A systematic review and meta-analysis of unplanned hospital visits and re-admissions following radical prostatectomy for prostate cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,019 | 0,043 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».