PD45-12 ECONOMIC EVALUATION OF RENAL CELL CARCINOMA (RCC) IN CANADA USING REAL-WORLD EVIDENCE; A HEALTHCARE SYSTEM PERSPECTIVE
Notice bibliographique
Résumé
You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance II (PD45)1 Apr 2020PD45-12 ECONOMIC EVALUATION OF RENAL CELL CARCINOMA (RCC) IN CANADA USING REAL-WORLD EVIDENCE; A HEALTHCARE SYSTEM PERSPECTIVE Alice Dragomir*, Sara Nazha, Ivan Yanev, and Simon Tanguay Alice Dragomir*Alice Dragomir* More articles by this author , Sara NazhaSara Nazha More articles by this author , Ivan YanevIvan Yanev More articles by this author , and Simon TanguaySimon Tanguay More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000932.012AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Kidney cancer is placed third in urologic cancers in Canada, right behind prostate and bladder cancer. Many new therapeutic options are being developed in the metastatic phase mainly, but these innovations are being presented with high costs. This is supported by the development of newer immune-therapies that constantly addresses an unmet need. The objective of the current study is thus to establish clinical and economic outcomes of the current practice in RCC treatment in Canada post-nephrectomy. METHODS: A Markov model with microsimulation was developed to estimate the cost of follow-up and treating patients from post-nephrectomy up to diagnosis of metastatic RCC and death from any cause. The model included 5 health states: Active Surveillance, Local recurrence, mRCC, death from RCC or death from other causes. Probabilities were adjusted by taking in consideration patient characteristics such as TNM staging and most estimate were extracted from real-world evidence studies assessing the survival of RCC and mRCC patients. Costs were extracted from available literature. Deterministic sensitivity analysis was conducted to account for uncertainty on different parameters by varying parameters by 25%. RESULTS: Mean survival (± SD) was evaluated to be 15.56 ± 5.69 life years (LYs) for T1 tumours, 13.22 ± 5.68 LY for T2, 12.22 ± 5.52 LY for T3 and 14.85 ± 5.71 LY for the weighted average of the 3 stages. The weighted mean and median total cost of the disease amounts to 107 811.22$ and 48 992.33$ respectively over a 20-year time horizon. In the weighted average scenario, the mRCC state costs represented the main burden, at around 40.3% of total cost. The local recurrence, active surveillance, death and kidney cancer related death states respectively represented 27.2%, 23.8%, 8.6% and 0.1%. CONCLUSIONS: The economic burden of mRCC is increasing with the severity of the disease. The results given in the present work constitute a groundwork for future studies that need to be done integrating newer treatment option in the management of RCC. Source of Funding: None © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e918-e918 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alice Dragomir* More articles by this author Sara Nazha More articles by this author Ivan Yanev More articles by this author Simon Tanguay More articles by this author Expand All 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 | 0,012 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».