Cancer Clinical Trial
Official title:
Randomized Controlled Trial of a Decision Aid for Incidental Genomic Findings
Health care providers (HCP) are increasingly using genomic sequencing (GS) to target treatment for patients. However, GS may incidentally reveal inherited risks for thousands of current and future diseases. Guidelines recommend HCP inform patients of incidental GS results. No decision aid (DA) exists to guide patients' decisions about which incidental GS results they wish to learn. This study will evaluate whether the DA followed by genetic counselling (GC) reduces decisional conflict compared to GC alone in a randomized controlled trial (RCT) with 128 patients with a family history of cancer, who have had a negative genetic test and may eligible for GS. A qualitative component with a subset of participants (n=40) will explore patients' preferences for the types of incidental results they wish to receive and their decision making process.
BACKGROUND: Health care providers are increasingly using GS to diagnose, prognose and treat
diseases. GS offers increased sensitivity over classic genetic tests, decreasing
time-consuming and costly diagnostic cascades. However, GS may also incidentally reveal
inherited risks for many other cancers and diseases. 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.
Gaps: Decision aids (DAs) are best suited to meet this challenge, but no DA exists to guide
patients' decisions about incidental GS results.
Rationale: It is not feasible to counsel patients on the thousands of incidental findings
available to make informed choices about which incidental results they wish to receive
because of the limited genomics expertise and capacity among oncologists, and the long wait
times for genetic counseling. Our DA fills this critical care and translational gap by
improving the quality of patients' decisions and saving oncologists time counseling patients
on incidental findings.
Preliminary data: 1) DA development: We created an interactive online DA. It begins with a
professional whiteboard video (by Dr. Mike Evans) that conveys the key concepts, risks and
benefits of learning about incidental GS results to educate patients. It then prepares
patients for decision-making using a values clarification exercise (with feedback of their
preferences) and a knowledge questionnaire (with correct answers provided after). It ends by
asking participants to select result categories they want to learn using a menu tool. 2)
Usability testing: We also evaluated the DA's usability with 15 patients in 2 rounds.
Interviews demonstrated strong face validity and content comprehension. Most patients found
the amount of information 'just right' (11/15), clear (12/15) and balanced (14/15). All
patients felt that the information was sufficient to reach a decision, that the DA was easy
to use and would recommend it.
OBJECTIVES
1. Evaluate the efficacy of the DA compared to standard genetic counseling (GC)
2. Understand the decision-making patients' use regarding GS and selecting incidental
findings.
METHODS
Phase 1 - RCT to evaluate the DA:
Methods: We will evaluate the efficacy of the DA in reducing decisional conflict compared to
standard genetic counseling (GC) using a superiority trial.
Population: We will recruit adult cancer patients who are eligible to have GS (i.e., tested
negative for the classic gene mutation associated with their cancer - e.g., BRCA1/2, MLH,
MSH, PMS, APC, MUTYH) from genetics clinics at Mount Sinai Hospital, Princess Margaret
Hospital and Sunnybrook Hospital in Toronto, ON Canada. We will include adults who speak and
read English and exclude patients with metastatic/recurrent disease as incidental results are
less consequential to this population.
Sample size: TThe primary outcome is decisional conflict; the study requires 64 patients/arm
to detect the minimal clinically important difference (MCID) of 0.3 using the Decisional
Conflict Scale (DCS) (Appendix 3), assuming a standard deviation of 0.6, an alpha of 0.05
(two-sided) and power of 0.815,16. In the last 3 months, 244 patients with a family history
of breast and colon cancer tested negative for their associated classic mutations (BRCA1/2,
MLH, MSH, PMS, APC, MUTYH) most of who would be eligible for GS. Extrapolating this over the
next 9 months we estimate that there would be 732 eligible patients. It is highly feasible to
reach our target of 128 patients.
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 arm: Participants will view the online DA and then complete the online
self-administered measures (below) in one sitting within 14 days of recruitment. Next, they
will speak with a GC over the telephone after the DA, using a standardized script. They will
then complete the same online measures again after speaking to the GC.
Control arm: The GC will conduct the GC session over the telephone within 14 days of
recruitment. A topics script will be used to standardize GC discussions covering standard
educational content to enable patients to select incidental GS results (participants will not
view the DA nor the video). Participants will complete the online self-administered measures
after speaking with the GC.
Outcome: Consistent with the Ottawa Decisional Support Framework, our primary outcome is
decisional conflict. Secondary outcomes are: knowledge of GS, satisfaction with decision,
preparation for decision-making and anxiety.
Measures: We will use validated scales to assess decisional conflict, knowledge, anxiety,
satisfaction with the decision and preparation for decision-making. We will develop a
standardized topic script for the GC in each arm, as well as a questionnaire to collect
intervention fidelity (e.g., usage statistics, duration of counseling sessions), demographic
and clinical characteristics (e.g., cancer status and genetic testing).
Analysis: Consistent with the Ottawa Decision Support Framework, our primary outcome is
decisional conflict, assessed via the validated Decisional Conflict Scale (DCS). Knowledge is
the secondary outcome, will be measured by the Cliseq genomic sequencing questionnaire and a
set of internal developed knowledge questions. Satisfaction and anxiety with also be
assessed. Satisfaction will be measured using the Satisfaction with Decision scale (SWD) and
the Preparation for Decision Making scale (PrepDM). Anxiety will be measured using the state
subscale of the State-Trait Anxiety Inventory (STAI). We will also include a demographics and
cancer history questionnaire.
The analysis of outcomes will follow the intention-to-treat (ITT) approach. Mean DCS, SWD,
PrepDM and STAI scores will be compared using a t-test. Knowledge scores will be assessed by
summing the number of correct responses to the questions, and compared using t-tests. Linear
regression will be used in a secondary analysis to account for known predictors for
decisional outcomes such as education. Secondary analyses will compare the mean DCS,
knowledge, SWD, PrepDM and STAI scores before and after GC in the intervention arm to explore
the additional benefit of GC after the DA. Un/adjusted mean differences and 95% confidence
intervals will be reported. We will use descriptive statistics to report participants'
characteristics.
Phase 2 - qualitative study of decision making for incidental results:
A subset of study participants will be asked to take part in a qualitative interview about
their decision-making regarding selecting incidental findings. These semi-structured
interviews will take place over the phone with a total of 40 participants. For the
qualitative component a purposeful sample of study participants will be used. We will target
a mix of participants across ages, cancer type and stage, gender, and study group to assess
the varying approaches to decision-making. At total of 40 participants will take part in the
qualitative component.
Analysis: Qualitative data analysis will draw on grounded theory methodology. We will sort
the data by searching for themes/patterns and variations within and across interviews using
HypeRESEARCH. Coding, which is the first stage in the analysis process, will involve
'labeling' the data with descriptive codes. Two team members will independently code each
transcript. Consensus on coding will be reached through comparison and discussion among these
members. The second stage will involve constant comparison, where codes and their content
will be compared across interviews to discern common and divergent themes and issues across
them. The final stage is selective coding, which integrates all the codes under a central
phenomenon to build a theory. Validation methods include triangulation and member checking.
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