Harmonizing Outcomes for Genomic Medicine: Comparison of eMERGE Outcomes to ClinGen Outcome/Intervention Pairs

Williams, Janet L.; Chung, Wendy K.; Fedotov, Alex; Krzemieniecki, Krzysztof; Weng, Chunhua; Connolly, John J.; Harr, Margaret; Hákonarson, Hákon; Leppig, Kathleen A.; Larson, Eric B.; Jarvik, Gail P.; Veenstra, David L.; Hoell, Christin; Smith, Maureen E.; Holm, Ingrid A.; Peterson, Josh F.; Williams, Marc S.

doi:10.1201/9780429295010-27

Cited by 8 publications

(10 citation statements)

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“…As a part of this process, it is essential that clinical outcomes be collected and aggregated to inform modifications and amendments to what we propose, which will also change the secondary findings gene list. The creation of a learning health system model to gather evidence for precision health has been proposed 49 and the need for harmonization of outcomes 50 to accelerate the accumulation of experience has been demonstrated. These efforts will be enhanced by the standardized approach advocated here.…”

Section: Box 4 Stk11 Hypothetical Examplementioning

confidence: 99%

“…This has the immense benefit of reducing data heterogeneity, which in turn will facilitate rapid learning. 49,50 Given current knowledge, a clinician following our approach will optimize the opportunity to mitigate disease while also remaining cognizant of the uncertainties inherent in genomic prediction. Ultimately, predictive genomic medicine has the potential to greatly improve health and healthcare.…”

Section: Box 4 Stk11 Hypothetical Examplementioning

confidence: 99%

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Management of Secondary Genomic Findings

Katz

Nussbaum

Solomon

et al. 2020

The American Journal of Human Genetics

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Secondary genomic findings are increasingly being returned to individuals as opportunistic screening results. A secondary finding offers the chance to identify and mitigate disease that may otherwise be unrecognized in an individual. As a form of screening, secondary findings must be considered differently from sequencing results in a diagnostic setting. For these reasons, clinicians should employ an evaluation and long-term management strategy that accounts for both the increased disease risk associated with a secondary finding and the lower positive predictive value of a screening result compared to an indication-based testing result. Here we describe an approach to the clinical evaluation and management of an individual who presents with a secondary finding. This approach enumerates five domains of evaluation-(1) medical history, (2) physical exam, (3) family history, (4) diagnostic phenotypic testing, and (5) variant correlationthrough which a clinician can distinguish a molecular finding from a clinicomolecular diagnosis of genomic disease. With this framework, both geneticists and non-geneticist clinicians can optimize their ability to detect and mitigate genomic disease while avoiding the pitfalls of overdiagnosis. Our goal with this approach is to help clinicians translate secondary findings into meaningful recognition, treatment, and prevention of disease.

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Section: Box 4 Stk11 Hypothetical Examplementioning

confidence: 99%

Section: Box 4 Stk11 Hypothetical Examplementioning

confidence: 99%

Management of Secondary Genomic Findings

Katz

Nussbaum

Solomon

et al. 2020

The American Journal of Human Genetics

Self Cite

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“…This list of measures will become more comprehensive over time as we and others identify additional validated measures and develop new measures. While we recognize the value of other frameworks relevant to genomic medicine such as ClinGen 28 (another NHGRI-funded consortium), these prior frameworks are relatively limited in scope, while the GMIR Framework is considerably more comprehensive.…”

Section: Discussionmentioning

confidence: 99%

The Genomic Medicine Integrative Research Framework: A Conceptual Framework for Conducting Genomic Medicine Research

Horowitz

Orlando²,

Slavotinek

et al. 2019

The American Journal of Human Genetics

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Conceptual frameworks are useful in research because they can highlight priority research domains, inform decisions about interventions, identify outcomes and factors to measure, and display how factors might relate to each other to generate and test hypotheses. Discovery, translational, and implementation research are all critical to the overall mission of genomic medicine and prevention, but they have yet to be organized into a unified conceptual framework. To fill this gap, our diverse team collaborated to develop the Genomic Medicine Integrative Research (GMIR) Framework, a simple but comprehensive tool to aid the genomics community in developing research questions, strategies, and measures and in integrating genomic medicine and prevention into clinical practice. Here we present the GMIR Framework and its development, along with examples of its use for research development, demonstrating how we applied it to select and harmonize measures for use across diverse genomic medicine implementation projects. Researchers can utilize the GMIR Framework for their own research, collaborative investigations, and clinical implementation efforts; clinicians can use it to establish and evaluate programs; and all stakeholders can use it to help allocate resources and make sure that the full complexity of etiology is included in research and program design, development, and evaluation.

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“…All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). 10…”

Section: Methodsmentioning

confidence: 99%

The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System

David

Singh

Pruitt

et al. 2021

Preprint

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Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines exist for many medications commonly prescribed prior to hospital discharge, yet there is limited data regarding the contribution of gene-x-drug interactions to hospital readmissions. The present study evaluated the relationship between prescription of CPIC medications prescribed within 30 days of hospital admission and 90-day hospital readmission from 2010-2020 in a study population (N=10,104) who underwent sequencing with a 14-gene pharmacogenetic panel. The presence of at least one pharmacogenetic indicator for a medication prescribed within 30 days of hospital admission was considered a gene-x-drug interaction. Multivariable logistic regression analyzed the association between one or more gene-x-drug interactions with 90-day readmission. There were 2,211/2,354 (93.9%) admitted patients who were prescribed at least one CPIC medication. Univariate analyses indicated that the presence of at least one identified gene-x-drug interaction increased risk of 90-day readmission by more than 40% (OR=1.42, 95% confidence interval (CI) 1.09-1.84)(p=0.01). A multivariable model adjusting for age, race, sex, employment status, body mass index, and medical conditions, slightly attenuated the effect (OR=1.33, 95% CI 1.02-1.75)(p=0.04). Our results suggest that the presence of one or more CPIC gene-x-drug interactions increases the risk of 90-day hospital readmission, even after adjustment for demographic and clinical risk factors.

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Harmonizing Outcomes for Genomic Medicine: Comparison of eMERGE Outcomes to ClinGen Outcome/Intervention Pairs

Cited by 8 publications

References 0 publications

Management of Secondary Genomic Findings

Management of Secondary Genomic Findings

The Genomic Medicine Integrative Research Framework: A Conceptual Framework for Conducting Genomic Medicine Research

The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System

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