385. HOSPITAL COSTS OF POSTOPERATIVE ADVERSE EVENTS FOLLOWING ESOPHAGECTOMY: RESULTS FROM 10 CANADIAN INSTITUTIONS
Bibliographic record
Abstract
Abstract Background Surgical resection plays a central role in the multimodal curative intent management of esophageal cancer. Despite advances in peri-operative care, 60% of patients undergoing esophagectomy experience postoperative adverse events (AEs), if AEs are prospectively collected by surgeons. We recently performed a systematic review documenting the published index hospital costs of AEs, yet the overall costs of AEs in Canadian esophageal centres remains unknown. Methods Between 2017–2021, prospectively collected data from 10 Canadian esophageal centres were included. Volumes of esophagectomy, incidence of postoperative AEs (anastomotic leak (AL), pneumonia, respiratory failure), graded using the Ottawa TM&M system [ottawatmm.org], and obtained from 2 Canadian Association of Thoracic Surgeons (CATS) National Positive Deviance Seminars were included. Using literature-derived index hospitalization costs of AEs, estimates of annual costs were obtained (2023 Cdn$). Results Data from 795 esophagectomies performed in Canada between 2017–2019 revealed an average AL rate of 15% (8.7–22%). This led to an estimated yearly hospital cost of $6.63 M. Additional data from 863 esophagectomies performed between 2018–2021 revealed an average yearly pneumonia rate of 10.2% (2.4–20.3%)., with an estimated cost of $1.44 M, and respiratory failure (requiring ICU admission) of 7.8% (0–13.8%), with an estimated cost of $1.83 M. When all 10 centres are combined, these 3 AEs equate to over $9.9 M in hospital expenditures/year. A 1% reduction/year in the 3 AEs would lead to a cost-savings of $816,738. Conclusion Our analysis of only 3 post-operative AEs highlights the staggering costs to hospitals across Canada individually, and collectively, and emphasizes the need to support value-based QI research. Direct financial support from hospitals to support value-based QI in esophageal surgery has the potential to simultaneously improve care and provide valuable cost savings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".