Evidence map›Paper›PMID 34972513›Full record

ArticleBMC medical informatics and decision making2021

A personalized decision aid for prostate cancer shared decision making.

Hilary P Bagshaw, Alejandro Martinez, Nastaran Heidari, David Scheinker, Alan Pollack, Radka Stoyanova, Eric Horwitz, Gerard Morton, Amar U Kishan, Mark K Buyyounouski

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Hilary P BagshawStanford University Radiation Oncology, Stanford, CA, USA. hbagshaw@stanford.edu.
Alejandro MartinezSchool of Engineering, Stanford University, Stanford, CA, USA.
Nastaran HeidariStanford University Radiation Oncology, Stanford, CA, USA.
David ScheinkerSchool of Engineering, Stanford University, Stanford, CA, USA.
Alan PollackUniversity of Miami Sylvester Comprehensive Cancer Center, University of Miami Health System, Miami, FL, USA.
Radka StoyanovaUniversity of Miami Sylvester Comprehensive Cancer Center, University of Miami Health System, Miami, FL, USA.
Eric HorwitzFox Chase Cancer Center, Philadelphia, PA, USA.
Gerard MortonSunnybrook Odette Cancer Centre, Toronto, ON, Canada.
Amar U KishanUniversity of California, Los Angeles, Los Angeles, CA, USA.
Mark K BuyyounouskiStanford University Radiation Oncology, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA shared decision-making model is preferred for engaging prostate cancer patients in treatment decisions. However, the process of assessing an individual's preferences and values is challenging and not formalized. The purpose of this study is to develop an automated decision aid for patient-centric treatment decision-making using decision analysis, preference thresholds and value elicitations to maximize the compatibility between a patient's treatment expectations and outcome.

methodsA template for patient-centric medical decision-making was constructed. The inputs included prostate cancer risk group, pre-treatment health state, treatment alternatives (primarily focused on radiation in this model), side effects (erectile dysfunction, urinary incontinence, nocturia and bowel incontinence), and treatment success (5-year freedom from biochemical failure). A linear additive value function was used to combine the values for each attribute (side effects, success and the alternatives) into a value for all prospects. The patient-reported toxicity probabilities were derived from phase II and III trials. The probabilities are conditioned on the starting state for each of the side effects. Toxicity matrices for erectile dysfunction, urinary incontinence, nocturia and bowel incontinence were created for the treatment alternatives. Toxicity probability thresholds were obtained by identifying the patient's maximum acceptable threshold for each of the side effects. Results are represented as a visual. R and Rstudio were used to perform analyses, and R Shiny for application creation.

resultsWe developed a web-based decision aid. Based on preliminary use of the application, every treatment alternative could be the best choice for a decision maker with a particular set of preferences. This result implies that no treatment has determinist dominance over the remaining treatments and that a preference-based approach can help patients through their decision-making process, potentially affecting compliance with treatment, tolerance of side effects and satisfaction with the decision.

conclusionsWe present a unique patient-centric prostate cancer treatment decision aid that systematically assesses and incorporates a patient's preferences and values to rank treatment options by likelihood of achieving the preferred outcome. This application enables the practice and study of personalized medicine. This model can be expanded to include additional inputs, such as genomics, as well as competing, concurrent or sequential therapies.

Indexed as

Decision Making, SharedProstatic NeoplasmsDecision MakingDecision Support TechniquesGenomicsHumansMalePatient ParticipationPrecision MedicineDecision aidPersonalized medicineProstate cancerRadiation therapy preference based decisionsShared decision making

Identifiers

PMID34972513
PMCPMC8720209

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.