Sharp, Richard R

Abstract Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient‐specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health‐system‐wide, mixed‐methods study. Clinicians representing multiple practices and care settings ( N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient‐specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0–100 points) of each alert, the perceived impact of each alert on stress and decision‐making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision‐making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician‐guided design of PGx alerts in the era of digital medicine.

Background/Objectives: Multi-gene, multi-cancer, hereditary cancer risk screenings may be useful in cancer prevention and treatment, not only for cancer patients but also for patients’ family members. If genetic cancer screening is to have the widest possible benefit, it must be extended into diverse cancer care settings that serve diverse patient communities, providing cancer patients and their relatives with individualized cancer risk evaluations. Little research, to date, has examined the impact of extending multigenic cancer screening into diverse settings. Without empirical data characterizing the support needs of cancer patients and their family members, we may not adequately satisfy the needs of all patients and risk exacerbating existing disparities in cancer care and outcomes. Methods: We examined patient perspectives on the sharing of genetic results with at-risk family members by surveying a racially diverse sample of cancer patients receiving a multi-gene, multi-cancer risk screen in a community hospital setting. Results: In a survey of 230 cancer patients, we found that intent to share results with family members was high but varied across family member types. More respondents planned to disclose results to at least one sister (82.5%) compared to at least one brother (73.1%). Over one-fourth of participants (27.4%) were either uncertain about sharing or intended to withhold their genomic screening results from at least one at-risk family member eligible for cascade testing. Participants were more likely to withhold their results from a sibling than from a child. Furthermore, intent to share across all family member types was lower if probands failed to identify at least one benefit to sharing. Conclusions: Understanding factors associated with decisions to share results with at-risk relatives in diverse patient populations can help clinicians support cascade genetic cancer screenings in diverse communities and settings.

Background: Large biobanks that link biological specimens with specimen donors’ health histories are a critical tool for precision medicine, and many health care institutions have invested significant resources in setting up and building up large collections for this purpose. As biobanks require consented participation from thousands of individual donors, much research has focused on the values and preferences of new and prospective donors who are actively contemplating an invitation to participate in the collection. Few studies, however, have focused on participants’ opinions about their biobank participation in the months and years following enrollment. Methods: We conducted a survey in a large, established biobank and evaluated participants’ levels of decisional regret regarding their decision to enroll in the biobank. Results: We found very low levels of decisional regret among established biobank participants. Multivariable regression analysis found that age, length of time in the biobank, lower educational attainment, inadequate health literacy, and previous invitations to research participation were all significant predictors of elevated regret. Discussion: Among those with elevated regret, several demographic factors may point to elevated likelihood of decisional regret. More research is needed to identify factors associated with long-term satisfaction with biobank participation and with elevated risk of regret and/or withdrawal from the collection.

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.

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.