Titre officiel
Genetics Adviser: Evaluating a Digital Decision Support Tool for Genetic Results
Sommaire:
Les oncologues ont de plus en plus souvent recours au séquençage
génomique pour diagnostiquer et optimiser la prise en charge de leurs patients.
L’atout de cette technologie réside dans sa capacité à déceler le risque, pour
un patient, de développer des milliers d’affections ou de maladies actuelles et
futures. D’après les lignes directrices existantes, les médecins doivent
permettre aux patients de choisir les résultats qu’ils souhaitent recevoir
avant de prescrire les analyses. Il n’est pas possible de conseiller les
patients concernant les milliers de résultats possibles en raison d’une
expertise génomique et de ressources cliniques limitées. Si les aides à la
décision (AD) peuvent combler cette lacune, il n’existe cependant pas d’AD à
même d’orienter les décisions des patients concernant les résultats du
séquençage génomique. Un prototype d’AD a été mis au point
(GenomicsADvISER.com), le premier de ce type. Cette étude vise à faire du
prototype d’AD un outil numérique d’aide à la décision interactif, adaptable et
axé sur le patient (Genetics ADvISER) grâce à des méthodes de conception axées
sur l’utilisateur. L’objectif de cette étude est d’évaluer l’efficacité de l’outil
Genetics ADvISER dans le cadre d’un essai contrôlé avec répartition
aléatoire auprès de patients à qui l’on proposera des résultats de séquençage
génomique. Les résultats de cet essai permettront de déterminer si l’outil
Genetics ADvISER est efficace dans la pratique. Cela contribuerait à
combler une lacune importante en matière de soins cliniques, à améliorer les
résultats de santé et l’utilisation des services en réduisant la charge du
conseil ainsi que l’utilisation excessive, la sous-utilisation et l’utilisation
abusive de ces services, préoccupations des décideurs cherchant à atteindre le
triple objectif relatif aux soins de santé.
Description de l'essai
Primary Outcome:
- Decisional Conflict Scale (DCS)
Secondary Outcome:
- Knowledge
- Satisfaction with Decision Scale (SWD)
- Preparation for Decision Making scale (PrepDM)
- State-Trait Anxiety Inventory
- Hospital Anxiety and Depression Scale (HADS)
- Acceptability
- Time
BACKGROUND: Genomic sequencing (GS) is a driver of precision oncology. Oncologists are
increasingly using tumour GS for precision oncology care, which is often times accompanied by
germline GS on normal control tissue. One complex feature of this technology is its capacity
to generate incidental findings (IF). Guidelines recommend doctors inform patients of their
incidental GS results. Yet there are limited tools to communicate the scope and implications
of the thousands incidental results available to help guide patients' decisions about which
results they wish to learn.
RATIONALE: There are limited decision support tools in genetics. Despite the long-standing
practice of medical genetics, there are relatively few decision support tools for genetic
testing and very few that have been rigorously evaluated. Even fewer decision support tools
exist on possible results from genomic sequencing; existing tools target pediatric contexts,
focus on genomic sequencing education-only or on the return of results; they do not cover all
possible results with decision support to simulate genetic counselling, limiting their use
and applicability in clinical care. Thus, there are no decision support tools to guide
patients about all results available from genomic sequencing.
OBJECTIVES:
Evaluate the effectiveness of the Genetics ADvISER vs standard genetic counseling
(GC) with patients receiving incidental findings.
HYPOTHESIS: Use of the Genetics ADvISER will reduce patients' decisional conflict & anxiety,
improve patient knowledge, satisfaction with decisions and preparedness for decision-making
when selecting IF compared to GC alone.
PHASE 1: RCT to evaluate the Decision Aid
Methods: This is a mixed method, non-blinded randomized controlled superiority trial. We will
evaluate whether use of the Genetics ADvISER followed by Genetic Counsellor (GC) reduces
decisional conflict compared to GC alone in a RCT. As a part of this trial, patients will
receive results from exome sequencing.
Study population: Adult cancer patients who have had GS for their cancer (but did not receive
incidental findings) or adult patients who have had a negative genetic panel test and may
eligible for GS.
Sample: The primary outcome is decisional conflict; the study requires 64 patients/arm (128
total) to detect the minimal clinically important difference (MCID) of 0.3 using the
Decisional Conflict Scale (DCS), assuming a standard deviation of 0.6, an alpha of 0.05
(two-sided) and power of 0.8. Participants will be consecutively randomized and allocated
from an existing list of eligible subjects using a computer-generated randomization in a 1:1
ratio with random permuted blocks of varying sizes. Patients from each clinic will be
randomized separately to ensure we have an even distribution of this population in both arms
of the study.
Intervention: Participants in the intervention arm will use the Genetics ADviSER to learn
about GS, select which results they would like to receive and to receive their GS results.
Control: Participants in the control arm will speak with a genetic counsellor to learn about
GS, select which results they would like to receive and to receive their GS results.
Outcomes and measures: The primary outcome is decisional conflict, assessed via the validated
Decisional Conflict Scale (DCS) consistent with the ODSF.
Secondary outcomes: Knowledge, measured using an established questionnaire assessing benefits
and limitations of genome sequencing and a set of internally developed knowledge questions on
IF; Satisfaction with decision-making, measured using the Satisfaction with Decision scale
and the Preparation for Decision Making scale; Anxiety, measured using the state subscale of
the State-Trait Anxiety Inventory. All sessions will be recorded to assess the length of GC
sessions.
Quantitative Analysis: The analysis of outcomes will follow the intention-to-treat approach.
Mean scores for decisional conflict, satisfaction with and preparation for decision-making,
knowledge of IF and GC session length will be compared using a t-test. Anxiety, knowledge of
sequencing benefits and sequencing limitations scores will be assessed by summing the number
of correct responses to the questions, and compared adjusting for baseline score using
analysis of covariance (ANCOVA). The primary time points of comparison will be (T1) for the
control versus (T2) for the intervention group. Secondary exploratory analyses will examine
the impact that the decision aid had alone (T1), without the addition of follow-up GC at T2
and at T3, after participants have received their IF on decision conflict, knowledge,
anxiety, satisfaction and preparation with decision-making. Descriptive statistics will be
used to describe participants' demographic characteristics (age, sex, education, etc.).
PHASE 2: Qualitative study
This study will explore the utility the of the Genetics ADvISER and incidental results via
qualitative interviews with participants. After the study is completed, a subset set of
participants (n = 40) will be selected to participate qualitative portion of the study.
Participants approached to complete the qualitative portion of the study will determined by
purposeful sampling, in order to get mix of participants across a range of experiences and
demographic characteristics.
Qualitative Analysis: The qualitative analyses will draw on grounded theory. Open coding,
constant comparison and axial coding will be used to identify common and divergent themes to
characterize the entire dataset. Interviews will consider participants' socio-demographic
factors that may influence their informational and decisional needs as well as how they
engage with genetic information and participate in shared decision making. Two researchers
will code transcripts independently; consensus on codes will be reached through discussion.
Validation methods may include triangulation and member checking. In keeping with qualitative
methodology, data analysis will occur in conjunction with data collection. On-going analysis
will inform the development of progressive iterations of the interview guides.
Voir cet essai sur ClinicalTrials.gov
