Literature DB >> 25562444

Prostate cancer risk estimation tool use by members of the American Urological Association: a survey based study.

Brandon J Otto1, E Charles Osterberg1, Sanjay Salgado1, Douglas S Scherr1, Shahrokh F Shariat2.   

Abstract

PURPOSE: Prostate cancer risk estimation tools have been developed to help guide patients and physicians with clinical decision making across all disease states. We assessed use patterns of these tools using an online survey sent to AUA (American Urological Association) members.
MATERIALS AND METHODS: We distributed a 21-question online survey to 5,674 AUA members to query prostate cancer risk estimation tool use. The survey was divided into 4 categories, including 1) demographics, 2) prebiopsy risk assessment, 3) pretreatment risk assessment and 4) risk estimation tool use.
RESULTS: A total of 565 members (10%) responded to the online survey, of whom 31% reported using a risk estimation tool in the prebiopsy decision setting. Providers who spent more than 20 minutes counseling patients were more likely to use a risk estimation tool (OR 2.2, p <0.01). After the prostate cancer diagnosis 70% of providers used a risk estimation tools to guide treatment recommendations. The total time spent counseling a patient (greater than 30 minutes) and the number of years in practice (fewer than 10) predicted prostate cancer risk tool use (OR 2.4, p <0.01 and 3.4, p <0.01, respectively).
CONCLUSIONS: AUA respondents use risk estimation tools more frequently in the pretreatment setting than in the prebiopsy setting. The time spent counseling patients and the time since graduation from residency predicted the likelihood of using risk estimation tools.
Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  nomograms; physician's practice patterns; prostatic neoplasms; questionnaires; risk

Mesh:

Year:  2015        PMID: 25562444     DOI: 10.1016/j.juro.2014.12.090

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  3 in total

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Authors:  Boris Gershman; Paul Maroni; Jon C Tilburt; Robert J Volk; Badrinath Konety; Charles L Bennett; Alexander Kutikov; Marc C Smaldone; Victor Chen; Simon P Kim
Journal:  World J Urol       Date:  2019-01-22       Impact factor: 4.226

2.  Current beliefs and practice patterns among urologists regarding prostate magnetic resonance imaging and magnetic resonance-targeted biopsy.

Authors:  Akhil Muthigi; Abhinav Sidana; Arvin K George; Michael Kongnyuy; Mahir Maruf; Subin Valayil; Bradford J Wood; Peter A Pinto
Journal:  Urol Oncol       Date:  2016-10-12       Impact factor: 3.498

Review 3.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24
  3 in total

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