PD29-01 COMPLICATIONS AND INTERVENTIONS IN PATIENTS WITH ARTIFICIAL URINARY SPHINCTERS.
Notice bibliographique
Résumé
You have accessJournal of UrologyTrauma/Reconstruction/Diversion: Urethral Reconstruction (including Stricture, Diverticulum) II1 Apr 2017PD29-01 COMPLICATIONS AND INTERVENTIONS IN PATIENTS WITH ARTIFICIAL URINARY SPHINCTERS. Vladimir A Ruzhynsky, Christopher JD Wallis, Sidney B Radomski, Refik Saskin, Lesley Carr, Robert K Nam, Armando Lorenzo, and Sender Herschorn Vladimir A RuzhynskyVladimir A Ruzhynsky More articles by this author , Christopher JD WallisChristopher JD Wallis More articles by this author , Sidney B RadomskiSidney B Radomski More articles by this author , Refik SaskinRefik Saskin More articles by this author , Lesley CarrLesley Carr More articles by this author , Robert K NamRobert K Nam More articles by this author , Armando LorenzoArmando Lorenzo More articles by this author , and Sender HerschornSender Herschorn More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1353AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The artificial urinary sphincter (AUS) is the most widely known treatment for male stress urinary incontinence. However, there are a lack of population-based data regarding rates of long-term AUS-related complications, including the need for revision/removal and reimplantation. We sought to characterize long-term rates of AUS revision/removal and reimplantation among all patients undergoing initial AUS insertion in the province of Ontario. Further, we sought to identify risk factors for these outcomes. METHODS We conducted a population-based, retrospective cohort study of all male patients who underwent AUS implantation from 1994-2013 in Ontario, Canada, a single payer government-funded health system. Hospital procedure codes and physician billing codes were used to identify patients who had initial AUS treatment and a subsequent revision/removal, or reimplantation. The Kaplan-Meier method and multivariable Cox proportional hazards models were used to examine the cumulative incidence of AUS reimplantation and revision/removal and to identify risk factors, respectively. RESULTS A total of 1632 male patients underwent implantation of AUS between 1994 and 2013. Overall, 10-year AUS reimplantation and revision/removal-free survival rates were 73.3% and 65.7%, respectively. Pre-implantation radiotherapy was not significantly associated with the risk of AUS reimplantation (p=0.17) or revision/removal (p=0.95). The risk of AUS reimplantation was significantly lower for patients who underwent AUS insertion at a hospital in the highest volume quartile of AUS surgeries (what is the quartile/yr) (Hazard Ratio (HR)=0.55, 95% CI 0.37-0.82), compared to those in the lowest quartile. Increasing comorbidity was associated with an increasing risk of AUS removal/revision (p=0.0008). Patient age at the time of implantation, region of residence, income quintile, and hospital type (academic vs. community) were not significantly associated with AUS reimplantation or revision/removal. CONCLUSIONS Most men who undergo AUS placement will still have a device in situ, without repeat surgeries, at 10 years following insertion. Radiotherapy does not appear to increase the risk of repeat surgeries. High volume centres have the lowest rates of reimplantation and patients with increasing morbidity have the highest risk of removal /revision. Standard clinical and epidemiologic data do not appear to predict the risk of these outcomes. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e572 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Vladimir A Ruzhynsky More articles by this author Christopher JD Wallis More articles by this author Sidney B Radomski More articles by this author Refik Saskin More articles by this author Lesley Carr More articles by this author Robert K Nam More articles by this author Armando Lorenzo More articles by this author Sender Herschorn More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,016 |
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 ».