at our institution and the financial impact of increased LOS for these patients. In addition, we sought to identify modifiable factors associated with prolonged LOS. Methods: We identified all patients who underwent elective EVAR between Jan. 1, 2011, and Dec. 31, 2014. Preoperative patient characteristics, intraoperative details, postoperative factors, long-term (1 yr) outcomes and cost data were reviewed. Univariate analysis was used to determine statistical differences between patients with LOS less than or equal to 2 days and greater than 2 days. Interventions were implemented to modify factors identified as having a negative impact on EVAR LOS. Results: Identified factors that negatively affected EVAR LOS included social, neurologic, cardiovascular, urologic and renal issues. Following targeted interventions, LOS after EVAR decreased from an average of 3.8 to 3.0 days (p < 0.05). Logistic regression (n = 124) identified cardiovascular issues as the most significant predictor of LOS greater than 2 days (p = 0.001, odds ratio 14.24, 95% confidence interval 2.8–71.4). Reduction in LOS was associated with the additional benefit of 6.6% adjusted cost savings. Conclusion: By leveraging SVS-VQI data, we were able to reduce EVAR LOS by identifying modifiable factors and instituting focused interventions. The reduction in LOS was associated with cost savings to the hospital.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".