Citations

Introduction: Relatively little is known about outcomes following return of secondary findings detected in large scale sequencing projects. We assessed 1-year outcomes following return of pathogenic/likely pathogenic (P/LP) variants associated with familial hypercholesterolemia (FH) in phase III of the eMERGE network (2015-2019). Methods: Adult participants (n=18,544) at 7 sites were enrolled in a prospective cohort study to study the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR , APOB , and PCSK9 . Process, intermediate, and/or clinical outcomes within 1-year after return of results (RoR) were ascertained by electronic health record (EHR) review. A process outcome was defined as referral to a specialist or ordering new tests, including lipid profile and screening tests for coronary heart disease. An intermediate outcome was defined as new diagnosis of FH or atherosclerotic cardiovascular disease. A clinical outcome was defined as initiation or change in lipid-lowering medication. The primary outcomes were a new diagnosis of FH and modification of lipid lowering therapy. Results: Results were returned to 134 individuals, and data for 119 participants were available for the outcomes analysis. Process (86%), intermediate (49%), and/or clinical (26%) outcomes occurred in 92% of participants. Process outcomes included 72% with new tests ordered and 61% referred to a specialist. Before RoR, 86.5% of individuals with FH associated P/LP variants had a diagnosis of hypercholesterolemia but only 17.6% had a diagnosis of FH; 89% were on a statin but only 58% were on a high intensity statin. Following RoR, 44% (52/119) received a new diagnosis of FH. Lipid lowering treatment was modified in 26% (31/119), including initiation of a statin in 5%, increasing statin dose in 13%, and adding ezetimibe or a PCSK9 inhibitor in 12.6%. Conclusions: In a multisite cohort of EHR-linked biobanks, monogenic FH was underdiagnosed (only 17.6% had a specific diagnosis) and undertreated (only 58% were on a high intensity statin); 44% of participants received a new diagnosis of FH and 26% had lipid lowering treatment modified after RoR. These results highlight the clinical utility of returning FH variants in EHR-linked biobanks.

Background: The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. Methods: Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR , APOB , and PCSK9 . FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. Results: The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00–4.53]) and premature CHD (odds ratio, 3.68 [2.34–5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. Conclusions: In a multisite cohort of electronic health record–linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record–linked biobanks to detect FH.

Background: Polygenic risk testing examines variation across multiple genes to estimate a risk score for a particular disease, including risk scores for many common, chronic health conditions. Although polygenic risk information (PRI) may be a promising tool for enhancing preventive counseling and facilitating early identification of disease, its potential impact on primary-care encounters and disease prevention efforts has not been well characterized. Methods: We conducted in-depth, semi-structured interviews of patients to assess their understandings of PRI and their beliefs about its relevance to disease prevention. Results: We completed interviews with 19 participants. Participants described enthusiasm for the generation of PRI and recognized its utility for disease prevention. Participants also described the value of PRI as limited if not corroborated by non-genetic risk factors. Finally, participants noted that PRI, by itself, would be insufficient as a trigger for initiating many preventive interventions. Conclusion: PRI has the potential to become an important tool in primary care. However, patient views about PRI as well as the complexities of disease prevention in the primary care context may limit the impact of PRI on disease prevention.

AbstractAlpha-synucleinopathies can be identified in their prodromal phase, raising several ethical issues. In this review, we first provide definitions of prodromal α-synucleinopathies and discuss the importance of distinguishing between prodromes and risk factors. Next, we discuss the implications of a diagnosis of prodromal α-synucleinopathy and considerations regarding prognostic counseling in both clinical and research settings. We review available data on patient preferences regarding disclosure as well as providers' perspectives. We examine the pros and cons of disclosing a diagnosis of prodromal α-synucleinopathy, taking into consideration the differences between clinical and research settings. Asking about willingness to know in clinical and research settings and the shared decision-making process applied to prognostic counseling is discussed. Concerning research settings, ethical aspects regarding clinical trials are addressed. Availability of direct-to-consumer technologies will likely lead to novel contexts requiring prognostic counseling, and future neuroprotective or neuromodulating treatments may require further considerations on the timing, role, and importance of prognostic counseling. Recommendations on how to address ethical gaps should be a priority for patients, medical professional societies, and research workgroups. Ethical issues must be considered as an integral part of the overall clinical and research approach to prodromal synucleinopathies.

Abstract Study Objectives Isolated REM sleep behavior disorder (iRBD) carries a high lifetime risk for phenoconversion to a defined neurodegenerative disease (NDD) including Parkinson disease, dementia with Lewy bodies, and multiple system atrophy. We aimed to examine iRBD patient values and preferences regarding prognostic counseling. Methods One hundred thirteen iRBD patient participants enrolled in the Mayo Clinic iRBD Patient Registry were sent an email survey concerning their values and preferences concerning NDD prognostic counseling and their experiences following diagnosis with iRBD. Results Of 81 respondents (71.7% response rate), the majority were men (74.0%) with an average age of 65.7 (±9.7) years. Responses indicated a strong preference toward receiving prognostic information about possible future NDD development. 92.5% of respondents felt knowledge concerning personal NDD risk was important, while 87.6% indicated prognostic discussions were important to maintaining trust in their physician. 95.7% indicated a desire for more information, while only 4.3% desired less information regarding their NDD prognostic risk. Most respondents strongly agreed that prognostic information was important to discuss with their family and friends and inform future life planning, and most expressed interest in learning more about future neuroprotective therapies and symptomatic treatments for parkinsonism and dementia. Conclusions Most iRBD patients indicated strong preferences for disclosure of NDD prognostic risk and indicated that prognostic information was important for family discussions and future life planning. Future broader surveys and qualitative studies of clinic-based and ultimately community dwelling iRBD patients’ values and preferences are needed to guide appropriately tailored and individualized prognostic counseling approaches following iRBD diagnosis.

Abstract Objective Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES. Materials and Methods This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES. Results Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2–Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria). Discussion Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias. Conclusion The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.

Background While use of artificial intelligence (AI) in healthcare is increasing, little is known about how patients view healthcare AI. Characterizing patient attitudes and beliefs about healthcare AI and the factors that lead to these attitudes can help ensure patient values are in close alignment with the implementation of these new technologies. Methods We conducted 15 focus groups with adult patients who had a recent primary care visit at a large academic health center. Using modified grounded theory, focus-group data was analyzed for themes related to the formation of attitudes and beliefs about healthcare AI. Results When evaluating AI in healthcare, we found that patients draw on a variety of factors to contextualize these new technologies including previous experiences of illness, interactions with health systems and established health technologies, comfort with other information technology, and other personal experiences. We found that these experiences informed normative and cultural beliefs about the values and goals of healthcare technologies that patients applied when engaging with AI. The results of this study form the basis for a theoretical framework for understanding patient orientation to applications of AI in healthcare, highlighting a number of specific social, health, and technological experiences that will likely shape patient opinions about future healthcare AI applications. Conclusions Understanding the basis of patient attitudes and beliefs about healthcare AI is a crucial first step in effective patient engagement and education. The theoretical framework we present provides a foundation for future studies examining patient opinions about applications of AI in healthcare.