Return of Results Policies for Genomic Research: Current Practices and the Hearts in Rhythm Organization (HiRO) Approach

Comber, Drake A.; Davies, Brianna; Roberts, Jason D.; Tadros, Rafik; Green, Martin S.; Healey, Jeffrey S.; Simpson, Christopher S.; Sanatani, Shubhayan; Steinberg, Christian; MacIntyre, Ciorsti; Angaran, Paul; Duff, Henry J.; Hamilton, Robert M.; Arbour, Laura; Leather, Richard; Seifer, Colette; Fournier, Anne; Atallah, Joseph; Kimber, Shane; Makanjee, Bhavanesh; Alqarawi, Wael; Cadrin‐Tourigny, Julia; Joza, Jacqueline; Gibbs, Karen; Robb, Laura; Zahavich, Laura; Gardner, Martin J.; Talajic, Mario; Virani, Alice; Krahn, Andrew D.; Lehman, Anna; Laksman, Zachary

doi:10.1016/j.cjca.2021.10.006

Cited by 3 publications

(3 citation statements)

References 48 publications

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“…In this diagnostic accuracy study, participants of all ages were recruited from the National Hearts in Rhythm Organization (HiRO) Registry. The HiRO Registry enrolls patients with inherited arrhythmias and cardiomyopathies and their unaffected relatives from 25 inherited arrhythmia or cardiogenetics centers . Ethical approval for this study was obtained from the University of British Columbia and Providence Health Care research ethics boards, and all participants provided written informed consent.…”

Section: Methodsmentioning

confidence: 99%

“…The HiRO Registry enrolls patients with inherited arrhythmias and cardiomyopathies and their unaffected relatives from 25 inherited arrhythmia or cardiogenetics centers. 10,11 Ethical approval for this study was obtained from the University of British Columbia and Providence Health Care research ethics boards, and all participants provided written informed consent. The study followed the Standards for Reporting of Diagnostic Accuracy (STARD) reporting guideline.…”

Section: Registry Data and Ethicsmentioning

confidence: 99%

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Deep Learning–Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome

Jiang,

Cheung,

Garcia-Montero

et al. 2024

JAMA Cardiol

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ImportanceCongenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG).ObjectiveTo develop a deep learning–based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG.Design, Setting, and ParticipantsThis diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals.ExposuresConvolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results.Main Outcomes and MeasuresThe main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection.ResultsA total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN’s high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval–based detection (AUC, 0.74; 95% CI, 0.69-0.78).Conclusions and RelevanceThe deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

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Section: Methodsmentioning

confidence: 99%

Section: Registry Data and Ethicsmentioning

confidence: 99%

Deep Learning–Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome

Jiang,

Cheung,

Garcia-Montero

et al. 2024

JAMA Cardiol

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“…The widening incorporation of gRoR in contexts ranging from the CSER Consortium and eMERGE Network to large population studies such as the Framingham Heart Study, Jackson Heart Study, and AoU signals that gRoR is increasingly part of the new normal in research (37,41,44,50,55,77,114). Many biobanks now incorporate gRoR for medically actionable genes (26,129). A consensus committee of the National Academies of Sciences, Engineering, and Medicine recommended in 2018 that "investigators.…”

Section: Introductionmentioning

confidence: 99%

Return of Results in Genomic Research Using Large-Scale or Whole Genome Sequencing: Toward a New Normal

Wolf

Green

2023

Annu. Rev. Genom. Hum. Genet.

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Genome sequencing is increasingly used in research and integrated into clinical care. In the research domain, large-scale analyses, including whole genome sequencing with variant interpretation and curation, virtually guarantees identification of variants that are pathogenic or likely pathogenic and actionable. Multiple guidelines recommend that findings associated with actionable conditions be offered to research participants in order to demonstrate respect for autonomy, reciprocity, and participant interests in health and privacy. Some recommendations go further and support offering a wider range of findings, including those that are not immediately actionable. In addition, entities covered by the US Health Insurance Portability and Accountability Act (HIPAA) may be required to provide a participant's raw genomic data on request. Despite these widely endorsed guidelines and requirements, the implementation of return of genomic results and data by researchers remains uneven. This article analyzes the ethical and legal foundations for researcher duties to offer adult participants their interpreted results and raw data as the new normal in genomic research. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 24 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Principles of Genetic Counseling in Inherited Heart Conditions

Christian

Dzwiniel

2023

Cardiac Electrophysiology Clinics

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Return of Results Policies for Genomic Research: Current Practices and the Hearts in Rhythm Organization (HiRO) Approach

Cited by 3 publications

References 48 publications

Deep Learning–Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome

Deep Learning–Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome

Return of Results in Genomic Research Using Large-Scale or Whole Genome Sequencing: Toward a New Normal

Principles of Genetic Counseling in Inherited Heart Conditions

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