Search

Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR.We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRRAmong 56,793 individuals (mean age 57 years, 51% women), the HRRDeep learning-derived estimates of HRR using resting ECG independently associated with future clinical outcomes, including new-onset DM and all-cause mortality. Inferring postexercise heart rate response from a resting ECG may have potential clinical implications and impact on preventive strategies warrants future study.

The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.

Background: Genetic variants in LMNA may cause cardiac disease, but population-level contributions of variants to cardiac disease burden are not well-characterized. Objectives: We sought to determine the frequency and contribution of rare LMNA variants to cardiomyopathy and arrhythmia risk among ambulatory adults. Methods: We included 185,990 UK Biobank participants with whole-exome sequencing. We annotated rare loss-of-function and missense LMNA variants for functional effect using 30 in silico prediction tools. We assigned a predicted functional effect weight to each variant and calculated a score for each carrier. We tested associations between the LMNA score and arrhythmia (atrial fibrillation, bradyarrhythmia, ventricular arrhythmia) or cardiomyopathy outcomes (dilated cardiomyopathy and heart failure). We also examined associations for variants located upstream vs downstream of the nuclear localization signal. Results: Overall, 1,167 (0.63%) participants carried an LMNA variant and 15,079 (8.11%) had an arrhythmia or cardiomyopathy event during a median follow-up of 10.9 years. The LMNA score was associated with arrhythmia or cardiomyopathy (OR: 2.21; P < 0.001) and the association was more significant when restricted to variants upstream of the nuclear localization signal (OR: 5.05; P < 0.001). The incidence rate of arrhythmia or cardiomyopathy was 8.43 per 1,000 person-years (95% CI: 6.73-10.12 per 1,000 person-years) among LMNA variant carriers and 6.38 per 1,000 person-years (95% CI: 6.27-6.50 per 1,000 person-years) among noncarriers. Only 3 (1.2%) of the variants were reported as pathogenic in ClinVar. Conclusions: Middle-aged adult carriers of rare missense or loss-of-function LMNA variants are at increased risk for arrhythmia and cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) causes ventricular arrhythmias (VAs) and sudden cardiac death (SCD). In 2019, a risk prediction model that estimates the 5-year risk of incident VAs in ARVC was developed (ARVCrisk.com). This study aimed to externally validate this prediction model in a large international multicentre cohort and to compare its performance with the risk factor approach recommended for implantable cardioverter-defibrillator (ICD) use by published guidelines and expert consensus. In a retrospective cohort of 429 individuals from 29 centres in North America and Europe, 103 (24%) experienced sustained VA during a median follow-up of 5.02 (2.05-7.90) years following diagnosis of ARVC. External validation yielded good discrimination [C-index of 0.70 (95% confidence interval-CI 0.65-0.75)] and calibration slope of 1.01 (95% CI 0.99-1.03). Compared with the three published consensus-based decision algorithms for ICD use in ARVC (Heart Rhythm Society consensus on arrhythmogenic cardiomyopathy, International Task Force consensus statement on the treatment of ARVC, and American Heart Association guidelines for VA and SCD), the risk calculator performed better with a superior net clinical benefit below risk threshold of 35%. Using a large independent cohort of patients, this study shows that the ARVC risk model provides good prognostic information and outperforms other published decision algorithms for ICD use. These findings support the use of the model to facilitate shared decision making regarding ICD implantation in the primary prevention of SCD in ARVC. P.J. is supported by the Daniel Bravo Foundation grant and Spanish Society of Cardiology Magda Heras mobility grant, A.G. by the Wilton W. Webster Fellow of Heart Rhythm Society, The Johns Hopkins ARVD/C Programme by the Leonie-Wild Foundation, Leyla Erkan Family Fund for ARVD Research, The Hugh Calkins, Marvin H. Weiner, and Jacqueline J. Bernstein Cardiac Arrhythmia Center, Dr Francis P. Chiramonte Private Foundation, Dr Satish, Rupal, and Robin Shah ARVD Fund at Johns Hopkins, Bogle Foundation, Campanella Family, Patrick J. Harrison Family, Peter French Memorial Foundation, and Wilmerding Endowments, H.K.J. by grants from Novo Nordisk Foundation, Denmark (NNF18OC0031258 and NNF20OC0065151), S.A.L. by NIH grant 1R01HL139731 and American Heart Association 18SFRN34250007, R.T. by the Canada Research Chairs programme, J.C.T. by the Philippa and Marvin Carsley Cardiology Research Chair. S.G.P. receives support from Ricerca Corrente funding scheme of the Italian Ministry of Health and Italian Ministry of Research and University Dipartimenti di Eccellenza 2018 to 2022 grant to the Molecular Medicine Department (University of Pavia). Sí

First-degree relatives of a proband with an inherited cardiac condition (ICC) are offered predictive genetic testing for the pathogenic or likely pathogenic (P/LP) cardiac gene variant (CGV) to clarify their risk for the familial condition. Relatives who test negative for a familial P/LP CGV typically do not require longitudinal cardiac surveillance. To our knowledge, no previous study has investigated adjustment to risk reduction and subsequent screening practices in genotype-negative relatives from an ICC population. We thus investigated risk perception and ongoing screening practices in genotype-negative adults who received cardiac genetic counseling. Correlations between clinical and demographic variables and risk perception and screening practices were also investigated. On average, participants (n = 71) reported a perceived 19.5% lifetime risk of developing the ICC in their family, despite their negative genetic test result. The majority (54%) of participants reported having undergone cardiac screening after disclosure of their negative result. There were no significant correlations between clinical and demographic variables and risk perception or screening practices. Furthermore, risk perception was not found to impact the likelihood of cardiac screening. These findings suggest that even with comprehensive cardiac genetic counseling, a proportion of this population did not accurately comprehend or recall their cardiac disease risk. Additional interventions beyond traditional result disclosure should be explored to help genotype-negative individuals adjust to their reduction in risk for a familial ICC.

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder.METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors.CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-β signalling molecules and spectrin β. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention.KEY QUESTION: Expand our understanding of the genetic basis for mitral valve prolapse (MVP). Uncover relevant pathways and target genes for MVP pathophysiology. Leverage genetic data for MVP risk prediction.KEY FINDING: Sixteen genetic loci were significantly associated with MVP, including 13 novel loci. Interesting target genes at these loci included LTBP2, TGFB2, ALKP3, BAG3, RBM20, and SPTBN1. A risk score including clinical factors and a polygenic risk score, performed best at predicting MVP, with an area under the receiver operating characteristics curve of 0.677.TAKE-HOME MESSAGE: Mitral valve prolapse has a polygenic basis: many genetic variants cumulatively influence pre-disposition for disease. Disease risk may be modulated via changes to transforming growth factor-β signalling, the cytoskeleton, as well as cardiomyopathy pathways. Polygenic risk scores could enhance the MVP risk prediction.

Implementation of polygenic risk scores (PRS) may improve disease prevention and management but poses several challenges: the construction of clinically valid assays, interpretation for individual patients, and the development of clinical workflows and resources to support their use in patient care. For the ongoing Veterans Affairs Genomic Medicine at Veterans Affairs (GenoVA) Study we developed a clinical genotype array-based assay for six published PRS. We used data from 36,423 Mass General Brigham Biobank participants and adjustment for population structure to replicate known PRS–disease associations and published PRS thresholds for a disease odds ratio (OR) of 2 (ranging from 1.75 (95% CI: 1.57–1.95) for type 2 diabetes to 2.38 (95% CI: 2.07–2.73) for breast cancer). After confirming the high performance and robustness of the pipeline for use as a clinical assay for individual patients, we analyzed the first 227 prospective samples from the GenoVA Study and found that the frequency of PRS corresponding to published OR > 2 ranged from 13/227 (5.7%) for colorectal cancer to 23/150 (15.3%) for prostate cancer. In addition to the PRS laboratory report, we developed physician- and patient-oriented informational materials to support decision-making about PRS results. Our work illustrates the generalizable development of a clinical PRS assay for multiple conditions and the technical, reporting and clinical workflow challenges for implementing PRS information in the clinic.

BACKGROUND: Genetic variants in TNNI3K (troponin-I interacting kinase) have previously been associated with dilated cardiomyopathy (DCM), cardiac conduction disease, and supraventricular tachycardias. However, the link between TNNI3K variants and these cardiac phenotypes shows a lack of consensus concerning phenotype and protein function. METHODS: We describe a systematic retrospective study of a cohort of patients undergoing genetic testing for cardiac arrhythmias and cardiomyopathy including TNNI3K . We further performed burden testing of TNNI3K in the UK Biobank. For 2 novel TNNI3K variants, we tested cosegregation. TNNI3K kinase function was estimated by TNNI3K autophosphorylation assays. RESULTS: We demonstrate enrichment of rare coding TNNI3K variants in DCM patients in the Amsterdam cohort. In the UK Biobank, we observed an association between TNNI3K missense (but not loss-of-function) variants and DCM and atrial fibrillation. Furthermore, we demonstrate genetic segregation for 2 rare variants, TNNI3K-p.Ile512Thr and TNNI3K-p.His592Tyr, with phenotypes consisting of DCM, cardiac conduction disease, and supraventricular tachycardia, together with increased autophosphorylation. In contrast, TNNI3K-p.Arg556_Asn590del, a likely benign variant, demonstrated depleted autophosphorylation. CONCLUSIONS: Our findings demonstrate an increased burden of rare coding TNNI3K variants in cardiac patients with DCM. Furthermore, we present 2 novel likely pathogenic TNNI3K variants with increased autophosphorylation, suggesting that enhanced autophosphorylation is likely to drive pathogenicity.

Background: Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population. Methods: We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed. Results: Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc /decile of PRS =1.4 ms [95% CI, 1.3 to 1.5]; P =1.1×10 -196 ). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS). Conclusions: QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10−6), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.

Increased left ventricular (LV) mass (LVM) and LV hypertrophy (LVH) are risk markers for adverse cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance (CMR) is the gold standard for LVM estimation, but is challenging to obtain at scale, which has limited the power of prior genetic analyses. In the current study, we performed a genome-wide association study (GWAS) of CMR-derived LVM indexed to body surface area (LVMI) estimated using a deep learning algorithm within nearly 50,000 participants from the UK Biobank. We identified 12 independent associations (1 known atTTNand 11 novel) meeting genome-wide significance, implicating several candidate genes previously associated with cardiac contractility and cardiomyopathy. Greater CMR-derived LVMI was associated with higher risk of incident dilated (hazard ratio [HR] 2.58 per 1-SD increase, 95% CI 2.10-3.17) and hypertrophic (HR 2.62, 95% CI 2.09-3.30) cardiomyopathies. A polygenic risk score (PRS) for LVMI was also associated with incident hypertrophic cardiomyopathy within a separate set of UK Biobank participants (HR 1.12, 95% CI 1.01-1.12) and among individuals in an external Mass General Brigham dataset (HR 1.18, 95% CI 1.01-1.37). In summary, using CMR-derived LVM available at scale, we have identified 12 common variants associated with LVMI (11 novel) and demonstrated that both CMR-derived and genetically determined LVMI are associated with risk of incident cardiomyopathy.Journal Subject Termsmachine learning, left ventricular hypertrophy, genetics

BACKGROUND: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. OBJECTIVE: To evaluate if artificial intelligence–enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH. METHODS: We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression (“LVH-Net”). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I (“LVH-Net Lead I”) or lead II (“LVH-Net Lead II”) from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH. RESULTS: The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93–0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90–0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well. CONCLUSION: An artificial intelligence–enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.

Genetic cardiomyopathies are associated with increased risk for cardiac arrhythmias and sudden cardiac death. The management of ventricular arrhythmias (VAs) in patients with these conditions can be nuanced due to particular disease-based considerations, yet data specifically addressing management in these patients are limited. Here we describe the current evidence-based approach to the management of ventricular rhythm disorders in patients with genetic forms of cardiomyopathy, namely, hypertrophic cardiomyopathy, arrhythmogenic cardiomyopathy, left ventricular noncompaction, and Brugada syndrome, including recommendations from consensus guideline statements when available.

Background: Physician communication of lifetime risk prediction can improve patient understanding of atrial fibrillation (AF) risk and motivate lifestyle changes and preventive measures. Therefore, our objective was to assess how physicians perceive short-term and lifetime risks of atrial fibrillation (AF), using a survey. Methods: We invited American Heart Association (AHA) members to complete an online survey via email from November 2-22, 2021. Respondents were randomized to one of 32 vignettes characterized by AF risk factors (height, weight, current smoking, systolic blood pressure, diastolic blood pressure, use of antihypertensive medication, diabetes, history of heart failure, and history of myocardial infarction). Respondents were further randomized to either estimate the 5-year or lifetime AF risk on a 0-100% scale with 10% intervals (e.g., “10-19%”) or were shown the predicted 5-year or lifetime AF risk value and asked to pick the correct interpretation in a multiple-choice question. Results: Of 11,330 AHA professional members who received the emailed survey, 109 (1%) physicians responded (mean age 51 years, 33% women) (Table). Most respondents estimated the 5-year AF risk correctly (7/9, 78%) but none accurately estimated the lifetime risk (0/18, 0%). Most respondents interpreted the predicted 5-year risk correctly (21/28, 75%) but about half of respondents misinterpreted the lifetime risk (14/25, 56%). Conclusions: In a small sample of physicians, few respondents correctly estimated and interpreted the lifetime risk of AF, suggesting that physicians may not be familiar with this risk format. It may be necessary to study the frequency and manner in which physicians communicate lifetime risk of AF.

Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. Methods: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology–Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women’s Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. Results: The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767–0.836]; BWH, 0.752 [95% CI, 0.741–0.763]; UK Biobank, 0.732 [95% CI, 0.704–0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790–0.856]; BWH, 0.747 [95% CI, 0.736–0.759]; UK Biobank, 0.705 [95% CI, 0.673–0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r : MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). Conclusions: AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Background: Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease. Methods: In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait. Results: Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 ( P =1.2×10 −26 ), IGFBP3 ( P =4.8×10 −18 ), and PHACTR1 ( P =1.4×10 −13 ), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data. Conclusions: We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.

The electrocardiogram (ECG) is an inexpensive and widely available diagnostic tool, and therefore has great potential to facilitate disease detection in large-scale populations. Both cardiac and noncardiac diseases may alter the appearance of the ECG, though the extent to which diseases across the human phenotypic landscape can be detected on the ECG remains unclear. We developed a deep learning variational autoencoder model that encodes and reconstructs ECG waveform data within a multidimensional latent space. We then systematically evaluated whether associations between ECG encodings and a broad range of disease phenotypes could be detected using the latent space model by deriving disease vectors and projecting individual ECG encodings onto the vectors. We developed models for both 12- and single-lead ECGs, akin to those used in wearable ECG technology. We leveraged phecodes to generate disease labels using International Classification of Disease (ICD) codes for about 1,600 phenotypes in three different datasets linked to electronic health record data. We tested associations between ECG encodings and disease phenotypes using a phenome-wide association study approach in each dataset, and meta-analyzed the results. We observed that the latent space ECG model identified associations for 645 (40%) diseases tested in the 12-lead model. Associations were enriched for diseases of the circulatory (n=140, 82% of category-specific diseases), respiratory (n=53, 62%), and endocrine/metabolic (n=73, 45%) systems, with additional associations evident across the human phenome; results were similar for the single-lead models. The top ECG latent space association was with hypertension in the 12-lead ECG model, and cardiomyopathy in the single-lead ECG model (p-308for each). The ECG latent space model demonstrated a greater number of associations than ECG models using standard ECG intervals alone, and generally resulted in improvements in discrimination of diseases compared to models comprising only age, sex, and race. We further demonstrate how a latent space model can be used to generate disease-specific ECG waveforms and facilitate disease profiling for individual patients.

The QT interval is a heritable electrocardiographic measure associated with arrhythmia risk when prolonged. Here, the authors used a series of genetic analyses to identify genetic loci, pathways, therapeutic targets, and relationships with cardiovascular disease. The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

Background Treatment guidelines for nonvalvular atrial fibrillation (AF) recommend use of non–vitamin K antagonist oral anticoagulants (NOACs) over warfarin, yet clinical trials excluded individuals with post cardiac surgery AF. We sought to compare outcomes with NOACs vs warfarin for new onset post cardiac surgery AF. Methods We examined 26,522 patients from The Society of Thoracic Surgeons’ database with post cardiac surgery AF who were discharged on oral anticoagulation from July 2017–December 2018. Three primary outcomes were evaluated: 30-day mortality, major bleeding complications, and stroke/transient ischemic attack. Secondary outcomes included postoperative length of stay, 30-day myocardial infarction, venous thromboembolism, and pericardial effusion/tamponade. Results A total of 9769 (36.8%) participants were prescribed NOACs and 16,753 (63.2%) warfarin. In multivariable analysis, there was no association between type of anticoagulant and 30-day major bleeding complications (odds ratio [OR]NOAC/warfarin 0.76, 95% confidence interval [CI] 0.49-1.18), stroke/transient ischemic attack (ORNOAC/warfarin 0.94, 95% CI 0.53-1.67) or mortality (ORNOAC/warfarin 1.08, 95% CI 0.80-1.45). After stratification by renal function or isolated coronary bypass vs valve surgery, there remained no difference in the primary outcomes. Additionally, there was no difference in 30-day myocardial infarction (ORNOAC/warfarin 1.17, 95% CI 0.62-2.22), venous thromboembolism (ORNOAC/warfarin 0.91, 95% CI 0.47-1.78), or pericardial effusion/tamponade (ORNOAC/warfarin 1.09, 95% CI 0.80-1.47) between the 2 groups. NOAC therapy was associated with a half-day reduction in postoperative length of stay (βNOAC/warfarin –0.47, 95% CI –0.62 to –0.33). Conclusions NOACs are associated with a reduction in postoperative length of stay, without excess bleeding or other short-term complications, compared with warfarin. These findings support the broader use of NOACs as a safe alternative to warfarin in patients with post cardiac surgery AF at elevated stroke risk and acceptable bleeding risk.

Introduction: Although patients are able to easily record electrocardiograms using consumer devices, these are typically not shared with their clinicians. This article discusses the development and acceptability of a mobile application (app) that integrates with the electronic health record to facilitate screening for atrial fibrillation (AF). Methods: After app development and implementation, we compared workflows with and without the mobile app. Seven older adults used it during a prospective twice-daily 2-week home-based AF screening protocol and completed an acceptability survey with Likert scale responses. Results: Compliance with the screening protocol was 82%. Acceptability and usability was favorable. Patients reported confidence in the connection between the app and their medical record. Discussion: The availability of apps to capture data and facilitate a connection with health systems is critical. The app developed is a feasible solution for older patients with AF to self-monitor and report results to their health provider.

Background Obesity is an established risk factor for severe COVID-19 outcomes. The mechanistic underpinnings of this association are not well-understood. Objective To evaluate the mediating role of systemic inflammation in obesity-associated COVID-19 outcomes. Methods This hospital-based, observational study included 3828 SARS-CoV-2-infected patients who were hospitalized February to May 2020 at Massachusetts General Hospital (MGH) or Columbia University Irving Medical Center/New York Presbyterian Hospital (CUIMC/NYP). We use mediation analysis to evaluate whether peak inflammatory biomarkers (C-reactive protein [CRP], erythrocyte sedimentation rate [ESR], D-dimer, ferritin, white blood cell count and interleukin-6) are in the causal pathway between obesity (BMI ≥ 30) and mechanical ventilation or death within 28 days of presentation to care. Results In the MGH cohort (n = 1202), obesity was associated with greater likelihood of ventilation or death (OR = 1.73; 95% CI = [1.25, 2.41]; P = 0.001) and higher peak CRP (P Conclusion Findings support systemic inflammatory pathways in obesity-associated severe COVID-19 disease, particularly in patients

Gene expression causally contributes to phenotypic variability. However, since tissue-specific gene expression is difficult to measure at scale, inferring phenotypic risk for individuals using expression profiles can be difficult. We statistically imputed tissue-specific gene expression for 486,452 UK Biobank participants and demonstrated its applications using rich phenotypic data in the biorepository. We performed joint analysis of transcriptome-wide association studies (TWAS) and colocalization for six traits to prioritize tissue-gene pairs for therapeutics development. Using genes identified in TWAS, we constructed imputed transcriptome risk scores and showed their predictive utility even when conditioning on genome-wide polygenic risk scores. A phenome-wide association analysis for established therapeutic targets in whole blood recapitulated known biology and prioritized repurposing potentials supported by previous literature. Our study provides evidence supporting the utility of individual-level imputed transcriptomes for discovering novel gene-tissue-phenotype trios, expression-based phenotypic risk prediction, and identifying individuals with therapeutically targetable predisposition to disease.