Acceptability, Appropriateness, and Feasibility of Automated Screening Approaches and Family Communication Methods for Identification of Familial Hypercholesterolemia: Stakeholder Engagement Results from the IMPACT-FH Study

Facilitating family communication of familial hypercholesterolemia genetic risk: Assessing engagement with innovative chatbot technology from the IMPACT-FH study

Objective

To assess use of two web-based conversational agents, the Family Sharing Chatbot (FSC) and One Month Chatbot (OMC), by individuals with familial hypercholesterolemia (FH).

Methods

FSC and OMC were sent using an opt-out methodology to a cohort of individuals receiving a FH genetic result. Data from 7/1/2021 through 5/12/2022 was obtained from the electronic health record and the chatbots' HIPAA-secure web portal.

Results

Of 175 subjects, 21 (12%) opted out of the chatbots. Older individuals were more likely to opt out. Most (91/154, 59%) preferred receiving chatbots via the patient EHR portal. Seventy-five individuals (49%) clicked the FSC link, 62 (40%) interacted, and 36 (23%) shared a chatbot about their FH result with at least one relative. Ninety-two of the subjects received OMC, 22 (23%) clicked the link and 20 (21%) interacted. Individuals who shared were majority female and younger on average than the overall cohort. Reminders tended to increase engagement.

Conclusion

Results demonstrate characteristics relevant to chatbot engagement. Individuals may be more inclined to receive chatbots if integrated within the patient EHR portal. Frequent reminders can potentially improve chatbot utilization.

Innovation

FSC and OMC employ innovative digital health technology that can facilitate family communication about hereditary conditions.

International Atherosclerosis Society guidance for implementing best practice in the care of familial hypercholesterolaemia

This contemporary, international, evidence-informed guidance aims to achieve the greatest good for the greatest number of people with familial hypercholesterolaemia (FH) across different countries. FH, a family of monogenic defects in the hepatic LDL clearance pathway, is a preventable cause of premature coronary artery disease and death. Worldwide, 35 million people have FH, but most remain undiagnosed or undertreated. Current FH care is guided by a useful and diverse group of evidence-based guidelines, with some primarily directed at cholesterol management and some that are country-specific. However, none of these guidelines provides a comprehensive overview of FH care that includes both the lifelong components of clinical practice and strategies for implementation. Therefore, a group of international experts systematically developed this guidance to compile clinical strategies from existing evidence-based guidelines for the detection (screening, diagnosis, genetic testing and counselling) and management (risk stratification, treatment of adults or children with heterozygous or homozygous FH, therapy during pregnancy and use of apheresis) of patients with FH, update evidence-informed clinical recommendations, and develop and integrate consensus-based implementation strategies at the patient, provider and health-care system levels, with the aim of maximizing the potential benefit for at-risk patients and their families worldwide.

Applications of machine learning in familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.

Designing implementation strategies to improve identification, cascade testing, and management of families with familial hypercholesterolemia: An intervention mapping approach

Introduction

Familial hypercholesterolemia (FH) is a common inherited cholesterol disorder that, without early intervention, leads to premature cardiovascular disease. Multilevel strategies that target all components of FH care including identification, cascade testing, and management are needed to address gaps that exist in FH care. We utilized intervention mapping, a systematic implementation science approach, to identify and match strategies to existing barriers and develop programs to improve FH care.

Methods

Data were collected utilizing two methods: a scoping review of published literature, related to any component of FH care, and a parallel mixed method study using interviews and surveys. The scientific literature was searched using key words including "barriers" or "facilitators" and "familial hypercholesterolemia" from inception to December 1, 2021. The parallel mixed method study recruited individuals and families with FH to participate in either dyadic interviews (N = 11 dyads/22 individuals) or online surveys (N = 98 respondents). Data generated from the scoping review, dyadic interviews, and online surveys were used in the 6-step intervention mapping process. Steps 1-3 included a needs assessment, development of program outcomes and creation of evidence-based implementation strategies. Steps 4-6 included program development, implementation, and evaluation of implementation strategies.

Results

In steps 1-3, a needs assessment found barriers to FH care included underdiagnosis of the condition which led to suboptimal management due to a myriad of determinants including knowledge gaps, negative attitudes, and risk misperceptions by individuals with FH and clinicians. Literature review highlighted barriers to FH care at the health system level, notably the relative lack of genetic testing resources and infrastructure needed to support FH diagnosis and treatment. Examples of strategies to overcome identified barriers included development of multidisciplinary care teams and educational programs. In steps 4-6, an NHLBI-funded study, the Collaborative Approach to Reach Everyone with FH (CARE-FH), deployed strategies that focused on improving identification of FH in primary care settings. The CARE-FH study is used as an example to describe program development, implementation, and evaluation techniques of implementation strategies.

Conclusion

The development and deployment of evidence-based implementation strategies that address barriers to FH care are important next steps to improve identification, cascade testing, and management.

Genetic Testing for Familial Hypercholesterolemia in Clinical Practice

PURPOSE OF REVIEW

Genetic testing has proven utility in identifying and diagnosing individuals with FH. Here we outline the current landscape of genetic testing for FH, recommendations for testing practices and the efforts underway to improve access, availability, and uptake.

RECENT FINDINGS

Alternatives to the traditional genetic testing and counseling paradigm for FH are being explored including expanding screening programs, testing in primary care and/or cardiology clinics, leveraging electronic communication tools like chatbots, and implementing direct contact approaches to facilitate genetic testing of both probands and at-risk relatives. There is no consensus on if, when, and how genetic testing or accompanying genetic counseling should be provided for FH, though traditional genetic counseling and/or testing in specialty lipid clinics is often recommended in expert statements and professional guidelines. More evidence is needed to determine whether alternative approaches to the implementation of genetic testing for FH may be more effective.

Optimizing communication strategies and designing a comprehensive program to facilitate cascade testing for familial hypercholesterolemia

BACKGROUND

This project aimed to optimize communication strategies to support family communication about familial hypercholesterolemia (FH) and improve cascade testing uptake among at-risk relatives. Individuals and families with FH provided feedback on multiple strategies including: a family letter, digital tools, and direct contact.

METHODS

Feedback from participants was collected via dyadic interviews (n = 11) and surveys (n = 98) on communication strategies and their proposed implementation to improve cascade testing uptake. We conducted a thematic analysis to identify how to optimize each strategy. We categorized optimizations and their implementation within the project's healthcare system using a Traffic Light approach.

RESULTS

Thematic analysis resulted in four distinct suggested optimizations for each communication strategy and seven suggested optimizations that were suitable across all strategies. Four suggestions for developing a comprehensive cascade testing program, which would offer all optimized communication strategies also emerged. All optimized suggestions coded green (n = 21) were incorporated. Suggestions coded yellow (n = 12) were partially incorporated. Only two suggestions were coded red and could not be incorporated.

CONCLUSIONS

This project demonstrates how to collect and analyze stakeholder feedback for program design. We identified feasible suggested optimizations, resulting in communication strategies that are patient-informed and patient-centered. Optimized strategies were implemented in a comprehensive cascade testing program.

How Can Implementation Science Improve the Care of Familial Hypercholesterolaemia?

PURPOSE OF REVIEW

Describe the application of implementation science to improve the detection and management of familial hypercholesterolaemia.

RECENT FINDINGS

Gaps between evidence and practice, such as underutilization of genetic testing, family cascade testing, failure to achieve LDL-cholesterol goals and low levels of knowledge and awareness, have been identified through clinical registry analyses and clinician surveys. Implementation science theories, models and frameworks have been applied to assess barriers and enablers in the literature specific to local contextual factors (e.g. stages of life). The effect of implementation strategies to overcome these factors has been evaluated; for example, automated identification of individuals with FH or training and education to improve statin adherence. Clinical registries were identified as a key infrastructure to monitor, evaluate and sustain improvements in care. The expansion in evidence supporting the care of familial hypercholesterolaemia requires a similar expansion of efforts to translate new knowledge into clinical practice.

Use of a chatbot to increase uptake of cascade genetic testing

Successful proband-mediated family communication and subsequent cascade genetic testing uptake requires interventions that present information clearly, in sufficient detail, and with medical authority. To facilitate family communication for patients receiving clinically actionable results via the MyCode® Community Health Initiative, a Family Sharing Tool (FST) and a cascade chatbot were developed. FST is an electronic mechanism allowing patients to share genetic test results with relatives via chatbot. The cascade chatbot describes the proband's result, associated disease risks, and recommended management and captures whether the user is a blood relative or caregiver, sex, and relationship to the proband. FST and cascade chatbot uptake among MyCode® probands and relatives was tracked from August 2018 through February 2020. Cascade genetic testing uptake was collected from testing laboratories as number of cascades per proband. Fifty-eight percent (316/543) of probands consented to FST; 42% (227/543) declined. Receipt preferences were patient electronic health record (EHR) portal (52%), email (29%), and text (19%). Patient EHR portal users (p < 0.001) and younger patients were more likely to consent (p < 0.001). FST was deployed to 308 probands. Fifty-nine percent (183/308) opened; of those, 56% (102/183) used FST to send a cascade chatbot to relatives. These 102 probands shared a cascade chatbot with 377 relatives. Sixty-two percent (235/377) of relatives opened; of these, 69% (161/235) started, and of these, 57% (92/161) completed the cascade chatbot. Cascade genetic testing uptake was significantly greater among relatives of probands who consented to the FST (M = 2.34 cascades, SD = 2.10) than relatives of probands who declined (M = 1.40 cascades, SD = 0.82, p < 0.001). Proband age was not a significant predictor of cascade genetic testing uptake. Further work is needed to better understand factors impacting proband use of FST and relative use of cascade chatbots.

Motivating cascade testing for familial hypercholesterolemia: applying the extended parallel process model for clinician communication

Motivating at-risk relatives to undergo cascade testing for familial hypercholesterolemia (FH) is critical for diagnosis and lifesaving treatment. As credible sources of information, clinicians can assist in family communication about FH and motivate cascade testing uptake. However, there are no guidelines regarding how clinicians should effectively communicate with probands (the first person diagnosed in the family) and at-risk relatives. Individuals and families with FH can inform our understanding of the most effective communications to promote cascade testing. Guided by the extended parallel process model (EPPM), we analyzed the perspectives of individuals and families with FH for effective messaging clinicians can use to promote cascade testing uptake. We analyzed narrative data from interviews and surveys collected as part of a larger mixed-methods study. The EPPM was used to identify message features recommended by individuals and families with FH that focus on four key constructs (severity, susceptibility, response efficacy, self-efficacy) to promote cascade testing. Participants included 22 individuals from 11 dyadic interviews and 98 survey respondents. Participants described prioritizing multiple messages that address each EPPM construct to alert relatives about their risk. They illustrated strategies clinicians could use within each EPPM construct to communicate to at-risk relatives about the importance of pursuing diagnosis via cascade testing and subsequent treatment for high cholesterol due to FH. Findings provide guidance on effective messaging to motivate cascade testing uptake for FH and demonstrates how the EPPM may guide communication with at-risk relatives about genetic risk and motivate cascade testing broadly.

Applying implementation science to improve care for familial hypercholesterolemia

PURPOSE OF REVIEW

Improving care of individuals with familial hypercholesteremia (FH) is reliant on the synthesis of evidence-based guidelines and their subsequent implementation into clinical care. This review describes implementation strategies, defined as methods to improve translation of evidence into FH care, that have been mapped to strategies from the Expert Recommendations for Implementing Change (ERIC) compilation.

RECENT FINDINGS

A search using the term 'familial hypercholesterolemia' returned 1350 articles from November 2018 to July 2021. Among these, there were 153 articles related to improving FH care; 1156 were excluded and the remaining 37 were mapped to the ERIC compilation of strategies: assess for readiness and identify barriers and facilitators [9], develop and organize quality monitoring systems [14], create new clinical teams [2], facilitate relay of clinical data to providers [4], and involve patients and family members [8]. There were only 8 of 37 studies that utilized an implementation science theory, model, or framework and two that explicitly addressed health disparities or equity.

SUMMARY

The mapping of the studies to implementation strategies from the ERIC compilation provides a framework for organizing current strategies to improve FH care. This study identifies potential areas for the development of implementation strategies to target unaddressed aspects of FH care.

Patient Experiences Align with The Familial Hypercholesterolemia Global Call to Action

Objective

To explore alignment of perspectives from individuals and families with familial hypercholesterolemia (FH) to the FH Global Call to Action recommendations.

Methods

Interviews and focus groups were conducted with individuals and families with FH from multiple U.S. health systems and the Family Heart Foundation community to capture lived experiences and to identify barriers to diagnosis, cascade testing, and treatment. Participant perspectives were examined and classified, according to their alignment to recommendations of the FH Global Call to Action.

Results

A total of 75 lived experiences were analyzed. Participants were majority female, mostly white, older, and well-educated. Participants most frequently mentioned recommendations were family-based care (84%) and screening, testing, & diagnosis (84%), followed by treatment (69%), advocacy (60%), cost & value (59%), awareness (56%), research & registries (43%), and severe & homozygous FH (11%). An average of 4.65 (SD 1.76) recommendations were mentioned.

Conclusions

The FH Global Call to Action was driven by the persistent unmet needs of those living with FH in receiving a timely diagnosis, appropriate care, and support to prevent early morbidity and mortality. Patient- and family-centric perspectives suggest the FH Global Call to Action captures these concerns. Acting on recommendations, particularly improvements in screening and family-based care, will address patient, and public health, concerns.