Financial burden of prolonged urinary retention in patients awaiting holmium laser enucleation of the prostate in the Quebec healthcare system: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: With rising surgical wait times for benign prostatic hyperplasia (BPH)-related urinary retention (UR), we aimed to assess the healthcare costs of prolonged UR management in patients awaiting holmium laser enucleation of the prostate (HoLEP) and evaluate the impact of wait times on resource utilization and complications. METHODS: We retrospectively analyzed 91 patients with BPH-related UR on the HoLEP waitlist (September 2021-2024). Wait times, urologic and emergency department (ED) visits, interventions, and costs were recorded. Total costs included institutional and physician-billed fees from our cost center. Continuous variables were reported as mean ± standard deviation or median (interquartile range). RESULTS: Mean patient age was 71±8 years, with a mean prostate size of 123±54 g. Median wait time from retention to surgery was 220 days (149-300), with median total cost of $5315.95 (4343.85-7385.94). Longer wait times correlated with higher total costs (r=0.374, p<0.001), but inversely with cost per month (r=-0.680, p<0.001), suggesting cumulative burden over time. There were 685 urology clinic visits and 55 ED visits, nine (16%) resulting in hospital admissions. Complications occurred in 51 patients, including infections (63%), hematuria (47%), catheter issues (18%), and urosepsis (16%). Admissions were due to acute kidney injury (AKI) (n=3), urosepsis (n=2), pyelonephritis (n=2), and hematuria (n=2), with pyelonephritis, AKI, and urosepsis contributing the highest costs. Patients with complications required more visits and incurred higher costs (all p<0.05). CONCLUSIONS: Prolonged UR management significantly increases healthcare costs. Prioritizing earlier surgical intervention may reduce complications, lessen economic strain, and improve patient 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".