19 Prehabilitation in open gynecologic oncology procedures and radical cystectomy: a pathway to better outcomes and cost savings
Bibliographic record
Abstract
Introduction As the surgical population ages, the risk of post-operative complications rises, highlighting the need for effective reduction strategies. Prehabilitation, a multimodal optimization program, seeks to reduce post-operative complications and enhance patient satisfaction. However, its cost-effectiveness and optimal implementation strategies remain unclear.Methods This study evaluated patients undergoing major gynecologic-oncology (N = 154) and radical cystectomy procedures (N = 62) at Vancouver General Hospital (VGH) in British Columbia, Canada, from 2017–2020. The prehabilitation in this study is part of the Surgical Patient Optimization Collaborative (SPOC), which promotes preoperative optimization across British Columbia. Our site selected seven of the 13 potential clinical components for preoptimization: smoking, anemia, diabetes, sleep apnea, nutrition, pain, and cardiovascular disease. Patients were identified preoperatively through surgical screening questionaries and promptly referred to management or treatment programs. Cost analysis used NSQIP and CIHI data, comparing prehabilitation patients to a matched control group while accounting for implementation costs (SPOC).Results Prehabilitation reduced costs by $480 per gynecologic-oncology patient, primarily by lowering readmissions and reoperations. For radical cystectomy, savings were $5,626 per patient, largely from reduced same-stay complications. Implementation prehabilitation costs for this study were estimated at $250, due to pre-existing programs. Assuming an optimization rate of 75%, annual net savings at VGH are estimated at $43,054 for gynecologic oncology and $342,741 for radical cystectomy procedures. Additionally, 75% of patients reported improved surgical experiences.Conclusion Prehabilitation enhances patient experience, reduces complications, and provides substantial cost savings, supporting its integration into surgical care pathways. These findings underscore its potential for broader adoption in preoperative optimization efforts.References Barberan-Garcia A, Ubre M, Pascual-Argente N, et al. Post-discharge impact and cost-consequence analysis of prehabilitation in high-risk patients undergoing major abdominal surgery: secondary results from a randomised controlled trial. British Journal of Anaesthesia 2019;123(4):450–456.Gillis C, Ljungqvist O, Carli F. Prehabilitation, enhanced recovery after surgery, or both? A narrative review. British Journal of Anaesthesia 2022;128(3).McIsaac DI, Gill M, Boland L, et al. Prehabilitation in adult patients undergoing surgery: an umbrella review of systematic reviews. British Journal of Anaesthesia 2022;128(2):244–257.Metzner M, Mayson K, Schierbeck G, Wallace T. The implementation of preoperative optimization in British Columbia: a quality improvement initiative. Can J Anaesth. 2024;71(12):1672–1684.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".