Genetics Adviser: Evaluating a Digital Decision Support Tool for Genetic Results

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

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