Journal of Personalized Medicine

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16 pages, 1739 KiB

Open AccessEditor’s ChoiceArticle

The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population

by Anurag Verma, Scott M. Damrauer, Nawar Naseer, JoEllen Weaver, Colleen M. Kripke, Lindsay Guare, Giorgio Sirugo, Rachel L. Kember, Theodore G. Drivas, Scott M. Dudek, Yuki Bradford, Anastasia Lucas, Renae Judy, Shefali S. Verma, Emma Meagher, Katherine L. Nathanson, Michael Feldman, Marylyn D. Ritchie, Daniel J. Rader and For The Penn Medicine BioBank

J. Pers. Med. 2022, 12(12), 1974; https://doi.org/10.3390/jpm12121974 - 29 Nov 2022

Cited by 24 | Viewed by 3022

Abstract

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker [...] Read more.

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission to recontact. Herein, we describe the operations and infrastructure of the PMBB, summarize the phenotypic architecture of the enrolled participants, and use body mass index (BMI) as a proof-of-concept quantitative phenotype for PheWAS, LabWAS, and GWAS. The major representation of African-American participants in the PMBB addresses the essential need to expand the diversity in genetic and translational research. There is a critical need for a “medical biobank consortium” to facilitate replication, increase power for rare phenotypes and variants, and promote harmonized collaboration to optimize the potential for biological discovery and precision medicine. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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15 pages, 772 KiB

Open AccessArticle

Real-World Results from Combined Screening for Monogenic Genomic Health Risks and Reproductive Risks in 300 Adults

by Robert S. Wildin, Diana L. Gerrard and Debra G. B. Leonard

J. Pers. Med. 2022, 12(12), 1962; https://doi.org/10.3390/jpm12121962 - 28 Nov 2022

Cited by 4 | Viewed by 1491

Abstract

New methods and demonstrations of feasibility guide future implementation of genomic population health screening programs. This is the first report of genomic population screening in a primary care, non-research setting using existing large carrier and health risk gene sequencing panels combined into one [...] Read more.

New methods and demonstrations of feasibility guide future implementation of genomic population health screening programs. This is the first report of genomic population screening in a primary care, non-research setting using existing large carrier and health risk gene sequencing panels combined into one 432-gene test that is offered to adults of any health status. This report summarizes basic demographic data and analyses patterns of pathogenic and likely pathogenic genetic findings for the first 300 individuals tested in this real-world scenario. We devised a classification system for gene results to facilitate clear message development for our Genomic Medicine Action Plan messaging tool used to summarize and activate results for patients and primary care providers. Potential genetic health risks of various magnitudes for a broad range of disorders were identified in 16% to 34% of tested individuals. The frequency depends on criteria used for the type and penetrance of risk. 86% of individuals are carriers for one or more recessive diseases. Detecting, reporting, and guiding response to diverse genetic health risks and recessive carrier states in a single primary care genomic screening test appears feasible and effective. This is an important step toward exploring an exome or genome sequence as a multi-purpose clinical screening tool. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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16 pages, 992 KiB

Open AccessArticle

Investigating Psychological Impact after Receiving Genetic Risk Results—A Survey of Participants in a Population Genomic Screening Program

by Cara Zayac McCormick, Kristen Dilzell Yu, Alicia Johns, Gemme Campbell-Salome, Miranda L. G. Hallquist, Amy C. Sturm and Adam H. Buchanan

J. Pers. Med. 2022, 12(12), 1943; https://doi.org/10.3390/jpm12121943 - 22 Nov 2022

Cited by 4 | Viewed by 1364

Abstract

Genomic screening programs have potential to benefit individuals who may not be clinically ascertained, but little is known about the psychological impact of receiving genetic results in this setting. The current study sought to further the understanding of individuals’ psychological response to receiving [...] Read more.

Genomic screening programs have potential to benefit individuals who may not be clinically ascertained, but little is known about the psychological impact of receiving genetic results in this setting. The current study sought to further the understanding of individuals’ psychological response to receiving an actionable genetic test result from genomic screening. Telephone surveys were conducted with patient-participants at 6 weeks and 6 months post genetic result disclosure between September 2019 and May 2021 and assessed emotional response to receiving results via the FACToR, PANAS, and decision regret scales. Overall, 354 (29.4%) study participants completed both surveys. Participants reported moderate positive emotions and low levels of negative emotions, uncertainty, privacy concern, and decision regret over time. There were significant decreases in negative emotions (p = 0.0004) and uncertainty (p = 0.0126) between time points on the FACToR scale. “Interested” was the highest scoring discrete emotion (T1 3.6, T2 3.3, scale 0–5) but was significantly lower at 6 months (<0.0001). Coupled with other benefits of genomic screening, these results of modest psychological impact waning over time adds support to clinical utility of population genomic screening programs. However, questions remain regarding how to elicit an emotional response that motivates behavior change without causing psychological harm. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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15 pages, 946 KiB

Open AccessArticle

The Development of an Infrastructure to Facilitate the Use of Whole Genome Sequencing for Population Health

by Nephi A. Walton, Brent Hafen, Sara Graceffo, Nykole Sutherland, Melanie Emmerson, Rachel Palmquist, Christine M. Formea, Maricel Purcell, Bret Heale, Matthew A. Brown, Christopher J. Danford, Sumathi I. Rachamadugu, Thomas N. Person, Katherine A. Shortt, G. Bryce Christensen, Jared M. Evans, Sharanya Raghunath, Christopher P. Johnson, Stacey Knight, Viet T. Le, Jeffrey L. Anderson, Margaret Van Meter, Teresa Reading, Derrick S. Haslem, Ivy C. Hansen, Betsey Batcher, Tyler Barker, Travis J. Sheffield, Bhaskara Yandava, David P. Taylor, Pallavi Ranade-Kharkar, Christopher C. Giauque, Kenneth R. Eyring, Jesse W. Breinholt, Mickey R. Miller, Payton R. Carter, Jason L. Gillman, Andrew W. Gunn, Kirk U. Knowlton, Joshua L. Bonkowsky, Kari Stefansson, Lincoln D. Nadauld and Howard L. McLeod Show full author list Hide full author list

J. Pers. Med. 2022, 12(11), 1867; https://doi.org/10.3390/jpm12111867 - 08 Nov 2022

Cited by 4 | Viewed by 2315

Abstract

The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for [...] Read more.

The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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18 pages, 835 KiB

Open AccessEditor’s ChoiceArticle

The Evolution of a Large Biobank at Mass General Brigham

by Natalie T. Boutin, Samantha B. Schecter, Emma F. Perez, Natasha S. Tchamitchian, Xander R. Cerretani, Vivian S. Gainer, Matthew S. Lebo, Lisa M. Mahanta, Elizabeth W. Karlson and Jordan W. Smoller

J. Pers. Med. 2022, 12(8), 1323; https://doi.org/10.3390/jpm12081323 - 17 Aug 2022

Cited by 11 | Viewed by 2010

Abstract

The Mass General Brigham Biobank (formerly Partners HealthCare Biobank) is a large repository of biospecimens and data linked to extensive electronic health record data and survey data. Its objective is to support and enable translational research focused on genomic, environmental, biomarker and family [...] Read more.

The Mass General Brigham Biobank (formerly Partners HealthCare Biobank) is a large repository of biospecimens and data linked to extensive electronic health record data and survey data. Its objective is to support and enable translational research focused on genomic, environmental, biomarker and family history associations with disease phenotypes. The Biobank has enrolled more than 135,000 participants, generated genomic data on more than 65,000 of its participants, distributed approximately 153,000 biospecimens, and served close to 450 institutional studies with biospecimens or data. Although the Biobank has been successful, based on some measures of output, this has required substantial institutional investment. In addition, several challenges are ongoing, including: (1) developing a sustainable cost model that doesn’t rely as heavily on institutional funding; (2) integrating Biobank operations into clinical workflows; and (3) building a research resource that is diverse and promotes equity in research. Here, we describe the evolution of the Biobank and highlight key lessons learned that may inform other efforts to build biobanking efforts in health system contexts. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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16 pages, 673 KiB

Open AccessArticle

Lessons Learned from the Pilot Phase of a Population-Wide Genomic Screening Program: Building the Base to Reach a Diverse Cohort of 100,000 Participants

by Caitlin G. Allen, Leslie Lenert, Kelly Hunt, Amy Jackson, Elissa Levin, Catherine Clinton, John T. Clark, Kelli Garrison, Sam Gallegos, Karen Wager, Wenjun He, Katherine Sterba, Paula S. Ramos, Cathy Melvin, Marvella Ford, Kenneth Catchpole, Lori McMahon and Daniel P. Judge

J. Pers. Med. 2022, 12(8), 1228; https://doi.org/10.3390/jpm12081228 - 27 Jul 2022

Cited by 7 | Viewed by 2894

Abstract

Background and Objectives: Genomic information is increasingly relevant for disease prevention and risk management at the individual and population levels. Screening healthy adults for Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia using a population-based approach [...] Read more.

Background and Objectives: Genomic information is increasingly relevant for disease prevention and risk management at the individual and population levels. Screening healthy adults for Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia using a population-based approach can help identify the 1–2% of the US population at increased risk of developing diseases associated with these conditions and tailor prevention strategies. Our objective is to report findings from an implementation science study that evaluates multi-level facilitators and barriers to implementation of the In Our DNA SC population-wide genomic screening initiative. Methods: We established an IMPACTeam (IMPlementAtion sCience for In Our DNA SC Team) to evaluate the pilot phase using principles of implementation science. We used a parallel convergent mixed methods approach to assess the Reach, Implementation, and Effectiveness outcomes from the RE-AIM implementation science framework during the pilot phase of In Our DNA SC. Quantitative assessment included the examination of frequencies and response rates across demographic categories using chi-square tests. Qualitative data were audio-recorded and transcribed, with codes developed by the study team based on the semi-structured interview guide. Results: The pilot phase (8 November 2021, to 7 March 2022) included recruitment from ten clinics throughout South Carolina. Reach indicators included enrollment rate and representativeness. A total of 23,269 potential participants were contacted via Epic’s MyChart patient portal with 1976 (8.49%) enrolled. Black individuals were the least likely to view the program invitation (28.9%) and take study-related action. As a result, there were significantly higher enrollment rates among White (10.5%) participants than Asian (8.71%) and Black (3.46%) individuals (p < 0.0001). Common concerns limiting reach and participation included privacy and security of results and the impact participation would have on health or life insurance. Facilitators included family or personal history of a Tier 1 condition, prior involvement in genetic testing, self-interest, and altruism. Assessment of implementation (i.e., adherence to protocols/fidelity to protocols) included sample collection rate (n = 1104, 55.9%) and proportion of samples needing recollection (n = 19, 1.7%). There were no significant differences in sample collection based on demographic characteristics. Implementation facilitators included efficient collection processes and enthusiastic clinical staff. Finally, we assessed the effectiveness of the program, finding low dropout rates (n = 7, 0.35%), the identification of eight individuals with Tier 1 conditions (0.72% positive), and high rates of follow-up genetic counseling (87.5% completion). Conclusion: Overall, Asian and Black individuals were less engaged, with few taking any study-related actions. Strategies to identify barriers and promoters for the engagement of diverse populations are needed to support participation. Once enrolled, individuals had high rates of completing the study and follow-up engagement with genetic counselors. Findings from the pilot phase of In Our DNA SC offer opportunities for improvement as we expand the program and can provide guidance to organizations seeking to begin efforts to integrate population-wide genomic screening. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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15 pages, 575 KiB

Open AccessArticle

Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System

by Loren Saulsberry, Lavisha Singh, Jaclyn Pruitt, Christopher Ward, Dyson T. Wake, Robert D. Gibbons, David O. Meltzer, Peter H. O’Donnell, Wanda Cruz-Knight, Peter J. Hulick, Henry M. Dunnenberger and Sean P. David

J. Pers. Med. 2022, 12(7), 1145; https://doi.org/10.3390/jpm12071145 - 15 Jul 2022

Cited by 1 | Viewed by 1745

Abstract

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we [...] Read more.

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45–10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61–4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77–3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88–2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47–3.07) (p < 0.0001), median household income (OR = 1.63, 1.03–2.58) (p = 0.035), male gender (OR = 1.47, 1.21–1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18–1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08–1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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Other

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5 pages, 200 KiB

Open AccessCommentary

Enabling Diagnostic Resulting as a New Category of Secondary Genomic Findings

by Michael F. Murray

J. Pers. Med. 2022, 12(2), 158; https://doi.org/10.3390/jpm12020158 - 26 Jan 2022

Viewed by 1628

Abstract

Over the past decade, the secondary analysis of existing DNA datasets for clinical resulting has become an established practice. However, this established practice is typically limited to only one category of secondary genomic findings, the identification of “disease risk”. Diagnostic resulting has been [...] Read more.

Over the past decade, the secondary analysis of existing DNA datasets for clinical resulting has become an established practice. However, this established practice is typically limited to only one category of secondary genomic findings, the identification of “disease risk”. Diagnostic resulting has been left out of secondary genomic findings. In medical practice, diagnostic resulting is triggered when a test is ordered for a patient based on a recognizable clinical indication for evaluation; most genetic and genomic testing is carried out in support of diagnostic evaluations. The secondary analysis of existing DNA data has the potential to cost less and have more rapid turnaround times for diagnostic results compared to current DNA diagnostic approaches that typically generate a new dataset with every test ordered. Worldwide, innovative health systems could position themselves to deliver valid secondary genomic finding results in both the established category of disease risk results, as well as a new category of diagnostic results. To support the ongoing delivery of both categories of secondary findings, health systems will need comprehensive genomic datasets for patients and secure workflows that allow for repeated access to that data for on-demand secondary analysis. Full article

(This article belongs to the Special Issue Genomic Medicine Cohorts Based at U.S. Health Systems and Health Centers)

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