Ignite Creation Date:
2024-05-06 @ 10:24 AM
Last Modification Date:
2024-10-26 @ 12:29 PM
Study NCT ID:
NCT03244202
Status:
COMPLETED
Last Update Posted:
2018-04-17
First Post:
2017-08-06
Brief Title:
Evaluation of a Decision Aid for Incidental Genomic Findings
Sponsor:
Unity Health Toronto
Organization:
Unity Health Toronto
Study Overview
Official Title:
Randomized Controlled Trial of a Decision Aid for Incidental Genomic Findings
Status:
COMPLETED
Status Verified Date:
2018-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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 n40 will explore patients preferences for the types of incidental results they wish to receive and their decision making process
Detailed Description:
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 DAs usability with 15 patients in 2 rounds Interviews demonstrated strong face validity and content comprehension Most patients found the amount of information just right 1115 clear 1215 and balanced 1415 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 ie tested negative for the classic gene mutation associated with their cancer - eg BRCA12 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 metastaticrecurrent disease as incidental results are less consequential to this population
Sample size TThe primary outcome is decisional conflict the study requires 64 patientsarm to detect the minimal clinically important difference MCID of 03 using the Decisional Conflict Scale DCS Appendix 3 assuming a standard deviation of 06 an alpha of 005 two-sided and power of 081516 In the last 3 months 244 patients with a family history of breast and colon cancer tested negative for their associated classic mutations BRCA12 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 11 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 eg usage statistics duration of counseling sessions demographic and clinical characteristics eg 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 Unadjusted 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 themespatterns 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
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 DAs usability with 15 patients in 2 rounds Interviews demonstrated strong face validity and content comprehension Most patients found the amount of information just right 1115 clear 1215 and balanced 1415 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 ie tested negative for the classic gene mutation associated with their cancer - eg BRCA12 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 metastaticrecurrent disease as incidental results are less consequential to this population
Sample size TThe primary outcome is decisional conflict the study requires 64 patientsarm to detect the minimal clinically important difference MCID of 03 using the Decisional Conflict Scale DCS Appendix 3 assuming a standard deviation of 06 an alpha of 005 two-sided and power of 081516 In the last 3 months 244 patients with a family history of breast and colon cancer tested negative for their associated classic mutations BRCA12 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 11 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 eg usage statistics duration of counseling sessions demographic and clinical characteristics eg 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 Unadjusted 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 themespatterns 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None