A systematic review and meta-analysis of unplanned hospital visits and re-admissions following radical prostatectomy for prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Unplanned visits (UPV) - re-admissions and emergency room (ER) visits - are markers of healthcare system quality. Radical prostatectomy (RP) is a commonly performed cancer procedure, where variation in UPV represents a gap in care for prostate cancer patients. Here, we systematically synthesize the rates, reasons, predictors, and interventions for UPV after RP to inform evidence-based quality improvement (QI) initiatives. METHODS: A systematic review was performed for studies from 2000-2020 using keywords: "re-admission," "emergency room/department," "unplanned visit," and "prostatectomy." Studies that focused on UPV following RP and that reported rates, reasons, predictors, or interventions, were included. Data was extracted via a standardized form. Meta-analysis was completed. RESULTS: Sixty studies, with 406 107 RP patients, were eligible; 16 028 UPV events (approximately 5%) were analyzed from 317 050 RP patients. UPV rates after RP varied between studies (ER visit range 6-24%; re-admissions range 0-56%). The 30-day and 90-day ER visit rates were 12% and 14%, respectively; the 30-day and 90-day re-admission rates were 4% and 9%, respectively. A total of 55% of all re-admissions after RP are directly due to postoperative genitourinary (GU)-related complications, such as strictures, obstructions, fistula, bladder-related, incontinence, urine leak, renal problems, and other unspecified urinary complications. The next most common re-admission reasons were anastomosis-related, infection-related, cardiovascular/pulmonary events, and wound-related issues. Thirty-four percent of all ER visits after RP are directly due to urine-related issues, such as retention, urinoma, obstruction, leak, and catheter problems. The next most common ER visit reasons were abdominal/gastrointestinal issues, infection-related, venous thromboembolic events, and wound-related issues. Predictors for increased re-admission included: open RP, lymph node dissection, Charlson comorbidity index ≥2, low surgeon/hospital case volume, and socioeconomic determinants of health. Of the 10 interventions evaluated, a 3.4% average reduction in UPV rate was observed, highlighting an approximate two-fold decrease. Meta-analysis demonstrated a significant benefit of interventions over controls, with odds ratio 0.62 (95% confidence interval 0.46-0.84). Interventions that used multidisciplinary, nurse-centered, programs, with patient self-care/empowerment were more beneficial than algorithmic patient care pathways and preoperative patient education. CONCLUSIONS: Twenty years of international, retrospective experience suggests UPV after RP are often related to GU complications and infection- or wound-related factors. QI interventions to reduce UPV should target these factors. While many re-admissions after RP appear to be unavoidable, ER visits have more opportunity for volume reduction by QI. The interventions evaluated herein have the potential to reduce UPV after RP.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".