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Background: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk., Methods: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system., Results: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified., Conclusions: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity., Competing Interests: None.

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.

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.

Background: DNA methylation is implicated in coronary heart disease (CHD), but current evidence is based on small, cross-sectional studies. We examined blood DNA methylation in relation to incident CHD across multiple prospective cohorts., Methods: Nine population-based cohorts from the United States and Europe profiled epigenome-wide blood leukocyte DNA methylation using the Illumina Infinium 450k microarray, and prospectively ascertained CHD events including coronary insufficiency/unstable angina, recognized myocardial infarction, coronary revascularization, and coronary death. Cohorts conducted race-specific analyses adjusted for age, sex, smoking, education, body mass index, blood cell type proportions, and technical variables. We conducted fixed-effect meta-analyses across cohorts., Results: Among 11 461 individuals (mean age 64 years, 67% women, 35% African American) free of CHD at baseline, 1895 developed CHD during a mean follow-up of 11.2 years. Methylation levels at 52 CpG (cytosine-phosphate-guanine) sites were associated with incident CHD or myocardial infarction (false discovery rate<0.05). These CpGs map to genes with key roles in calcium regulation (ATP2B2, CASR, GUCA1B, HPCAL1), and genes identified in genome- and epigenome-wide studies of serum calcium (CASR), serum calcium-related risk of CHD (CASR), coronary artery calcified plaque (PTPRN2), and kidney function (CDH23, HPCAL1), among others. Mendelian randomization analyses supported a causal effect of DNA methylation on incident CHD; these CpGs map to active regulatory regions proximal to long non-coding RNA transcripts., Conclusion: Methylation of blood-derived DNA is associated with risk of future CHD across diverse populations and may serve as an informative tool for gaining further insight on the development of CHD.

Background: Blacks have a higher incidence of out-of-hospital sudden cardiac death (SCD) in comparison with whites. However, the racial differences in the cumulative risk of SCD and the reasons for these differences have not been assessed in large-scale community-based cohorts. The objective of this study is to compare the lifetime cumulative risk of SCD among blacks and whites, and to evaluate the risk factors that may explain racial differences in SCD risk in the general population., Methods: This is a cohort study of 3832 blacks and 11 237 whites participating in the Atherosclerosis Risk in Communities Study (ARIC). Race was self-reported. SCD was defined as a sudden pulseless condition from a cardiac cause in a previously stable individual, and SCD cases were adjudicated by an expert committee. Cumulative incidence was computed using competing risk models. Potential mediators included demographic and socioeconomic factors, cardiovascular risk factors, presence of coronary heart disease, and electrocardiographic parameters as time-varying factors., Results: The mean (SD) age was 53.6 (5.8) years for blacks and 54.4 (5.7) years for whites. During 27.4 years of follow-up, 215 blacks and 332 whites experienced SCD. The lifetime cumulative incidence of SCD at age 85 years was 9.6, 6.6, 6.5, and 2.3% for black men, black women, white men, and white women, respectively. The sex-adjusted hazard ratio for SCD comparing blacks with whites was 2.12 (95% CI, 1.79-2.51). The association was attenuated but still statistically significant in fully adjusted models (hazard ratio, 1.38; 95% CI, 1.11-1.71). In mediation analysis, known factors explained 65.3% (95% CI 37.9-92.8%) of the excess risk of SCD in blacks in comparison with whites. The single most important factor explaining this difference was income (50.5%), followed by education (19.1%), hypertension (22.1%), and diabetes mellitus (19.6%). Racial differences were evident in both genders but stronger in women than in men., Conclusions: Blacks had a much higher risk for SCD in comparison with whites, particularly among women. Income, education, and traditional risk factors explained ≈65% of the race difference in SCD. The high burden of SCD and the racial-gender disparities observed in our study represent a major public health and clinical problem.

Background: Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke., Methods: To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from <1×10 -3 to <1×10 -8 in a prior independent genetic association study., Results: Incident AF occurred in 1032 individuals (5.5%). AF genetic risk scores were associated with new-onset AF after adjustment for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95% confidence interval, 1.13-1.46; P =1.5×10 -4 ) to 1.67 (25 variants; 95% confidence interval, 1.47-1.90; P =9.3×10 -15 ). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95% confidence interval, 1.39-4.58; P =2.7×10 -3 ). The effect persisted after the exclusion of individuals (n=70) with known AF (odds ratio, 2.25; 95% confidence interval, 1.20-4.40; P =0.01)., Conclusions: Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors but offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms., (© 2016 American Heart Association, Inc.)

Background: Most sudden cardiac death (SCD) events occur in the general population among persons who do not have any prior history of clinical heart disease. We sought to develop a predictive model of SCD among US adults., Methods: We evaluated a series of demographic, clinical, laboratory, electrocardiographic, and echocardiographic measures in participants in the ARIC study (Atherosclerosis Risk in Communities) (n=13 677) and the CHS (Cardiovascular Health Study) (n=4207) who were free of baseline cardiovascular disease. Our initial objective was to derive a SCD prediction model using the ARIC cohort and validate it in CHS. Independent risk factors for SCD were first identified in the ARIC cohort to derive a 10-year risk model of SCD. We compared the prediction of SCD with non-SCD and all-cause mortality in both the derivation and validation cohorts. Furthermore, we evaluated whether the SCD prediction equation was better at predicting SCD than the 2013 American College of Cardiology/American Heart Association Cardiovascular Disease Pooled Cohort risk equation., Results: There were a total of 345 adjudicated SCD events in our analyses, and the 12 independent risk factors in the ARIC study included age, male sex, black race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes mellitus, serum potassium, serum albumin, high-density lipoprotein, estimated glomerular filtration rate, and QTc interval. During a 10-year follow-up period, a model combining these risk factors showed good to excellent discrimination for SCD risk (c-statistic 0.820 in ARIC and 0.745 in CHS). The SCD prediction model was slightly better in predicting SCD than the 2013 American College of Cardiology/American Heart Association Pooled Cohort risk equations (c-statistic 0.808 in ARIC and 0.743 in CHS). Only the SCD prediction model, however, demonstrated similar and accurate prediction for SCD using both the original, uncalibrated score and the recalibrated equation. Finally, in the echocardiographic subcohort, a left ventricular ejection fraction <50% was present in only 1.1% of participants and did not enhance SCD prediction., Conclusions: Our study is the first to derive and validate a generalizable risk score that provides well-calibrated, absolute risk estimates across different risk strata in an adult population of white and black participants without a clinical diagnosis of cardiovascular disease., (© 2016 American Heart Association, Inc.)

Background: Asymptomatic individuals account for the majority of sudden cardiac deaths (SCDs). Development of effective, low-cost, and noninvasive SCD risk stratification tools is necessary., Methods and Results: Participants from the Atherosclerosis Risk in Communities study and Cardiovascular Health Study (n=20 177; age, 59.3±10.1 years; age range, 44-100 years; 56% female; 77% white) were followed up for 14.0 years (median). Five ECG markers of global electric heterogeneity (GEH; sum absolute QRST integral, spatial QRST angle, spatial ventricular gradient [SVG] magnitude, SVG elevation, and SVG azimuth) were measured on standard 12-lead ECGs. Cox proportional hazards and competing risks models evaluated associations between GEH electrocardiographic parameters and SCD. An SCD competing risks score was derived from demographics, comorbidities, and GEH parameters. SCD incidence was 1.86 per 1000 person-years. After multivariable adjustment, baseline GEH parameters and large increases in GEH parameters over time were independently associated with SCD. Final SCD risk scores included age, sex, race, diabetes mellitus, hypertension, coronary heart disease, stroke, and GEH parameters as continuous variables. When GEH parameters were added to clinical/demographic factors, the C statistic increased from 0.777 to 0.790 (P=0.008), the risk score classified 10-year SCD risk as high (>5%) in 7.2% of participants, 10% of SCD victims were appropriately reclassified into a high-risk category, and only 1.4% of SCD victims were inappropriately reclassified from high to intermediate risk. The net reclassification index was 18.3%., Conclusions: Abnormal electrophysiological substrate quantified by GEH parameters is independently associated with SCD in the general population. The addition of GEH parameters to clinical characteristics improves SCD risk prediction., (© 2016 American Heart Association, Inc.)

Background: ECG QRS duration, a measure of cardiac intraventricular conduction, varies ≈2-fold in individuals without cardiac disease. Slow conduction may promote re-entrant arrhythmias., Methods and Results: We performed a genome-wide association study to identify genomic markers of QRS duration in 5272 individuals without cardiac disease selected from electronic medical record algorithms at 5 sites in the Electronic Medical Records and Genomics (eMERGE) network. The most significant loci were evaluated within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium QRS genome-wide association study meta-analysis. Twenty-three single-nucleotide polymorphisms in 5 loci, previously described by CHARGE, were replicated in the eMERGE samples; 18 single-nucleotide polymorphisms were in the chromosome 3 SCN5A and SCN10A loci, where the most significant single-nucleotide polymorphisms were rs1805126 in SCN5A with P=1.2×10(-8) (eMERGE) and P=2.5×10(-20) (CHARGE) and rs6795970 in SCN10A with P=6×10(-6) (eMERGE) and P=5×10(-27) (CHARGE). The other loci were in NFIA, near CDKN1A, and near C6orf204. We then performed phenome-wide association studies on variants in these 5 loci in 13859 European Americans to search for diagnoses associated with these markers. Phenome-wide association study identified atrial fibrillation and cardiac arrhythmias as the most common associated diagnoses with SCN10A and SCN5A variants. SCN10A variants were also associated with subsequent development of atrial fibrillation and arrhythmia in the original 5272 "heart-healthy" study population., Conclusions: We conclude that DNA biobanks coupled to electronic medical records not only provide a platform for genome-wide association study but also may allow broad interrogation of the longitudinal incidence of disease associated with genetic variants. The phenome-wide association study approach implicated sodium channel variants modulating QRS duration in subjects without cardiac disease as predictors of subsequent arrhythmias.

Background: Despite a higher burden of standard atrial fibrillation (AF) risk factors, African Americans have a lower risk of AF than whites. It is unknown whether the higher risk is due to genetic or environmental factors. Because African Americans have varying degrees of European ancestry, we sought to test the hypothesis that European ancestry is an independent risk factor for AF., Methods and Results: We studied whites (n=4543) and African Americans (n=822) in the Cardiovascular Health Study (CHS) and whites (n=10 902) and African Americans (n=3517) in the Atherosclerosis Risk in Communities (ARIC) Study (n=3517). Percent European ancestry in African Americans was estimated with 1747 ancestry informative markers from the Illumina custom ITMAT-Broad-CARe array. Among African Americans without baseline AF, 120 of 804 CHS participants and 181 of 3517 ARIC participants developed incident AF. A meta-analysis from the 2 studies revealed that every 10% increase in European ancestry increased the risk of AF by 13% (hazard ratio, 1.13; 95% confidence interval, 1.03 to 1.23; P=0.007). After adjustment for potential confounders, European ancestry remained a predictor of incident AF in each cohort alone, with a combined estimated hazard ratio for each 10% increase in European ancestry of 1.17 (95% confidence interval, 1.07 to 1.29; P=0.001). A second analysis using 3192 ancestry informative markers from a genome-wide Affymetrix 6.0 array in ARIC African Americans yielded similar results., Conclusions: European ancestry predicted risk of incident AF. Our study suggests that investigating genetic variants contributing to differential AF risk in individuals of African versus European ancestry will be informative.