Publications by authors named "Euan A Ashley"

193 Publications

The genetics of human performance.

Nat Rev Genet 2021 Sep 14. Epub 2021 Sep 14.

Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Human physiology is likely to have been selected for endurance physical activity. However, modern humans have become largely sedentary, with physical activity becoming a leisure-time pursuit for most. Whereas inactivity is a strong risk factor for disease, regular physical activity reduces the risk of chronic disease and mortality. Although substantial epidemiological evidence supports the beneficial effects of exercise, comparatively little is known about the molecular mechanisms through which these effects operate. Genetic and genomic analyses have identified genetic variation associated with human performance and, together with recent proteomic, metabolomic and multi-omic analyses, are beginning to elucidate the molecular genetic mechanisms underlying the beneficial effects of physical activity on human health.
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http://dx.doi.org/10.1038/s41576-021-00400-5DOI Listing
September 2021

Multi-task deep learning for cardiac rhythm detection in wearable devices.

NPJ Digit Med 2020 Sep 9;3(1):116. Epub 2020 Sep 9.

Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.
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http://dx.doi.org/10.1038/s41746-020-00320-4DOI Listing
September 2020

Worldwide differences in primary prevention implantable cardioverter defibrillator utilization and outcomes in hypertrophic cardiomyopathy.

Eur Heart J 2021 Sep 7. Epub 2021 Sep 7.

Department of Medicine, Brigham and Women's Hospital, Cardiovascular Medicine Division, 75 Francis Street, Boston, MA 02115, USA.

Aims : Risk stratification algorithms for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) and regional differences in clinical practice have evolved over time. We sought to compare primary prevention implantable cardioverter defibrillator (ICD) implantation rates and associated clinical outcomes in US vs. non-US tertiary HCM centres within the international Sarcomeric Human Cardiomyopathy Registry.

Methods And Results: We included patients with HCM enrolled from eight US sites (n = 2650) and five non-US (n = 2660) sites and used multivariable Cox-proportional hazards models to compare outcomes between sites. Primary prevention ICD implantation rates in US sites were two-fold higher than non-US sites (hazard ratio (HR) 2.27 [1.89-2.74]), including in individuals deemed at high 5-year SCD risk (≥6%) based on the HCM risk-SCD score (HR 3.27 [1.76-6.05]). US ICD recipients also had fewer traditional SCD risk factors. Among ICD recipients, rates of appropriate ICD therapy were significantly lower in US vs. non-US sites (HR 0.52 [0.28-0.97]). No significant difference was identified in the incidence of SCD/resuscitated cardiac arrest among non-recipients of ICDs in US vs. non-US sites (HR 1.21 [0.74-1.97]).

Conclusion : Primary prevention ICDs are implanted more frequently in patients with HCM in US vs. non-US sites across the spectrum of SCD risk. There was a lower rate of appropriate ICD therapy in US sites, consistent with a lower-risk population, and no significant difference in SCD in US vs. non-US patients who did not receive an ICD. Further studies are needed to understand what drives malignant arrhythmias, optimize ICD allocation, and examine the impact of different ICD utilization strategies on long-term outcomes in HCM.
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http://dx.doi.org/10.1093/eurheartj/ehab598DOI Listing
September 2021

Combining digital data and artificial intelligence for cardiovascular health.

Cardiovasc Res 2021 Jul;117(9):e116-e117

Division of Cardiology, Department of Medicine, Stanford University, Stanford, CA, USA.

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http://dx.doi.org/10.1093/cvr/cvab211DOI Listing
July 2021

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19.

J Exp Med 2021 08 15;218(8). Epub 2021 Jun 15.

Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA.

Our understanding of protective versus pathological immune responses to SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses revealed widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, including prominent hyperactivation signatures in neutrophils and NK cells. We also identified chromatin accessibility changes at NF-κB binding sites within cytokine gene loci as a potential mechanism for the striking lack of pro-inflammatory cytokine production observed in monocytes in severe and fatal COVID-19. We further demonstrated that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention.
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http://dx.doi.org/10.1084/jem.20210582DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8210586PMC
August 2021

Towards precision medicine in heart failure.

Nat Rev Cardiol 2021 Jun 9. Epub 2021 Jun 9.

Department of Medicine, Division of Cardiology, Stanford University, Palo Alto, CA, USA.

The number of therapies for heart failure (HF) with reduced ejection fraction has nearly doubled in the past decade. In addition, new therapies for HF caused by hypertrophic and infiltrative disease are emerging rapidly. Indeed, we are on the verge of a new era in HF in which insights into the biology of myocardial disease can be matched to an understanding of the genetic predisposition in an individual patient to inform precision approaches to therapy. In this Review, we summarize the biology of HF, emphasizing the causal relationships between genetic contributors and traditional structure-based remodelling outcomes, and highlight the mechanisms of action of traditional and novel therapeutics. We discuss the latest advances in our understanding of both the Mendelian genetics of cardiomyopathy and the complex genetics of the clinical syndrome presenting as HF. In the phenotypic domain, we discuss applications of machine learning for the subcategorization of HF in ways that might inform rational prescribing of medications. We aim to bridge the gap between the biology of the failing heart, its diverse clinical presentations and the range of medications that we can now use to treat it. We present a roadmap for the future of precision medicine in HF.
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http://dx.doi.org/10.1038/s41569-021-00566-9DOI Listing
June 2021

Time trajectories in the transcriptomic response to exercise - a meta-analysis.

Nat Commun 2021 06 9;12(1):3471. Epub 2021 Jun 9.

Center for Inherited Cardiovascular Disease, School of Medicine, Stanford University, Stanford, CA, USA.

Exercise training prevents multiple diseases, yet the molecular mechanisms that drive exercise adaptation are incompletely understood. To address this, we create a computational framework comprising data from skeletal muscle or blood from 43 studies, including 739 individuals before and after exercise or training. Using linear mixed effects meta-regression, we detect specific time patterns and regulatory modulators of the exercise response. Acute and long-term responses are transcriptionally distinct and we identify SMAD3 as a central regulator of the exercise response. Exercise induces a more pronounced inflammatory response in skeletal muscle of older individuals and our models reveal multiple sex-associated responses. We validate seven of our top genes in a separate human cohort. In this work, we provide a powerful resource ( www.extrameta.org ) that expands the transcriptional landscape of exercise adaptation by extending previously known responses and their regulatory networks, and identifying novel modality-, time-, age-, and sex-associated changes.
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http://dx.doi.org/10.1038/s41467-021-23579-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190306PMC
June 2021

Mulibrey Nanism and the Real Time Use of Genome and Biobank Engines to Inform Clinical Care in an Ultrarare Disease.

Circ Genom Precis Med 2021 Jun 7;14(3):e003430. Epub 2021 Jun 7.

Department of Medicine, Division of Cardiology (C.S.W., E.A.A.), Stanford University, Palo Alto, CA.

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http://dx.doi.org/10.1161/CIRCGEN.121.003430DOI Listing
June 2021

Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation.

Circ Genom Precis Med 2021 Jun 15;14(3):e003168. Epub 2021 Jun 15.

Division of Cardiology, Department of Medicine (J.W.O., M.T., M.P., H.W., C.T., S.L.C., E.A.A.), Stanford University School of Medicine, Stanford, CA.

Background: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHADS-VASc) do not include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS).

Methods: Using data from the largest available genome-wide association study in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability.

Results: Compared with the currently recommended risk tool (CHADS-VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%-3.0%]) and fit (χ =0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06-1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson correlation coefficient, -0.018).

Conclusions: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors; however, the prediction of stroke remains challenging.
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http://dx.doi.org/10.1161/CIRCGEN.120.003168DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212575PMC
June 2021

Computational prediction of protein subdomain stability in MYBPC3 enables clinical risk stratification in hypertrophic cardiomyopathy and enhances variant interpretation.

Genet Med 2021 07 29;23(7):1281-1287. Epub 2021 Mar 29.

Cardiovascular Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Purpose: Variants in MYBPC3 causing loss of function are the most common cause of hypertrophic cardiomyopathy (HCM). However, a substantial number of patients carry missense variants of uncertain significance (VUS) in MYBPC3. We hypothesize that a structural-based algorithm, STRUM, which estimates the effect of missense variants on protein folding, will identify a subgroup of HCM patients with a MYBPC3 VUS associated with increased clinical risk.

Methods: Among 7,963 patients in the multicenter Sarcomeric Human Cardiomyopathy Registry (SHaRe), 120 unique missense VUS in MYBPC3 were identified. Variants were evaluated for their effect on subdomain folding and a stratified time-to-event analysis for an overall composite endpoint (first occurrence of ventricular arrhythmia, heart failure, all-cause mortality, atrial fibrillation, and stroke) was performed for patients with HCM and a MYBPC3 missense VUS.

Results: We demonstrated that patients carrying a MYBPC3 VUS predicted to cause subdomain misfolding (STRUM+, ΔΔG ≤ -1.2 kcal/mol) exhibited a higher rate of adverse events compared with those with a STRUM- VUS (hazard ratio = 2.29, P = 0.0282). In silico saturation mutagenesis of MYBPC3 identified 4,943/23,427 (21%) missense variants that were predicted to cause subdomain misfolding.

Conclusion: STRUM identifies patients with HCM and a MYBPC3 VUS who may be at higher clinical risk and provides supportive evidence for pathogenicity.
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http://dx.doi.org/10.1038/s41436-021-01134-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257482PMC
July 2021

Functional and structural analysis of cytokine-selective IL6ST defects that cause recessive hyper-IgE syndrome.

J Allergy Clin Immunol 2021 Aug 23;148(2):585-598. Epub 2021 Mar 23.

Translational Gastroenterology Unit, University of Oxford, Oxford, United Kingdom; Department of Paediatrics, University of Oxford, Oxford, United Kingdom; Oxford National Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.

Background: Biallelic variants in IL6ST, encoding GP130, cause a recessive form of hyper-IgE syndrome (HIES) characterized by high IgE level, eosinophilia, defective acute phase response, susceptibility to bacterial infections, and skeletal abnormalities due to cytokine-selective loss of function in GP130, with defective IL-6 and IL-11 and variable oncostatin M (OSM) and IL-27 levels but sparing leukemia inhibitory factor (LIF) signaling.

Objective: Our aim was to understand the functional and structural impact of recessive HIES-associated IL6ST variants.

Methods: We investigated a patient with HIES by using exome, genome, and RNA sequencing. Functional assays assessed IL-6, IL-11, IL-27, OSM, LIF, CT-1, CLC, and CNTF signaling. Molecular dynamics simulations and structural modeling of GP130 cytokine receptor complexes were performed.

Results: We identified a patient with compound heterozygous novel missense variants in IL6ST (p.Ala517Pro and the exon-skipping null variant p.Gly484_Pro518delinsArg). The p.Ala517Pro variant resulted in a more profound IL-6- and IL-11-dominated signaling defect than did the previously identified recessive HIES IL6ST variants p.Asn404Tyr and p.Pro498Leu. Molecular dynamics simulations suggested that the p.Ala517Pro and p.Asn404Tyr variants result in increased flexibility of the extracellular membrane-proximal domains of GP130. We propose a structural model that explains the cytokine selectivity of pathogenic IL6ST variants that result in recessive HIES. The variants destabilized the conformation of the hexameric cytokine receptor complexes, whereas the trimeric LIF-GP130-LIFR complex remained stable through an additional membrane-proximal interaction. Deletion of this membrane-proximal interaction site in GP130 consequently caused additional defective LIF signaling and Stüve-Wiedemann syndrome.

Conclusion: Our data provide a structural basis to understand clinical phenotypes in patients with IL6ST variants.
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http://dx.doi.org/10.1016/j.jaci.2021.02.044DOI Listing
August 2021

Clinical characteristics and outcomes in childhood-onset hypertrophic cardiomyopathy.

Eur Heart J 2021 05;42(20):1988-1996

Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.

Aims: Childhood-onset hypertrophic cardiomyopathy (HCM) is far less common than adult-onset disease, thus natural history is not well characterized. We aim to describe the characteristics and outcomes of childhood-onset HCM.

Methods And Results: We performed an observational cohort study of 7677 HCM patients from the Sarcomeric Human Cardiomyopathy Registry (SHaRe). Hypertrophic cardiomyopathy patients were stratified by age at diagnosis [<1 year (infancy), 1-18 years (childhood), >18 years (adulthood)] and assessed for composite endpoints reflecting heart failure (HF), life-threatening ventricular arrhythmias, atrial fibrillation (AF), and an overall composite that also included stroke and death. Stratifying by age of diagnosis, 184 (2.4%) patients were diagnosed in infancy; 1128 (14.7%) in childhood; and 6365 (82.9%) in adulthood. Childhood-onset HCM patients had an ∼2%/year event rate for the overall composite endpoint, with ventricular arrhythmias representing the most common event in the 1st decade following baseline visit, but HF and AF becoming more common by the end of the 2nd decade. Sarcomeric variants were more common in childhood-onset HCM (63%) and carried a worse prognosis than non-sarcomeric disease, including a greater than two-fold increased risk of HF [HRadj 2.39 (1.36-4.20), P = 0.003] and 67% increased risk of the overall composite outcome [HRadj 1.67 (1.16-2.41), P = 0.006]. When compared with adult-onset HCM, childhood-onset was 36% more likely to develop life-threatening ventricular arrhythmias [HRadj 1.36 (1.03-1.80)] and twice as likely to require transplant or ventricular assist device [HRadj 1.99 (1.23-3.23)].

Conclusion: Patients with childhood-onset HCM are more likely to have sarcomeric disease, carry a higher risk of life-threatening ventricular arrythmias, and have greater need for advanced HF therapies. These findings provide insight into the natural history of disease and can help inform clinical risk stratification.
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http://dx.doi.org/10.1093/eurheartj/ehab148DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139852PMC
May 2021

Genomic Context Differs Between Human Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy.

J Am Heart Assoc 2021 04 25;10(7):e019944. Epub 2021 Mar 25.

Center for Genetic Medicine Northwestern University Feinberg School of Medicine Chicago IL.

Background Inherited cardiomyopathies display variable penetrance and expression, and a component of phenotypic variation is genetically determined. To evaluate the genetic contribution to this variable expression, we compared protein coding variation in the genomes of those with hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). Methods and Results Nonsynonymous single-nucleotide variants (nsSNVs) were ascertained using whole genome sequencing from familial cases of HCM (n=56) or DCM (n=70) and correlated with echocardiographic information. Focusing on nsSNVs in 102 genes linked to inherited cardiomyopathies, we correlated the number of nsSNVs per person with left ventricular measurements. Principal component analysis and generalized linear models were applied to identify the probability of cardiomyopathy type as it related to the number of nsSNVs in cardiomyopathy genes. The probability of having DCM significantly increased as the number of cardiomyopathy gene nsSNVs per person increased. The increase in nsSNVs in cardiomyopathy genes significantly associated with reduced left ventricular ejection fraction and increased left ventricular diameter for individuals carrying a DCM diagnosis, but not for those with HCM. Resampling was used to identify genes with aberrant cumulative allele frequencies, identifying potential modifier genes for cardiomyopathy. Conclusions Participants with DCM had more nsSNVs per person in cardiomyopathy genes than participants with HCM. The nsSNV burden in cardiomyopathy genes did not correlate with the probability or manifestation of left ventricular measures in HCM. These findings support the concept that increased variation in cardiomyopathy genes creates a genetic background that predisposes to DCM and increased disease severity.
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http://dx.doi.org/10.1161/JAHA.120.019944DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8174318PMC
April 2021

Generation of three induced pluripotent stem cell lines, SCVIi003-A, SCVIi004-A, SCVIi005-A, from patients with ARVD/C caused by heterozygous mutations in the PKP2 gene.

Stem Cell Res 2021 05 12;53:102284. Epub 2021 Mar 12.

Stanford Cardiovascular Institute, Stanford University, School of Medicine, United States; Division of Cardiovascular Medicine, Depart of Medicine, Stanford University, School of Medicine, United States; Department of Radiology, Stanford University, School of Medicine, United States. Electronic address:

Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is an inherited heart disease which can cause life-threatening ventricular arrhythmias and cardiac dysfunction. The autosomal dominant form of ARVD/C is caused by mutations in the cardiac desmosome, such as those in the plakoglobin plakophilin-2 (PKP2) gene. Here, we generated three human induced pluripotent stem cell (iPSC) lines from the peripheral blood mononuclear cells (PBMCs) of three ARVD/C patients carrying pathogenic variants in their PKP2 genes (c.2065_2070delinsG; c.235C>T; c.1725_1728dup). All lines show the typical morphology of pluripotent stem cells, demonstrate high expression of pluripotent markers, display normal karyotype, and differentiate into all three germ layers in vitro. These lines are valuable resources for studying the pathological mechanisms of ARVD/C caused by PKP2 mutation.
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http://dx.doi.org/10.1016/j.scr.2021.102284DOI Listing
May 2021

Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries.

Am J Cardiol 2021 06 3;148:157-164. Epub 2021 Mar 3.

Genomics plc, Oxford, UK.

The American College of Cardiology / American Heart Association pooled cohort equations tool (ASCVD-PCE) is currently recommended to assess 10-year risk for atherosclerotic cardiovascular disease (ASCVD). ASCVD-PCE does not currently include genetic risk factors. Polygenic risk scores (PRSs) have been shown to offer a powerful new approach to measuring genetic risk for common diseases, including ASCVD, and to enhance risk prediction when combined with ASCVD-PCE. Most work to date, including the assessment of tools, has focused on performance in individuals of European ancestries. Here we present evidence for the clinical validation of a new integrated risk tool (IRT), ASCVD-IRT, which combines ASCVD-PCE with PRS to predict 10-year risk of ASCVD across diverse ethnicity and ancestry groups. We demonstrate improved predictive performance of ASCVD-IRT over ASCVD-PCE, not only in individuals of self-reported White ethnicities (net reclassification improvement [NRI]; with 95% confidence interval = 2.7% [1.1 to 4.2]) but also Black / African American / Black Caribbean / Black African (NRI = 2.5% [0.6-4.3]) and South Asian (Indian, Bangladeshi or Pakistani) ethnicities (NRI = 8.7% [3.1 to 14.4]). NRI confidence intervals were wider and included zero for ethnicities with smaller sample sizes, including Hispanic (NRI = 7.5% [-1.4 to 16.5]), but PRS effect sizes in these ethnicities were significant and of comparable size to those seen in individuals of White ethnicities. Comparable results were obtained when individuals were analyzed by genetically inferred ancestry. Together, these results validate the performance of ASCVD-IRT in multiple ethnicities and ancestries, and favor their generalization to all ethnicities and ancestries.
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http://dx.doi.org/10.1016/j.amjcard.2021.02.032DOI Listing
June 2021

Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction.

Circ Genom Precis Med 2021 04 2;14(2):e003304. Epub 2021 Mar 2.

Genomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).

Background: There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment.

Methods: Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals.

Results: The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%-15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6-19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period.

Conclusions: An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.
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http://dx.doi.org/10.1161/CIRCGEN.120.003304DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284388PMC
April 2021

Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays.

BMC Bioinformatics 2021 Feb 24;22(1):85. Epub 2021 Feb 24.

Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.

Background: Benchmarking the performance of complex analytical pipelines is an essential part of developing Lab Developed Tests (LDT). Reference samples and benchmark calls published by Genome in a Bottle (GIAB) consortium have enabled the evaluation of analytical methods. The performance of such methods is not uniform across the different genomic regions of interest and variant types. Several benchmarking methods such as hap.py, vcfeval, and vcflib are available to assess the analytical performance characteristics of variant calling algorithms. However, assessing the performance characteristics of an overall LDT assay still requires stringing together several such methods and experienced bioinformaticians to interpret the results. In addition, these methods are dependent on the hardware, operating system and other software libraries, making it impossible to reliably repeat the analytical assessment, when any of the underlying dependencies change in the assay. Here we present a scalable and reproducible, cloud-based benchmarking workflow that is independent of the laboratory and the technician executing the workflow, or the underlying compute hardware used to rapidly and continually assess the performance of LDT assays, across their regions of interest and reportable range, using a broad set of benchmarking samples.

Results: The benchmarking workflow was used to evaluate the performance characteristics for secondary analysis pipelines commonly used by Clinical Genomics laboratories in their LDT assays such as the GATK HaplotypeCaller v3.7 and the SpeedSeq workflow based on FreeBayes v0.9.10. Five reference sample truth sets generated by Genome in a Bottle (GIAB) consortium, six samples from the Personal Genome Project (PGP) and several samples with validated clinically relevant variants from the Centers for Disease Control were used in this work. The performance characteristics were evaluated and compared for multiple reportable ranges, such as whole exome and the clinical exome.

Conclusions: We have implemented a benchmarking workflow for clinical diagnostic laboratories that generates metrics such as specificity, precision and sensitivity for germline SNPs and InDels within a reportable range using whole exome or genome sequencing data. Combining these benchmarking results with validation using known variants of clinical significance in publicly available cell lines, we were able to establish the performance of variant calling pipelines in a clinical setting.
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http://dx.doi.org/10.1186/s12859-020-03934-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903625PMC
February 2021

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.

Nat Commun 2021 01 13;12(1):350. Epub 2021 Jan 13.

Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.
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http://dx.doi.org/10.1038/s41467-020-20516-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806647PMC
January 2021

The effect of digital physical activity interventions on daily step count: a randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study.

Lancet Digit Health 2019 11 9;1(7):e344-e352. Epub 2019 Oct 9.

Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA. Electronic address:

Background: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study.

Methods: In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with ClinicalTrials.gov, NCT03090321.

Findings: Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline monitoring and at least 1 day of one of the four interventions and thus were included in the modified intention-to-treat analysis set. 493 individuals completed the full set of assigned interventions. All four interventions significantly increased mean daily step count from baseline (mean daily step count 2914 [SE 74]): mean step count increased by 319 steps (75) for participants in the American Heart Association website prompt group (p<0·0001), 267 steps (74) for participants in the hourly stand prompt group (p=0·0003), 254 steps (74) for participants in the cluster-specific prompts group (p=0·0006), and by 226 steps (75) for participants in the 10 000 daily step prompt group (p=0·0026 vs baseline).

Interpretation: Four smartphone-based physical activity coaching interventions significantly increased daily physical activity. These findings suggests that digital interventions delivered via a mobile app have the ability to increase short-term physical activity levels in a free-living cohort.

Funding: Stanford Data Science Initiative.
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http://dx.doi.org/10.1016/S2589-7500(19)30129-3DOI Listing
November 2019

Associations Between Female Sex, Sarcomere Variants, and Clinical Outcomes in Hypertrophic Cardiomyopathy.

Circ Genom Precis Med 2021 02 7;14(1):e003062. Epub 2020 Dec 7.

Brigham and Women's Hospital (N.K.L., A.L.C., C.E.S., C.Y.H.), Harvard Medical School, MA.

Background: The impact of sex on phenotypic expression in hypertrophic cardiomyopathy (HCM) has not been well characterized in genotyped cohorts.

Methods: Retrospective cohort study from an international registry of patients receiving care at experienced HCM centers. Sex-based differences in baseline characteristics and clinical outcomes were assessed.

Results: Of 5873 patients (3788 genotyped), 2226 (37.9%) were women. At baseline, women were older (49.0±19.9 versus 42.9±18.4 years, <0.001) and more likely to have pathogenic/likely pathogenic sarcomeric variants (HCM patients with a sarcomere mutation; 51% versus 43%, <0.001) despite equivalent utilization of genetic testing. Age at diagnosis varied by sex and genotype despite similar distribution of causal genes. Women were 3.6 to 7.1 years older at diagnosis (<0.02) except for patients with variants where age at diagnosis was comparable for women and men (n=492; 34.8±19.2 versus 33.3±16.8 years, =0.39). Over 7.7 median years of follow-up, New York Heart Association III-IV heart failure was more common in women (hazard ratio, 1.87 [CI, 1.48-2.36], <0.001), after controlling for their higher burden of symptoms and outflow tract obstruction at baseline, reduced ejection fraction, HCM patients with a sarcomere mutation, age, and hypertension. All-cause mortality was increased in women (hazard ratio, 1.50 [CI, 1.13-1.99], <0.01) but neither implantable cardioverter-defibrillator utilization nor ventricular arrhythmia varied by sex.

Conclusions: In HCM, women are older at diagnosis, partly modified by genetic substrate. Regardless of genotype, women were at higher risk of mortality and developing severe heart failure symptoms. This points to a sex-effect on long-term myocardial performance in HCM, which should be investigated further.
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http://dx.doi.org/10.1161/CIRCGEN.120.003062DOI Listing
February 2021

Digital Health Applications for Pharmacogenetic Clinical Trials.

Genes (Basel) 2020 10 26;11(11). Epub 2020 Oct 26.

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Digital health (DH) is the use of digital technologies and data analytics to understand health-related behaviors and enhance personalized clinical care. DH is increasingly being used in clinical trials, and an important field that could potentially benefit from incorporating DH into trial design is pharmacogenetics. Prospective pharmacogenetic trials typically compare a standard care arm to a pharmacogenetic-guided therapeutic arm. These trials often require large sample sizes, are challenging to recruit into, lack patient diversity, and can have complicated workflows to deliver therapeutic interventions to both investigators and patients. Importantly, the use of DH technologies could mitigate these challenges and improve pharmacogenetic trial design and operation. Some DH use cases include (1) automatic electronic health record-based patient screening and recruitment; (2) interactive websites for participant engagement; (3) home- and tele-health visits for patient convenience (e.g., samples for lab tests, physical exams, medication administration); (4) healthcare apps to collect patient-reported outcomes, adverse events and concomitant medications, and to deliver therapeutic information to patients; and (5) wearable devices to collect vital signs, electrocardiograms, sleep quality, and other discrete clinical variables. Given that pharmacogenetic trials are inherently challenging to conduct, future pharmacogenetic utility studies should consider implementing DH technologies and trial methodologies into their design and operation.
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http://dx.doi.org/10.3390/genes11111261DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692850PMC
October 2020

Multi-task deep learning for cardiac rhythm detection in wearable devices.

NPJ Digit Med 2020 9;3:116. Epub 2020 Sep 9.

Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA USA.

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.
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http://dx.doi.org/10.1038/s41746-020-00320-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481177PMC
September 2020

Temporal Trend of Age at Diagnosis in Hypertrophic Cardiomyopathy: An Analysis of the International Sarcomeric Human Cardiomyopathy Registry.

Circ Heart Fail 2020 09 8;13(9):e007230. Epub 2020 Sep 8.

Cardiomyopathy Unit and Genetic Unit, Careggi University Hospital, Florence, Italy (C.F., F.M., I.O.).

Background: Over the last 50 years, the epidemiology of hypertrophic cardiomyopathy (HCM) has changed because of increased awareness and availability of advanced diagnostic tools. We aim to describe the temporal trends in age, sex, and clinical characteristics at HCM diagnosis over >4 decades.

Methods: We retrospectively analyzed records from the ongoing multinational Sarcomeric Human Cardiomyopathy Registry. Overall, 7286 patients with HCM diagnosed at an age ≥18 years between 1961 and 2019 were included in the analysis and divided into 3 eras of diagnosis (<2000, 2000-2010, >2010).

Results: Age at diagnosis increased markedly over time (40±14 versus 47±15 versus 51±16 years, <0.001), both in US and non-US sites, with a stable male-to-female ratio of about 3:2. Frequency of familial HCM declined over time (38.8% versus 34.3% versus 32.7%, <0.001), as well as heart failure symptoms at presentation (New York Heart Association III/IV: 18.1% versus 15.8% versus 12.6%, <0.001). Left ventricular hypertrophy became less marked over time (maximum wall thickness: 20±6 versus 18±5 versus 17±5 mm, <0.001), while prevalence of obstructive HCM was greater in recent cohorts (peak gradient >30 mm Hg: 31.9% versus 39.3% versus 39.0%, =0.001). Consistent with decreasing phenotypic severity, yield of pathogenic/likely pathogenic variants at genetic testing decreased over time (57.7% versus 45.6% versus 38.4%, <0.001).

Conclusions: Evolving HCM populations include progressively greater representation of older patients with sporadic disease, mild phenotypes, and genotype-negative status. Such trend suggests a prominent role of imaging over genetic testing in promoting HCM diagnoses and urges efforts to understand genotype-negative disease eluding the classic monogenic paradigm.
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http://dx.doi.org/10.1161/CIRCHEARTFAILURE.120.007230DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497482PMC
September 2020

Apelin increases atrial conduction velocity, refractoriness, and prevents inducibility of atrial fibrillation.

JCI Insight 2020 09 3;5(17). Epub 2020 Sep 3.

Division of Cardiovascular Medicine, Stanford Medicine, Stanford, California, USA.

Previous studies have shown an association between elevated atrial NADPH-dependent oxidative stress and decreased plasma apelin in patients with atrial fibrillation (AF), though the basis for this relationship is unclear. In the current study, RT-PCR and immunofluorescence studies of human right atrial appendages (RAAs) showed expression of the apelin receptor, APJ, and reduced apelin content in the atria, but not in plasma, of patients with AF versus normal sinus rhythm. Disruption of the apelin gene in mice increased (2.4-fold) NADPH-stimulated superoxide levels and slowed atrial conduction velocities in optical mapping of a Langendorff-perfused isolated heart model, suggesting that apelin levels may influence AF vulnerability. Indeed, in mice with increased AF vulnerability (induced by chronic intense exercise), apelin administration reduced the incidence and duration of induced atrial arrhythmias in association with prolonged atrial refractory periods. Moreover, apelin decreased AF induction in isolated atria from exercised mice while accelerating conduction velocity and increasing action potential durations. At the cellular level, these changes were associated with increased atrial cardiomyocyte sodium currents. These findings support the conclusion that reduced atrial apelin is maladaptive in fibrillating human atrial myocardium and that increasing apelin bioavailability may be a worthwhile therapeutic strategy for treating and preventing AF.
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http://dx.doi.org/10.1172/jci.insight.126525DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526452PMC
September 2020

Spatial and Functional Distribution of Pathogenic Variants and Clinical Outcomes in Patients With Hypertrophic Cardiomyopathy.

Circ Genom Precis Med 2020 10 25;13(5):396-405. Epub 2020 Aug 25.

Cardiovascular Medicine, University of Pennsylvania, Philadelphia (S.M.D.).

Background: Pathogenic variants in , encoding cardiac MyBP-C (myosin binding protein C), are the most common cause of familial hypertrophic cardiomyopathy. A large number of unique variants and relatively small genotyped hypertrophic cardiomyopathy cohorts have precluded detailed genotype-phenotype correlations.

Methods: Patients with hypertrophic cardiomyopathy and variants were identified from the Sarcomeric Human Cardiomyopathy Registry. Variant types and locations were analyzed, morphological severity was assessed, and time-event analysis was performed (composite clinical outcome of sudden death, class III/IV heart failure, left ventricular assist device/transplant, atrial fibrillation). For selected missense variants falling in enriched domains, myofilament localization and degradation rates were measured in vitro.

Results: Among 4756 genotyped patients with hypertrophic cardiomyopathy in Sarcomeric Human Cardiomyopathy Registry, 1316 patients were identified with adjudicated pathogenic truncating (N=234 unique variants, 1047 patients) or nontruncating (N=22 unique variants, 191 patients) variants in . Truncating variants were evenly dispersed throughout the gene, and hypertrophy severity and outcomes were not associated with variant location (grouped by 5'-3' quartiles or by founder variant subgroup). Nontruncating pathogenic variants clustered in the C3, C6, and C10 domains (18 of 22, 82%, <0.001 versus Genome Aggregation Database common variants) and were associated with similar hypertrophy severity and adverse event rates as observed with truncating variants. MyBP-C with variants in the C3, C6, and C10 domains was expressed in rat ventricular myocytes. C10 mutant MyBP-C failed to incorporate into myofilaments and degradation rates were accelerated by ≈90%, while C3 and C6 mutant MyBP-C incorporated normally with degradation rate similar to wild-type.

Conclusions: Truncating variants account for 91% of pathogenic variants and cause similar clinical severity and outcomes regardless of location, consistent with locus-independent loss-of-function. Nontruncating pathogenic variants are regionally clustered, and a subset also cause loss of function through failure of myofilament incorporation and rapid degradation. Cardiac morphology and clinical outcomes are similar in patients with truncating versus nontruncating variants.
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http://dx.doi.org/10.1161/CIRCGEN.120.002929DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676622PMC
October 2020

High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs.

medRxiv 2020 Sep 1. Epub 2020 Sep 1.

During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.
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http://dx.doi.org/10.1101/2020.07.27.20163147DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402057PMC
September 2020

Molecular Choreography of Acute Exercise.

Cell 2020 05;181(5):1112-1130.e16

Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.
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http://dx.doi.org/10.1016/j.cell.2020.04.043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299174PMC
May 2020

Video-based AI for beat-to-beat assessment of cardiac function.

Nature 2020 04 25;580(7802):252-256. Epub 2020 Mar 25.

Department of Computer Science, Stanford University, Stanford, CA, USA.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training. Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.
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http://dx.doi.org/10.1038/s41586-020-2145-8DOI Listing
April 2020
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