Stephen R Thompson1, Geoff P Delaney2, Susannah Jacob3, Jesmin Shafiq3, Karen Wong3, Timothy P Hanna4, Gabriel S Gabriel3, Michael B Barton3. 1. Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Ingham Institute for Applied Medical Research, Liverpool Hospital, UNSW, Sydney, Australia; Department of Radiation Oncology, Prince of Wales Hospital, Sydney, Australia; University of New South Wales, Sydney, Australia. Electronic address: stephen.thompson@sesiahs.health.nsw.gov.au. 2. Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Ingham Institute for Applied Medical Research, Liverpool Hospital, UNSW, Sydney, Australia; University of New South Wales, Sydney, Australia; University of Western Sydney, Australia. 3. Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Ingham Institute for Applied Medical Research, Liverpool Hospital, UNSW, Sydney, Australia; University of New South Wales, Sydney, Australia. 4. Division of Cancer Care and Epidemiology, Queen's University Cancer Research Institute, Kingston, Canada.
Abstract
BACKGROUND AND PURPOSE: We aimed to construct an evidence-based model of optimal treatment utilisation for prostate cancer, incorporating all local treatment modalities: radical prostatectomy (RP), external beam radiotherapy (EBRT), and brachytherapy (BT); and then to compare this optimal model with actual practice. MATERIALS AND METHODS: Evidence-based guidelines were used to construct a prostate cancer treatment decision-tree. The proportion of patients who fulfilled treatment criteria was drawn from the epidemiological literature. These data were combined to calculate the overall proportion of patients that should optimally have RP, EBRT and/or BT at least once during the course of their disease. The model was peer reviewed and tested by sensitivity analyses and compared with actual practice. RESULTS: Optimal utilisation rates, at some point during the disease course, were: RP, 24% (range 15-30%); EBRT, 58% (range 54-64%); BT, 9.6% (range 6.0-17.9%); and any RT, 60% (range 56-66%). Many patients had indications for more than one of these treatments, and at least one of these treatments was indicated in 76% of patients. The model was sensitive to patient preference estimates. Optimal rates were achievable in some health care jurisdictions. CONCLUSIONS: Modelling optimal utilisation of all local treatment options for a particular cancer is possible. These optimal prostate cancer treatment rates can be used as a planning and quality assurance tool, providing an evidence-based benchmark against which can be measured patterns of practice.
BACKGROUND AND PURPOSE: We aimed to construct an evidence-based model of optimal treatment utilisation for prostate cancer, incorporating all local treatment modalities: radical prostatectomy (RP), external beam radiotherapy (EBRT), and brachytherapy (BT); and then to compare this optimal model with actual practice. MATERIALS AND METHODS: Evidence-based guidelines were used to construct a prostate cancer treatment decision-tree. The proportion of patients who fulfilled treatment criteria was drawn from the epidemiological literature. These data were combined to calculate the overall proportion of patients that should optimally have RP, EBRT and/or BT at least once during the course of their disease. The model was peer reviewed and tested by sensitivity analyses and compared with actual practice. RESULTS: Optimal utilisation rates, at some point during the disease course, were: RP, 24% (range 15-30%); EBRT, 58% (range 54-64%); BT, 9.6% (range 6.0-17.9%); and any RT, 60% (range 56-66%). Many patients had indications for more than one of these treatments, and at least one of these treatments was indicated in 76% of patients. The model was sensitive to patient preference estimates. Optimal rates were achievable in some health care jurisdictions. CONCLUSIONS: Modelling optimal utilisation of all local treatment options for a particular cancer is possible. These optimal prostate cancer treatment rates can be used as a planning and quality assurance tool, providing an evidence-based benchmark against which can be measured patterns of practice.
Authors: Subas Neupane; Jaakko Nevalainen; Jani Raitanen; Kirsi Talala; Paula Kujala; Kimmo Taari; Teuvo L J Tammela; Ewout W Steyerberg; Anssi Auvinen Journal: Cancers (Basel) Date: 2021-01-24 Impact factor: 6.639
Authors: Michael Yan; Andre G Gouveia; Fabio L Cury; Nikitha Moideen; Vanessa F Bratti; Horacio Patrocinio; Alejandro Berlin; Lucas C Mendez; Fabio Y Moraes Journal: Nat Rev Urol Date: 2021-08-13 Impact factor: 14.432