PD45-12 ECONOMIC EVALUATION OF RENAL CELL CARCINOMA (RCC) IN CANADA USING REAL-WORLD EVIDENCE; A HEALTHCARE SYSTEM PERSPECTIVE
Bibliographic record
Abstract
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 ...
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".