The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.

Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.

Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias.

BACKGROUND AND AIMS: Clonal haematopoiesis of indeterminate potential (CHIP), the age-related expansion of blood cells with preleukemic mutations, is associated with atherosclerotic cardiovascular disease and heart failure. This study aimed to test the association of CHIP with new-onset arrhythmias. METHODS: UK Biobank participants without prevalent arrhythmias were included. Co-primary study outcomes were supraventricular arrhythmias, bradyarrhythmias, and ventricular arrhythmias. Secondary outcomes were cardiac arrest, atrial fibrillation, and any arrhythmia. Associations of any CHIP [variant allele fraction (VAF) ≥ 2%], large CHIP (VAF ≥10%), and gene-specific CHIP subtypes with incident arrhythmias were evaluated using multivariable-adjusted Cox regression. Associations of CHIP with myocardial interstitial fibrosis [T1 measured using cardiac magnetic resonance (CMR)] were also tested. RESULTS: This study included 410 702 participants [CHIP: n = 13 892 (3.4%); large CHIP: n = 9191 (2.2%)]. Any and large CHIP were associated with multi-variable-adjusted hazard ratios of 1.11 [95% confidence interval (CI) 1.04-1.18; P = .001] and 1.13 (95% CI 1.05-1.22; P = .001) for supraventricular arrhythmias, 1.09 (95% CI 1.01-1.19; P = .031) and 1.13 (95% CI 1.03-1.25; P = .011) for bradyarrhythmias, and 1.16 (95% CI, 1.00-1.34; P = .049) and 1.22 (95% CI 1.03-1.45; P = .021) for ventricular arrhythmias, respectively. Associations were independent of coronary artery disease and heart failure. Associations were also heterogeneous across arrhythmia subtypes and strongest for cardiac arrest. Gene-specific analyses revealed an increased risk of arrhythmias across driver genes other than DNMT3A. Large CHIP was associated with 1.31-fold odds (95% CI 1.07-1.59; P = .009) of being in the top quintile of myocardial fibrosis by CMR. CONCLUSIONS: CHIP may represent a novel risk factor for incident arrhythmias, indicating a potential target for modulation towards arrhythmia prevention and treatment.

Atrial Fibrillation Occurring During Acute Hospitalization: A Scientific Statement From the American Heart Association.

Acute atrial fibrillation is defined as atrial fibrillation detected in the setting of acute care or acute illness; atrial fibrillation may be detected or managed for the first time during acute hospitalization for another condition. Atrial fibrillation after cardiothoracic surgery is a distinct type of acute atrial fibrillation. Acute atrial fibrillation is associated with high risk of long-term atrial fibrillation recurrence, warranting clinical attention during acute hospitalization and over long-term follow-up. A framework of substrates and triggers can be useful for evaluating and managing acute atrial fibrillation. Acute management requires a multipronged approach with interdisciplinary care collaboration, tailoring treatments to the patient's underlying substrate and acute condition. Key components of acute management include identification and treatment of triggers, selection and implementation of rate/rhythm control, and management of anticoagulation. Acute rate or rhythm control strategy should be individualized with consideration of the patient's capacity to tolerate rapid rates or atrioventricular dyssynchrony, and the patient's ability to tolerate the risk of the therapeutic strategy. Given the high risks of atrial fibrillation recurrence in patients with acute atrial fibrillation, clinical follow-up and heart rhythm monitoring are warranted. Long-term management is guided by patient substrate, with implications for intensity of heart rhythm monitoring, anticoagulation, and considerations for rhythm management strategies. Overall management of acute atrial fibrillation addresses substrates and triggers. The 3As of acute management are acute triggers, atrial fibrillation rate/rhythm management, and anticoagulation. The 2As and 2Ms of long-term management include monitoring of heart rhythm and modification of lifestyle and risk factors, in addition to considerations for atrial fibrillation rate/rhythm management and anticoagulation. Several gaps in knowledge related to acute atrial fibrillation exist and warrant future research.

Multiancestry Genome-Wide Association Study of Aortic Stenosis Identifies Multiple Novel Loci in the Million Veteran Program.

BACKGROUND: Calcific aortic stenosis (CAS) is the most common valvular heart disease in older adults and has no effective preventive therapies. Genome-wide association studies (GWAS) can identify genes influencing disease and may help prioritize therapeutic targets for CAS. METHODS: We performed a GWAS and gene association study of 14 451 patients with CAS and 398 544 controls in the Million Veteran Program. Replication was performed in the Million Veteran Program, Penn Medicine Biobank, Mass General Brigham Biobank, BioVU, and BioMe, totaling 12 889 cases and 348 094 controls. Causal genes were prioritized from genome-wide significant variants using polygenic priority score gene localization, expression quantitative trait locus colocalization, and nearest gene methods. CAS genetic architecture was compared with that of atherosclerotic cardiovascular disease. Causal inference for cardiometabolic biomarkers in CAS was performed using Mendelian randomization and genome-wide significant loci were characterized further through phenome-wide association study. RESULTS: We identified 23 genome-wide significant lead variants in our GWAS representing 17 unique genomic regions. Of the 23 lead variants, 14 were significant in replication, representing 11 unique genomic regions. Five replicated genomic regions were previously known risk loci for CAS (PALMD, TEX41, IL6, LPA, FADS) and 6 were novel (CEP85L, FTO, SLMAP, CELSR2, MECOM, CDAN1). Two novel lead variants were associated in non-White individuals (P<0.05): rs12740374 (CELSR2) in Black and Hispanic individuals and rs1522387 (SLMAP) in Black individuals. Of the 14 replicated lead variants, only 2 (rs10455872 [LPA], rs12740374 [CELSR2]) were also significant in atherosclerotic cardiovascular disease GWAS. In Mendelian randomization, lipoprotein(a) and low-density lipoprotein cholesterol were both associated with CAS, but the association between low-density lipoprotein cholesterol and CAS was attenuated when adjusting for lipoprotein(a). Phenome-wide association study highlighted varying degrees of pleiotropy, including between CAS and obesity at the FTO locus. However, the FTO locus remained associated with CAS after adjusting for body mass index and maintained a significant independent effect on CAS in mediation analysis. CONCLUSIONS: We performed a multiancestry GWAS in CAS and identified 6 novel genomic regions in the disease. Secondary analyses highlighted the roles of lipid metabolism, inflammation, cellular senescence, and adiposity in the pathobiology of CAS and clarified the shared and differential genetic architectures of CAS with atherosclerotic cardiovascular diseases.

A polygenic risk score predicts atrial fibrillation in cardiovascular disease.

AIMS: Interest in targeted screening programmes for atrial fibrillation (AF) has increased, yet the role of genetics in identifying patients at highest risk of developing AF is unclear. METHODS AND RESULTS: A total of 36,662 subjects without prior AF were analyzed from four TIMI trials. Subjects were divided into quintiles using a validated polygenic risk score (PRS) for AF. Clinical risk for AF was calculated using the CHARGE-AF model. Kaplan-Meier event rates, adjusted hazard ratios (HRs), C-indices, and net reclassification improvement were used to determine if the addition of the PRS improved prediction compared with clinical risk and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Over 2.3 years, 1018 new AF cases developed. AF PRS predicted a significant risk gradient for AF with a 40% increased risk per 1-SD increase in PRS [HR: 1.40 (1.32-1.49); P < 0.001]. Those with high AF PRS (top 20%) were more than two-fold more likely to develop AF [HR 2.45 (1.99-3.03), P < 0.001] compared with low PRS (bottom 20%). Furthermore, PRS provided an additional gradient of risk stratification on top of the CHARGE-AF clinical risk score, ranging from a 3-year incidence of 1.3% in patients with low clinical and genetic risk to 8.7% in patients with high clinical and genetic risk. The subgroup of patients with high clinical risk, high PRS, and elevated NT-proBNP had an AF risk of 16.7% over 3 years. The C-index with the CHARGE-AF clinical risk score alone was 0.65, which improved to 0.67 (P < 0.001) with the addition of NT-proBNP, and increased further to 0.70 (P < 0.001) with the addition of the PRS. CONCLUSION: In patients with cardiovascular conditions, AF PRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score and NT-proBNP.

Screening for Atrial Fibrillation in Older Adults at Primary Care Visits: VITAL-AF Randomized Controlled Trial.

BACKGROUND: Undiagnosed atrial fibrillation (AF) may cause preventable strokes. Guidelines differ regarding AF screening recommendations. We tested whether point-of-care screening with a handheld single-lead ECG at primary care practice visits increases diagnoses of AF. METHODS: We randomized 16 primary care clinics 1:1 to AF screening using a handheld single-lead ECG (AliveCor KardiaMobile) during vital sign assessments, or usual care. Patients included were ages ≥65 years. Screening results were provided to primary care clinicians at the encounter. All confirmatory diagnostic testing and treatment decisions were made by the primary care clinician. New AF diagnoses during the 1-year follow-up were ascertained electronically and manually adjudicated. Proportions and incidence rates were calculated. Effect heterogeneity was assessed. RESULTS: Of 30 715 patients without prevalent AF (n=15 393 screening [91% screened], n=15 322 control), 1.72% of individuals in the screening group had new AF diagnosed at 1 year versus 1.59% in the control group (risk difference, 0.13% [95% CI, -0.16 to 0.42]; P=0.38). In prespecified subgroup analyses, new AF diagnoses in the screening and control groups were greater among those aged ≥85 years (5.56% versus 3.76%, respectively; risk difference, 1.80% [95% CI, 0.18 to 3.30]). The difference in newly diagnosed AF between the screening period and the previous year was marginally greater in the screening versus control group (0.32% versus -0.12%; risk difference, 0.43% [95% CI, -0.01 to 0.84]). The proportion of individuals with newly diagnosed AF who were initiated on oral anticoagulants was not different in the screening (n=194, 73.5%) and control (n=172, 70.8%) arms (risk difference, 2.7% [95% CI, -5.5 to 10.4]). CONCLUSIONS: Screening for AF using a single-lead ECG at primary care visits did not affect new AF diagnoses among all individuals aged 65 years or older compared with usual care. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03515057.

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study.

BACKGROUND: Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential. METHODS: We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF. RESULTS: A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%-99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%-99.4%). CONCLUSIONS: A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04380415.

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

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.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population.

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.

Clinical and Genetic Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Atrial fibrillation (AF) is a common cause of stroke but may not be detectable at the time of stroke. We hypothesized that an AF polygenic risk score (PRS) can discriminate between cardioembolic stroke and noncardioembolic strokes. METHODS: We evaluated AF and stroke risk in 26 145 individuals of European descent from the Stroke Genetics Network case-control study. AF genetic risk was estimated using 3 recently developed PRS methods (LDpred-funct-inf, sBayesR, and PRS-CS) and 2 previously validated PRSs. We performed logistic regression of each AF PRS on AF status and separately cardioembolic stroke, adjusting for clinical risk score (CRS), imputation group, and principal components. We calculated model discrimination of AF and cardioembolic stroke using the concordance statistic (c-statistic) and compared c-statistics using 2000-iteration bootstrapping. We also assessed reclassification of cardioembolic stroke with the addition of PRS to either CRS or a modified CHA2DS2-VASc score alone. RESULTS: Each AF PRS was significantly associated with AF and with cardioembolic stroke after adjustment for CRS. Addition of each AF PRS significantly improved discrimination as compared with CRS alone (P<0.01). When combined with the CRS, both PRS-CS and LDpred scores discriminated both AF and cardioembolic stroke (c-statistic 0.84 for AF; 0.74 for cardioembolic stroke) better than 3 other PRS scores (P<0.01). Using PRS-CS PRS and CRS in combination resulted in more appropriate reclassification of stroke events as compared with CRS alone (event reclassification [net reclassification indices]+=14% [95% CI, 10%-18%]; nonevent reclassification [net reclassification indices]-=17% [95% CI, 15%-0.19%]) or the modified CHA2DS2-VASc score (net reclassification indices+=11% [95% CI, 7%-15%]; net reclassification indices-=14% [95% CI, 12%-16%]) alone. CONCLUSIONS: Addition of polygenic risk of AF to clinical risk factors modestly improves the discrimination of cardioembolic from noncardioembolic strokes, as well as reclassification of stroke subtype. Polygenic risk of AF may be a useful biomarker for identifying strokes caused by AF.

Accuracy and variability of cardiologist interpretation of single lead electrocardiograms for atrial fibrillation: The VITAL-AF trial.

BACKGROUND: Screening for atrial fibrillation (AF) using consumer-based devices capable of producing a single lead electrocardiogram (1L ECG) is increasing. There are limited data on the accuracy of physician interpretation of these tracings. The goal of this study is to assess the sensitivity, specificity, confidence, and variability of cardiologist interpretation of point-of-care 1L ECGs. METHODS: Fifteen cardiologists reviewed point-of-care handheld 1L ECGs collected from patients aged 65 years or older enrolled in the VITAL-AF clinical trial [NCT035115057] who underwent cardiac rhythm assessments with a 1L ECG using an AliveCor KardiaMobile device. Random sampling of 1L ECGs for cardiologist review was stratified by the AliveCor algorithm interpretation. A 12L ECG performed on the same day for clinical purposes was used as the gold standard. Cardiologists each reviewed a common sample of 200 1L ECG tracings and completed a survey associated with each tracing. Cardiologists were blinded to both the AliveCor algorithm and same day 12L ECG interpretation. For each tracing, study cardiologists were asked to assess the rhythm (sinus rhythm, AF, unclassifiable), report their assessment of the quality of the tracing, and rate their confidence in rhythm interpretation. The outcomes included the sensitivity, specificity, variability, and confidence in physician interpretation. Variables associated with each measure were identified using multivariable regression. RESULTS: The average sensitivity for AF was 77.4% (range 50%-90.6%, standard deviation [SD]=11.4%) and the average specificity was 73.0% (range 41.3%-94.6%, SD = 15.4%). The mean variability was 30.8% (range 0%-76.2%, SD = 23.2%). The average reviewer confidence of 1L ECG rhythm assessment was 3.6 out of 5 (range 2.5-4.2, SD = 0.6). Patient and tracing factors associated with sensitivity, specificity, variability, and confidence were identified and included age, body mass index, and presence of artifact. CONCLUSION: Cardiologist interpretation of point-of-care handheld 1L ECGs has modest diagnostic sensitivity and specificity with substantial variability for AF classification despite high confidence. Variability in cardiologist interpretation of 1L ECGs highlights the importance of confirmatory testing for diagnosing AF.

Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program.

All of Us is a biorepository aiming to advance biomedical research by providing various types of data in diverse human populations. Here we present a demonstration project validating the program's genomic data in 98,622 participants. We sought to replicate known genetic associations for three diseases (atrial fibrillation [AF], coronary artery disease, type 2 diabetes [T2D]) and two quantitative traits (height and low-density lipoprotein [LDL]) by conducting common and rare variant analyses. We identified one known risk locus for AF, five loci for T2D, 143 loci for height, and nine loci for LDL. In gene-based burden tests for rare loss-of-function variants, we replicated associations between TTN and AF, GIGYF1 and T2D, ADAMTS17, ACAN, NPR2 and height, APOB, LDLR, PCSK9 and LDL. Our results are consistent with previous literature, indicating that the All of Us program is a reliable resource for advancing the understanding of complex diseases in diverse human populations.

Arrhythmic risk prediction in arrhythmogenic right ventricular cardiomyopathy: external validation of the arrhythmogenic right ventricular cardiomyopathy risk calculator.

AIMS: 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. METHODS AND RESULTS: 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%. CONCLUSION: 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.

Research Priorities in Atrial Fibrillation Screening: A Report From a National Heart, Lung, and Blood Institute Virtual Workshop.

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute's virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.

Epigenetic Age and the Risk of Incident Atrial Fibrillation.

BACKGROUND: The most prominent risk factor for atrial fibrillation (AF) is chronological age; however, underlying mechanisms are unexplained. Algorithms using epigenetic modifications to the human genome effectively predict chronological age. Chronological and epigenetic predicted ages may diverge in a phenomenon referred to as epigenetic age acceleration (EAA), which may reflect accelerated biological aging. We sought to evaluate for associations between epigenetic age measures and incident AF. METHODS: Measures for 4 epigenetic clocks (Horvath, Hannum, DNA methylation [DNAm] PhenoAge, and DNAm GrimAge) and an epigenetic predictor of PAI-1 (plasminogen activator inhibitor-1) levels (ie, DNAm PAI-1) were determined for study participants from 3 population-based cohort studies. Cox models evaluated for associations with incident AF and results were combined via random-effects meta-analyses. Two-sample summary-level Mendelian randomization analyses evaluated for associations between genetic instruments of the EAA measures and AF. RESULTS: Among 5600 participants (mean age, 65.5 years; female, 60.1%; Black, 50.7%), there were 905 incident AF cases during a mean follow-up of 12.9 years. Unadjusted analyses revealed all 4 epigenetic clocks and the DNAm PAI-1 predictor were associated with statistically significant higher hazards of incident AF, though the magnitudes of their point estimates were smaller relative to the associations observed for chronological age. The pooled EAA estimates for each epigenetic measure, with the exception of Horvath EAA, were associated with incident AF in models adjusted for chronological age, race, sex, and smoking variables. After multivariable adjustment for additional known AF risk factors that could also potentially function as mediators, pooled EAA measures for 2 clocks remained statistically significant. Five-year increases in EAA measures for DNAm GrimAge and DNAm PhenoAge were associated with 19% (adjusted hazard ratio [HR], 1.19 [95% CI, 1.09-1.31]; P<0.01) and 15% (adjusted HR, 1.15 [95% CI, 1.05-1.25]; P<0.01) higher hazards of incident AF, respectively. Mendelian randomization analyses for the 5 EAA measures did not reveal statistically significant associations with AF. CONCLUSIONS: Our study identified adjusted associations between EAA measures and incident AF, suggesting that biological aging plays an important role independent of chronological age, though a potential underlying causal relationship remains unclear. These aging processes may be modifiable and not constrained by the immutable factor of time.

Clinical Application of a Novel Genetic Risk Score for Ischemic Stroke in Patients With Cardiometabolic Disease.

BACKGROUND: Genome-wide association studies have identified single-nucleotide polymorphisms that are associated with an increased risk of stroke. We sought to determine whether a genetic risk score (GRS) could identify subjects at higher risk for ischemic stroke after accounting for traditional clinical risk factors in 5 trials across the spectrum of cardiometabolic disease. METHODS: Subjects who had consented for genetic testing and who were of European ancestry from the ENGAGE AF-TIMI 48 (Effective Anticoagulation with Factor Xa Next Generation in Atrial Fibrillation), SOLID-TIMI 52 (Stabilization of Plaques Using Darapladib), SAVOR-TIMI 53 (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus), PEGASUS-TIMI 54 (Prevention of Cardiovascular Events in Patients With Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin), and FOURIER (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Patients With Elevated Risk) trials were included in this analysis. A set of 32 single-nucleotide polymorphisms associated with ischemic stroke was used to calculate a GRS in each patient and identify tertiles of genetic risk. A Cox model was used to calculate hazard ratios for ischemic stroke across genetic risk groups, adjusted for clinical risk factors. RESULTS: In 51 288 subjects across the 5 trials, a total of 960 subjects had an ischemic stroke over a median follow-up period of 2.5 years. After adjusting for clinical risk factors, a higher GRS was strongly and independently associated with increased risk for ischemic stroke (P trend=0.009). In comparison with individuals in the lowest third of the GRS, individuals in the middle and top tertiles of the GRS had adjusted hazard ratios of 1.15 (95% CI, 0.98-1.36) and 1.24 (95% CI 1.05-1.45) for ischemic stroke, respectively. Stratification into subgroups revealed that the performance of the GRS appeared stronger in the primary prevention cohort with an adjusted hazard ratio for the top versus lowest tertile of 1.27 (95% CI, 1.04-1.53), in comparison with an adjusted hazard ratio of 1.06 (95% CI, 0.81-1.41) in subjects with previous stroke. In an exploratory analysis of patients with atrial fibrillation and CHA2DS2-VASc score of 2, high genetic risk conferred a 4-fold higher risk of stroke and an absolute risk equivalent to those with CHA2DS2-VASc score of 3. CONCLUSIONS: Across a broad spectrum of subjects with cardiometabolic disease, a 32-single-nucleotide polymorphism GRS was a strong, independent predictor of ischemic stroke. In patients with atrial fibrillation but lower CHA2DS2-VASc scores, the GRS identified patients with risk comparable to those with higher CHA2DS2-VASc scores.

Rationale and Design of the Randomized Controlled Trial of New Oral Anticoagulants Versus Warfarin for Post Cardiac Surgery Atrial Fibrillation: The NEW-AF Trial.

OBJECTIVE: This manuscript describes the rationale and design of a randomized, controlled trial comparing outcomes with Warfarin vs Novel Oral Anticoagulant (NOAC) therapy in patients with new onset atrial fibrillation after cardiac surgery. BACKGROUND: New onset atrial fibrillation commonly occurs after cardiac surgery and is associated with increased rates of stroke and mortality. in nonsurgical patients with atrial fibrillation, NOACs have been shown to confer equivalent benefits for stroke prevention with less bleeding risk and less tedious monitoring requirements compared with Warfarin. However, NOAC use has yet to be adopted widely in cardiac surgery patients. METHODS: The NEW-AF study has been designed as a pragmatic, prospective, randomized controlled trial that will compare financial, convenience and safety outcomes for patients with new onset atrial fibrillation after cardiac surgery that are treated with NOACs versus Warfarin. RESULTS: Study results may contribute to optimizing the options for stroke prophylaxis in cardiac surgery patients and catalyze more widespread application of NOAC therapy in this patient population. CONCLUSIONS: The study is ongoing and actively enrolling at the time of the publication. The trial is registered with clinicaltrials.gov under registration number NCT03702582.

ReducinG stroke by screening for UndiAgnosed atRial fibrillation in elderly inDividuals (GUARD-AF): Rationale and design of the GUARD-AF randomized trial of screening for atrial fibrillation with a 14-day patch-based continuous ECG monitor.

BACKGROUND: Screening for atrial fibrillation (AF) is attractive because AF independently raises the risk of ischemic stroke, this risk is largely reversible by long-term oral anticoagulant therapy (OAC), and many patients with AF remain undiagnosed and untreated. Recent trials of one-time brief screening for AF have not produced a significant increase in the proportion of patients diagnosed with AF. Trials of longer-term screening have demonstrated an increase in AF diagnoses, primarily paroxysmal AF. To date, however, no trials have demonstrated that screening for AF results in lower rates of stroke. Clinical practice guidelines conflict in their level of support for screening for AF. METHODS: The GUARD-AF individually randomized trial is designed to test whether screening for AF in individuals age 70 years or greater using a 2-week single-lead electrocardiographic patch monitor can identify patients with undiagnosed AF and lead to treatment with OAC, resulting in a reduced rate of stroke in the screened population. The trial's efficacy end point is hospitalization for stroke (either ischemic or hemorrhagic) and the trial's safety end point is hospitalization for a bleeding event. End points will be ascertained via Medicare claims or electronic health records at 2.5 years after study start. Enrollment is based in primary care practices and the OAC decision for screen-detected cases is left to the patient and their physician. The initial planned target sample size was 52,000, with 26,000 allocated to either screening or to usual care. RESULTS: Trial enrollment was severely hampered by the novel coronavirus disease 2019 (COVID-19) pandemic and stopped at a total enrollment of 11,931 participants. Of 5,965 randomized to the screening arm, 5,713 patients (96%) returned monitors with analyzable results. Incidence of screen-detected and clinically detected AF and associated stroke and bleeding outcomes will be ascertained. CONCLUSIONS: GUARD-AF is the largest AF screening randomized trial using a longer-term patch-based continuous electrocardiographic monitor. The results will contribute important information on the yield of patch-based AF screening, the "burden" of AF detected (percent time in AF, longest episode), and physicians' OAC decisions as a function of AF burden. GUARD-AF's stroke and bleed results will contribute to pooled trial analyses of AF screening, thereby informing future studies and guidelines.

Population-Based Screening for Atrial Fibrillation.

Atrial fibrillation (AF) is a common and morbid arrhythmia. Stroke is a major hazard of AF and may be preventable with oral anticoagulation. Yet since AF is often asymptomatic, many individuals with AF may be unaware and do not receive treatment that could prevent a stroke. Screening for AF has gained substantial attention in recent years as several studies have demonstrated that screening is feasible. Advances in technology have enabled a variety of approaches to facilitate screening for AF using both medical-prescribed devices as well as consumer electronic devices capable of detecting AF. Yet controversy about the utility of AF screening remains owing to concerns about potential harms resulting from screening in the absence of randomized data demonstrating effectiveness of screening on outcomes such as stroke and bleeding. In this review, we summarize current literature, present technology, population-based screening considerations, and consensus guidelines addressing the role of AF screening in practice.

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.