Atrial Fibrillation Risk Score Research Articles

Abstract TMP101: Clinical and Genetic Risk Factors Predict Atrial Fibrillation Based on Hypertrophic Cardiomyopathy

Background: Clinical and genetic predispositions are significant in predicting atrial fibrillation (AF); however, their role in patients with hypertrophic cardiomyopathy (HCM) remains unclear. This study aims to elucidate the impact of clinical and genetic risk factors on the development of AF in patients with and without HCM. Methods: This retrospective analysis involved data from the UK Biobank cohort, collected between 2006 and 2010. The cohort comprised 500,543 individuals: 1,361 diagnosed with and 499,182 without HCM. Participants were stratified based on their validated polygenic risk score for AF: the bottom 10% constituted the low-risk genetic group, the top 10% comprised the high-risk genetic group, and the remainder comprised the intermediate-risk genetic group. We assessed the incidence of AF and cardiovascular complications and analyzed its predictors, including genetic risk. Results: We examined 1,180 patients with HCM (mean age 61.1±7.1, 63.0% male) and 476,238 participants without HCM (mean age 57.0±8.1, 45.3% male). During the 11.6-year follow-up period, the age- and sex-adjusted AF incidence rates for the low-, intermediate-, and high-risk genetic groups were 2.4, 3.6, and 5.4 per 100 person-years in patients with HCM and 0.2, 0.5, and 1.0 per 100 person-years in participants without HCM, respectively. Genetic risk was a less significant predictor of AF in HCM groups (HR:2.51, 95%CI:1.59–3.98) than in non-HCM groups (HR:4.77, 95%CI:4.47–5.09) ( P =0.008 for interaction). Clinical risk factors, including male sex, body mass index, hypertension, and previous myocardial infarction, were less significant predictors of AF in patients with HCM than in those without HCM. A high genetic risk was significantly associated with the risk of cardiovascular complications in participants with and without HCM. Conclusions: Genetic predisposition was associated with the development of AF and cardiovascular complications in people with and without HCM; this association was weaker in the HCM group.

Polygenic risk scores for atrial fibrillation and heart failure and the risk of stroke and dementia.

Atrial fibrillation and heart failure have both been suggested to increase stroke and dementia risk. However, in observational studies, reversed causation and unmeasured confounding may occur. To mitigate these issues, this study aims to investigate if higher genetic risk for atrial fibrillation and heart failure increases dementia and stroke risk. Data were obtained from the population-based Gothenburg H70 Birth Cohort Studies in Sweden. Participants (N = 984) were born in 1930 with baseline examinations at age 70, 75, 79 or 85 and follow-ups until age 88-89. Polygenic risk scores at the 5 × 10-8, 1 × 10-5, 1 × 10-3 and 1 × 10-1 thresholds were generated for atrial fibrillation and heart failure. Stroke was diagnosed based on self-reports, close-informant interviews, and the National Patient Register. Dementia was diagnosed based on neuropsychiatric examinations, close-informant interviews, and the National Patient Register. Cox regression analyses were performed, adjusted for sex, age at baseline and the first five principal components to correct for population stratification. Those within the highest atrial fibrillation-polygenic risk score tertile had a 1.5 (95% CI 1.09-2.03) increased risk of dementia (at the 1 × 10-5 threshold) and a 1.5 (95% CI 1.07-2.03) increased risk of stroke (at the 1 × 10-3 threshold) compared to the lowest tertile. Those within the highest heart failure-polygenic risk score tertile had a 1.6 (95% CI 1.19-2.27) increased risk of dementia (at the 5 × 10-8 threshold), but no increased risk of stroke (HR 1.2; 95% CI 0.83-1.60 at the 1 × 10-5 threshold), compared to the lowest tertile. When analysing the polygenic risk scores as a continuous variable, the associations were in the same direction, although weaker. This study, investigating genetic risk of atrial fibrillation and heart failure in relation to stroke and dementia, supports the increasing body of evidence suggesting that atrial fibrillation is associated with both stroke and dementia risk. Whether heart failure increases dementia risk is less established, but the present study found that genetic risk of heart failure increased dementia risk. The finding that genetic risk for heart failure did not increase stroke risk needs to be interpreted with caution, as it may be due to a lack of statistical power. There are guidelines on how to best treat atrial fibrillation to prevent stroke, but more knowledge is needed on how to treat atrial fibrillation and heart failure to prevent dementia.

Prediction of atrial and ventricular arrhythmias using multiple cardiovascular risk-factor polygenic risk scores.

Atrial fibrillation (AF) prediction improves by combining clinical scores with a polygenic risk score (PRS) for AF (AF-PRS), but there are limited studies of PRS for ventricular arrhythmia (VA) prediction. We assessed the value of including multiple PRS for cardiovascular risk factors (CV-PRS) for incident AF and VA prediction. We used 158,733 individuals of European ancestry from UK Biobank to build 3 models for AF: CHARGE-AF (AF1), AF1 + AF-PRS (AF2), AF2 + CV-PRS (AF3). Models for VA included sex and age (VA1), VA1 + coronary artery disease (CAD) PRS (CAD-PRS, VA2), and VA2 + CV-PRS (VA3), conducting separate analyses in subjects with and without ischemic heart disease (IHD). Performance was evaluated in individuals of European (N = 158,733), African (N = 7200), South Asian (N = 9241) and East Asian (N = 2076) ancestry from UK Biobank. AF2 had a higher C-index than AF1 (0.762 vs 0.746, P < .001), marginally improving to 0.765 for AF3 (P < .001, including PRS for heart failure, electrocardiogram and cardiac magnetic resonance measures). In South Asians, AF2 C-index was higher than AF1 (P < .001). For VA, the C-index for VA2 was greater than VA1 (0.692 vs 0.681, P < .001) in Europeans, which was also observed in South Asians (P < .001). VA3 improved prediction of VA in individuals with IHD. CV-PRS improved AF prediction compared to CHARGE-AF and AF-PRS. A CAD-PRS improved VA prediction, while CV-PRS contributed in IHD. AF- and CAD-PRS were transferable to individuals of South Asian ancestry. Our results inform of the use of CV-PRS for personalized screening.

Abstract 4113263: Serum Metabolomics Adds Predictive Value to Clinical and Polygenic Risk Scores for Atrial Fibrillation: A UK Biobank study of &gt;240,000 Patients

Background: Atrial fibrillation (AF) poses substantial morbidity and mortality burdens globally. It is critical to identify individuals at high-risk for AF to implement both lifestyle modifications and close monitoring for initiation of oral anticoagulants. Currently, risk prediction in AF relies on clinical scores such as CHARGE-AF. Small molecule metabolites are thought to play a role in AF pathogenesis, but the additional predictive value of metabolomics over clinical and genetic risk factors is unknown. Using the UK Biobank (UKBB), we sought to perform this evaluation. Methods: We included all UKBB participants with complete 1 H-NMR metabolite measurements. We excluded participants with prior history of AF or with incomplete AF Polygenic Risk Score (AF-PRS) or CHARGE-AF components. The final cohort included 240,628 patients. In-patient records, primary care notes, and death registries were queried to identify patients who had developed incident AF within 5 years of enrollment. The cohort was divided into a 80/20:Train/Test split. We compared the performance of an Elastic-Net regularized Cox Proportional Hazards (EN-CPH) model trained on CHARGE-AF, AF-PRS, and the full 170 metabolite panel to a CPH model trained only on CHARGE-AF and AF-PRS. Results: Within 5 years, 4,174 (1.7%) patients developed AF. Compared to the control population, the incident AF cases were more likely to be older (62 vs. 56, p&lt;0.001), male (65% vs 45%, p&lt;0.001), and have higher CHARGE-AF and AF-PRS scores (p&lt;0.001). After training the EN-CPH model on CHARGE-AF, AF-PRS, and 170 metabolites, the final model included 8 metabolites on top of CHARGE-AF (HR: 1.28) and AF-PRS (HR: 1.11). After adjusting for CHARGE-AF and AF-PRS, creatinine level was associated with increased risk of AF (HR: 1.01) while linoleic acid level (HR: 0.985) was associated with decreased risk. Furthermore, total cholesterol, esterified cholesterol, free cholesterol, cholesteryl esters in IDL, omega-6 fatty acids, and cholesterol in large LDL were associated with HR 0.993-0.999. The EN-CPH model out-performed the CPH model without metabolomics on the test set (AUC 0.786 vs 0.753, p &lt; 0.001). Conclusions: The addition of metabolomics to clinical and genomic risk scores improves prediction of 5-year incident AF within a general population. This study highlights the significance of the metabolome as an independent prognosticator of AF risk. Further study of the mechanisms by which selected metabolites alter AF risk is needed.

Abstract 4137717: Atrial Fibrillation in Valvular Heart Disease: Assessing the Impact of Genetics in the UK Biobank

Background: Atrial fibrillation (AF) in valvular heart disease (VHD) is typically linked to pressure or volume overload resulting in atrial remodeling. However, the independent contribution of genetics to AF in VHD beyond abnormal hemodynamics and other risk factors remains unclear. Objective: To study the role of a previously developed Polygenic Risk Score (PRS) for AF in predicting AF across different VHDs in the UK Biobank cohort. Methods: We analyzed UK Biobank data between 2000 and 2022. ICD codes were used to define incident AF and VHD status, including mitral valve (MV) disease [prolapse (MVP), regurgitation (MR) and stenosis (MS)] and aortic (AV) disease [stenosis (AS) and regurgitation (AR)]. Prior AF was excluded. Participants were divided into quintiles based on the PRS: the top quintile was defined as high genetic AF risk, second through fourth as intermediate genetic risk, and bottom quintile as low genetic risk. Results: Among 452841 participants with available PRS, 2238 had at least one VHD (385 MVP, 1058 MR, 81 MS, 421 AR, and 509 AS). AF incidence was 9.9%, 13.6%, and 19% in the low, intermediate and high genetic risk groups respectively, compared to 1.4%, 2.8%, and 5.7% in the general population (all p&lt;0.05) - Figure 1A . Cox regression models adjusted for cardiovascular risk factors showed different genetic effects on AF across VHD types. Censoring patients at death, including valve intervention as a time-dependent variable, and excluding AF within 30 days post-intervention, PRS independently predicted AF in MV disease (hazard ratio [HR] 1.7, p=0.03 for the intermediate risk/mid PRS; HR 2.3, p=0.002 for the high risk PRS), particularly in those with MVP (HR 3.1, p=0.03 for the high PRS). However, in those with AV disease, PRS was not significantly associated with AF (p=0.93 and p=0.07) - Figure 1B. Conclusion: In patients with VHD, AF PRS is significantly associated with an increased risk of developing AF. However, the genetic impact varies across different VHD. In AV disease, there is a minimal genetic contribution, and the elevated AF risk may be predominantly due to pressure or volume overload. In contrast, in MV disease, particularly in MVP, there is a substantial genetic influence on AF risk.

Predictors of Atrial Fibrillation After Thoracic Radiotherapy.

Atrial fibrillation (AF) has been associated with thoracic radiotherapy, but the specific risk with irradiating different cardiac substructures remains unknown. This study sought to examine the relationship between irradiation of cardiac substructures and the risk of clinically significant (grade≥3) AF. We analyzed data from patients who underwent definitive radiotherapy for localized cancers (non-small cell lung, breast, Hodgkin lymphoma, or esophageal) at our institution between 2004 and 2022. The 2-Gy fraction equivalent dose was calculated for cardiac substructures, including the pulmonary veins (PVs), left atrium, sinoatrial node, and left coronary arteries (the left main, left anterior descending, and left circumflex arteries). Competing risk models (subdistribution HRs [sHRs]) for AF incidence were adjusted for the Mayo AF risk score (MAFRS). Among 539 patients, the median follow-up was 58.8months. The 5-year cumulative incidence of AF was 11.1% for non-small cell lung cancer, 8.3% for esophageal cancer, 1.3% for breast cancer, and 0.8% for Hodgkin lymphoma. Increased AF risk was associated with a higher PV maximum dose (dmax) (sHR: 1.22; P< 0.001), larger left atrial volume (sHR: 1.01; P = 0.002), greater smoking history in pack-years (sHR: 1.01; P = 0.010), and higher MAFRS (sHR: 1.16; P<0.001). PV dmax remained a significant predictor of AF across different MAFRS subgroups (P interaction=0.11), and a PVdmax>39.7Gy was linked to a higher AF risk, even when stratified by MAFRS. PV dmax is a significant predictor of grade≥3 AF regardless of underlying risk factors. These findings highlight the importance of cardiac substructures in radiation toxicity and suggest that various PV dose metrics should be further validated in clinical settings.

Left atrial function for predicting atrial fibrillation: a machine learning approach

Abstract Background Common risk scores for atrial fibrillation (AF) are based on clinical risk factors, and little is known about the contribution of left atrial (LA) function parameters to risk assessment. Purpose To determine whether a machine learning-based model that includes LA function parameters outperforms a commonly used risk scores, the CHARGE-AF, in predicting AF. Methods Participants in the Atherosclerosis Risk in Communities (ARIC) Study (a community-based cohort study in the USA) who attended visit 5 (2011-2013) and without a history of AF were included in this analysis. Incident AF was ascertained from ECG during study visits, hospitalization discharge codes, and death certificates. Random survival forest methodology was used to build a prediction model for AF; candidate variables included traditional clinical risk factors and measures of LA structure and function. Dataset was randomly split into a training (75%) and testing (25%) cohort. For each variable, importance was measured based on the reduction in Harrell’s c-statistic if the variable was randomly permuted prior to obtaining predicted values. Variables significantly associated with AF were included in the final model. Model calibration was tested using the Greenwood-Nam-D'Agostino (GND) test. Discrimination ability of our model was assessed in the testing cohort using Harrell’s c-statistic and compared with that of the CHARGE-AF model. Results 4,909 participants (59% female, 22% Black, mean age 75 years) were included. During a median follow-up of 7.0 years (1st-3rd quartile: 5.4-7.8), 650 participants developed AF. Variables significantly associated with incident AF were race, triglycerides, systolic blood pressure, coronary heart disease, age, LA maximal volume index, NT-proBNP, and LA reservoir strain (Figure 1). LA reservoir strain was the variable most strongly associated with AF. The final model showed good discrimination (c-statistic (95% CI): 0.734 (0.698-0.768)). The GND test did not detect statistically significant miscalibration (GND p=0.08). Compared with our model, CHARGE-AF had lower discrimination ability (c-statistic (95% CI): 0.635 (0.594-0.677)) and the difference was statistically significant (difference in c-statistic (95% CI): 0.099 (0.043-0.152)). Conclusion A machine learning-based model with few variables including LA function outperforms a commonly used risk model in predicting AF. Incorporation of LA functional assessment may help inform clinicians regarding individual risk of AF.

Interethnic validation of an artificial intelligence algorithm for prediction of atrial fibrillation using sinus rhythm electrocardiogram

Abstract Background Previous research has demonstrated acceptable diagnostic accuracy of artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation for identifying paroxysmal atrial fibrillation (AF). However, interethnic validations of the AI algorithms have not been widely conducted. Purpose We aimed to develop our own AI model for the identification of paroxysmal AF based on SR ECGs in the Korean population and to validate its diagnostic performance in Brazilian citizens. Methods We trained a Transformer-based vision network on 90% of a dataset comprising 808,194 ECGs from 121,282 patients at Seoul National University Bundang Hospital (2003-2020). The remaining 10% of the dataset was used for internal validation. The model was trained to compute a risk score for paroxysmal AF or new-onset AF within 2 years. External validation was conducted using non-AF ECGs from the CODE 15% dataset provided by the Telehealth Network of Minas Gerais, Brazil. Results In internal validation, our AI model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.911 (95% CI: 0.902 - 0.921), with a sensitivity of 78.1% and a specificity of 89.0%. Subgroup analyses showed an AUROC of 0.893 (95% CI: 0.876 - 0.909) for patients in routine health checkups or outpatient settings, and 0.854 for patients with "Normal ECG" interpretations. In external validation with the CODE 15% dataset, the AI model exhibited an AUROC of 0.884 (95% CI: 0.869-0.900), which increased to 0.906 (95% CI: 0.893-0.919) when adjusted for age and sex (Figure 1). In the subset of patients with "Normal ECG" interpretations, the AUROC was 0.826 (95% CI: 0.769-0.883), increasing to 0.861 (95% CI: 0.814-0.908) after applying the same adjustments. Conclusions Our AI-powered SR ECG interpretation model demonstrated excellent diagnostic performance in predicting paroxysmal AF, with valid performance in the Brazilian population as well. This suggests that the model has potential for broad application across different ethnic groups.Figure 1

Polygenic risk score for prediction of atrial fibrillation according to presence of the hypertrophic cardiomyopathy

Abstract Background The significance of genetic predisposition in predicting atrial fibrillation (AF) is well recognized, yet its role in hypertrophic cardiomyopathy (HCM) patients remains less clear. Objective In this study, we aimed to elucidate the impact of genetic risk on the development of AF in both HCM and non-HCM patients. Methods This retrospective analysis examined 1,180 HCM patients (mean age 61.1±7.1, 63.0% male) and 476,238 non-HCM patients (mean age 57.0±8.1, 45.3% male) from the UK Biobank. Participants were stratified based on their validated polygenic risk score (PRS) for AF: the bottom 10% constituted the low genetic risk group, the top 10% comprised the high genetic risk group, and the remainder fell into the intermediate genetic risk group. We assessed the incidence of AF and major cardiovascular events and analyzed predictors, including genetic risk. Results Among HCM patients, no significant differences in baseline characteristics were observed based on genetic risk, except for the rate of CIED implantation (P=0.036). Conversely, among non-HCM patients, variations in age, gender, and comorbidities were noted according to genetic risk. The HCM patients were categorized into three groups based on risk, with respective AF incidence rates of 2.4, 3.6, and 5.4 events per 100 person-years, respectively. In non-HCM patients, the incidence of AF per 100 person-years was 0.2, 0.5, and 1.0, respectively. Genetic risk emerged as a significant predictor of AF in both HCM and non-HCM patients, although with a higher hazard ratio observed in non-HCM patient (HR, 2.51; 95% CI, 1.59–3.98 vs. HR, 4.77; 95% CI, 4.47–5.09) (P=0.003 for interaction). In non-HCM patients, predictors of AF included age, male sex, body mass index, smoking history, and a history of hypertension, diabetes, heart failure, stroke, and myocardial infarction, in addition to PRS. However, in HCM patients, only age, diabetes, and a history of stroke were identified as independent predictors of AF. Moreover, high genetic risk was significantly associated with the risk of major adverse cardiovascular events. Conclusions In conclusion, our study indicates associations between genetic predisposition and AF occurrence in both HCM and non-HCM patients.

Socioeconomic deprivation and atrial fibrillation risk: an observational and gene-environment interaction analysis

Abstract Background The association between socioeconomic deprivation and incident atrial fibrillation (AF) is debated, along with limited evidence on how genetic predisposition to AF interacts with socioeconomic deprivation and AF risk. Purpose To evaluate the association and gene-environmental interaction between socioeconomic deprivation and the risk of incident AF. Methods From the UK Biobank prospective cohort, we selected participants who were free from previous AF. Participants with missing values and those who did not pass the genetic quality control were excluded. As an indicator of socioeconomic deprivation, we utilised the indices of multiple deprivation (IMD), which is calculated from scores of multiple socioeconomic factors, including crime, education, employment, health, housing, income, and living environment. Participants were divided into 3 groups by IMD; the top 20% as high IMD (most deprived), the bottom 20% as low IMD, and 20-80% as mid IMD. Using a polygenic risk score (PRS) for AF generated using a Bayesian approach applied to external genome-wide association study data, the interaction between genetic risk and socioeconomic deprivation was assessed. The primary endpoint was incident AF during a median of 13.7 years of follow-up. Results A total of 377,386 (aged 56.5±8.1 years, 45.2% male) were analysed. The high IMD group (n=85,108) was younger and had a higher proportion of comorbidities than the mid or low-IMD groups. An increase in IMD as a continuous variable was significantly associated with an increased risk of incident AF. The high IMD group showed a significantly increased risk of AF (multivariable-adjusted hazard ratio [aHR]=1.12, 95% confidence interval=1.07-1.17, P&amp;lt;0.001) compared with the low IMD group (incidence rate 7.7 vs. 4.8/1,000 person-year). All components of IMD except housing score significantly increased the risk of incident AF (aHR ranged from 1.07 to 1.15). Within each IMD group, higher AF PRS groups were significantly associated with an increased risk of incident AF with the low AF PRS group as a reference, showing aHRs of 1.50 for mid-PRS and 2.57 for high-PRS in the low IMD group, 1.60 and 2.65 in the mid-IMD group, and 1.55 and 2.33 in the high IMD group, respectively (all P&amp;lt;0.001). The impact of socioeconomic deprivation was attenuated in participants with high AF PRS (P for interaction=0.042), mainly driven by the interaction effect of crime (P for interaction [Pi]=0.005), employment deprivation (Pi=0.005), and health deprivation scores (Pi=0.043). Conclusion Socioeconomically deprived individuals had a significantly higher risk of AF, and the effect was partially modified by genetic predisposition to AF. A personalised approach to applying AF screening for high-risk individuals may be more efficacious. Legal measures and policies to enhance public health by alleviating socioeconomic inequalities are needed to mitigate AF risk and improve cardiovascular health outcomes.

Enhanced detection of damaged myocardium and risk stratification in hypertrophic cardiomyopathy using integrated [68Ga]Ga-FAPI-04 PET/CMR imaging.

This study aims to explore the correlation between PET and CMR in integrated [68Ga]Ga-FAPI-04 PET/CMR multimodal imaging and its value in the diagnosis and risk assessment of hypertrophic cardiomyopathy (HCM). This study included 20 HCM patients and 11 age- and gender-matched controls. PET analysis evaluated left ventricular (LV) [68Ga]Ga-FAPI-04 uptake, including SUVmax, TBR, cardiac fibroblast activity (CFA) and volume (CFV), and total SUV of the 16 segments. CMR tissue characterization parameters included cardiac function, myocardial thickness, late gadolinium enhancement (LGE), relaxation time, extracellular volume (ECV), and peak strain parameters. The 5-year sudden cardiac death (SCD) risk score and the 2-year and 5-year atrial fibrillation (AF) risk scores were calculated for each patient. The study analyzed differences between HCM patients and controls, the correlation between [68Ga]Ga-FAPI-04 PET and concurrent CMR imaging results, and the predictive value of PET/CMR. The FAPI uptake, myocardial mass, myocardial thickness, and T1/T2 mapping values were significantly higher in HCM patients compared to controls. Twenty HCM patients and their 320 myocardial segments were discussed. Increased [68Ga]Ga-FAPI-04 uptake in the left ventricular wall was observed in 95% (19/20) of the patients, covering 48.8% (156/320) of the segments. On concurrent CMR, 80% (16/20) of the patients showed LGE, including 95 (29.7%) segments. The FAPI(+)LGE(+) segments exhibited the highest myocardial PET uptake, greatest thickness, longest T1/T2 native values, largest ECV value and the greatest loss of myocardial strain capacity (P < 0.05). There was a significant correlation between FAPI uptake and CMR parameters (P < 0.05). Higher [68Ga]Ga-FAPI-04 uptake showed a positive correlation with SCD and AF risk scores (P < 0.05). The number of LGE(+) segments, mapping parameters, and ECV values in CMR also had prognostic significance. Combining PET with CMR aided in further risk stratification of HCM. [68Ga]Ga-FAPI-04 PET/CMR multimodal imaging has potential value in the detection of damaged myocardial lesions and risk assessment of HCM patients. [68Ga]Ga-FAPI-04 PET can detect more affected myocardium compared to CMR, and segments with abnormalities in both PET and CMR show more severe myocardial damage.

Smoking timing, genetic susceptibility, and the risk of incident atrial fibrillation: a large prospective cohort study.

Although smoking is a well-known risk factor for atrial fibrillation (AF), the association of smoking timing with AF risk remains unclear. This study aimed to prospectively investigate the association of smoking timing with the risk of incident AF and test the modification effect of genetic susceptibility. A total of 305 627 participants with detailed information for time from waking to the first cigarette were enrolled from UK Biobank database. The Cox proportional hazard model was employed to assess the relationship between smoking timing and AF risk. The weighted genetic risk score for AF was calculated. Over a median 12.2-year follow-up, 13 410 AF cases were documented. Compared with non-smokers, time from waking to the first cigarette showed gradient inverse associations with the risk of incident AF (P-trend <0.001). The adjusted hazard ratio related to smoking timing was 1.13 [95% confidence interval (CI): 0.96-1.34] for >120 min, 1.20 (95% CI: 1.01-1.42) for 61-120 min, 1.34 (95% CI: 1.19-1.51) for 30-60 min, 1.43 (95% CI: 1.26-1.63) for 5-15 min, and 1.49 (95% CI: 1.24-1.63) for <5 min, respectively. Additionally, we found that the increased risk of AF related to shorter time from waking to the first cigarette was strengthened by the genetic susceptibility to AF. Our findings suggest gradient inverse association between time from waking to the first cigarette and risk of incident AF, and the association is strengthened by the genetic susceptibility to AF.

Physical frailty, genetic predisposition, and incident arrhythmias.

Cross-sectional evidence suggests a possible link between frailty and atrial fibrillation (AF). It remains unclear whether frailty and incident arrhythmias are longitudinally associated. This study aimed to determine whether the frailty phenotype is longitudinally associated with incident arrhythmias, especially AF. In this prospective cohort of UK Biobank, individuals with arrhythmias at baseline, those without data for frailty phenotype, and no genetic data were excluded. Five domains of physical frailty, including weight loss, exhaustion, low physical activity, low grip strength, and slow gait speed, were assessed. A total of 142 single-nucleotide polymorphisms was used to calculate the polygenic risk score (PRS) for AF. Hospital inpatient records and death records were used to identify incident arrhythmias. This study included 464154 middle-aged and older adults (mean age 56.4±8.1years, 54.7% female) without arrhythmia at baseline. During a median follow-up of 13.4years (over 5.9 million person-years), 46454 new-onset arrhythmias cases were recorded. In comparison with non-frailty, the multivariable-adjusted hazard ratios (HRs) of AF were 1.12 (95% CI: 1.09, 1.15, P<0.0001) and 1.44 (95% CI: 1.36, 1.51, P<0.0001) for participants with pre-frailty and frailty, respectively. Similar associations were observed for other arrhythmias. We found that slow gait speed presented the strongest risk factor in predicting all arrhythmias, including AF (HR 1.34, 95% CI: 1.30, 1.39), bradyarrhythmias (HR 1.30, 95% CI: 1.22, 1.37), conduction system diseases (HR 1.29, 95% CI: 1.22, 1.36), supraventricular arrhythmias (HR 1.32, 95% CI: 1.19, 1.47), and ventricular arrhythmias (HR 1.37, 95% CI: 1.25, 1.51), with all P values <0.0001. In addition to slow gait speed, weight loss (HR 1.13, 95% CI: 1.09, 1.16, P<0.0001) and exhaustion (HR 1.11, 95% CI: 1.07, 1.14, P<0.0001) were significantly associated with incident AF, whereas insignificant associations were observed for physical activity (HR 1.03, 95% CI: 0.996, 1.08, P=0.099) and low grip strength (HR 1.00, 95% CI: 0.97, 1.03, P=0.89). We observed a significant interaction between genetic predisposition and frailty on incident AF (P for interaction <0.0001), where those with frailty and the highest tertile of PRS had the highest risk of AF (HR 3.34, 95% CI: 3.08, 3.61, P<0.0001) compared with those with non-frailty and the lowest tertile of PRS. Physical pre-frailty and frailty were significantly and independently associated with incident arrhythmias. Although direct causal inference still needs to be further validated, these results suggested the importance of assessing and managing frailty for arrhythmia prevention.

Polygenic risk score for Atrial Fibrillation as a predictor for the incidence of Sick Sinus Syndrome: A comprehensive analysis from the UK biobank

Abstract Background Sick sinus syndrome (SSS) and atrial fibrillation (AF) often coexist, demonstrating a bidirectional relationship. The polygenic risk score (PRS) represents the cumulative risk of disease onset based on variations at multiple genetic loci and is known as a predictor for disease incidence. However, the PRS for SSS remains under-investigated. This study aims to evaluate whether the PRS for AF can predict the incidence of SSS in the general population. Methods The UK biobank, comprising of about 500,000 participants aged 40 to 70, was established across 22 centres in the UK between 2006 and 2010. After excluding missing values, prior AF history, and valvular heart disease, a total of 101,654 participants without AF (median age, 56.0 [interquartile range (IQR), 49.0–63.0] years; 44,851 [44.1%] male) were analyzed. Participants were divided into quartiles according to a validated PRS for AF. Throughout the observation period, the incidence of SSS event was documented. Results During a follow-up period of 11.7 (IQR 11.0-12.5) years, a total of 157 cases of Sick sinus syndrome were identified. In the new-onset SSS group, age, PRS for AF, BMI, and waist measurements were significantly higher. Additionally, there was a statistically significant increase in the proportions of male sex, hypertension, previous MI, hypothyroidism, dyslipidemia, and ESRD/CKD. The Kaplan-Meier analysis demonstrated an association between the highest quartile and an increased incidence of SSS compared to the other quartiles. (Log-rank P &amp;lt; 0.001) The Cox regression model revealed that a PRS for AF was associated with an increased risk of incidence of sick sinus syndrome (aHR 1.29, 95% CI 1.10-1.51, p-value 0.002). Notably, the discriminative power of our model was substantial, yielding a C-index of 0.746. Conclusion In our study, the PRS for AF effectively predicted the incidence of SSS. In the absence of a dedicated PRS for SSS, the AF PRS may offer insights into developing one for SSS.

Diabetes status, genetic susceptibility, and incident arrhythmias: A prospective cohort study of 457,151 participants

AimsThe association of diabetes onset age and duration with incident arrhythmias remains unclear. This study evaluates the association of diabetes onset age and duration with incident arrhythmias and assesses modifications by the genetic predisposition to atrial fibrillation (AF). MethodsWe included 457,151 participants from the UK Biobank study. Multivariable Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) were used for the association between diabetes status, genetic predisposition, and risk of incident arrhythmias. The polygenic risk score (PRS) for AF comprised 142 single-nucleotide variants. ResultsOver 12 years of follow-up, we documented 23,518 AF, 9079 bradyarrhythmia, 9280 conduction system diseases, 3358 supraventricular arrhythmias, and 3095 ventricular arrhythmias. Compared with non-diabetes, the risks of AF increased by 19%, 25%, and 36% for those with diabetes durations <5, 5–9, and ≥10 years, respectively. After multivariate adjustment, with the increase in diabetes onset age, the HRs of outcomes were gradually attenuated. The multivariable-adjusted HRs (95% CI) of diabetes for AF were 1.46 (1.24–1.71) in early middle age (<55 years), 1.21 (1.12–1.30) in late middle age (55–64 years), and 1.15 (1.06–1.24) in the elderly population (≥65 years). A significant interaction between diabetes status and AF-PRS for incident AF was observed (P for interaction <0.001). The same trends were observed for the other arrhythmias. ConclusionsDiabetes was associated with higher risks of incident arrhythmias, and younger age at onset of diabetes was significantly associated with higher risk of subsequent arrhythmias.

Abstract 97: Acute Ischemic Stroke Despite Anticoagulant Use: The Neuro-AFib Study

Background: Acute ischemic stroke (AIS) admissions in patients with atrial fibrillation (AF) using anticoagulants (AIS-despite-AC) are commonly seen in stroke units but data on their frequency, causes, and outcomes are scarce. Methods: The Neuro-AFib study is a multicenter effort geared toward elucidating the causes and consequences of strokes in a contemporary AF cohort. Detailed clinical, laboratory and multimodal imaging data from known AF patients consecutively admitted to 20 stroke centers with an IS between 1/2018-12/2019 were used to define characteristics of AIS-despite-AC and compared to AIS-off-AC. Results: Out of 4456 patients with known AF prior to the AIS, 2051 (46%) were using anticoagulants. Patients who had AIS-despite-AC were younger (76.8 + 11 vs 77.8 + 12, p=0.007), had higher mean CHA 2 DS 2 -VASc scores (4.57 + 1.7 vs 4.21 + 1.7, p&lt;0.001), and they were more likely to have permanent AF (23.5% vs 19.6%, p=0.002) and a past-history of stroke/TIA (36% vs 25%, p&lt;0.001) when compared to non-AC group. Chronic embolic (non-lacunar) infarcts on imaging were more common among AIS-despite-AC (34.5% vs 26.7%, p&lt;0.001). Acute large vessel occlusion (LVO) was common among AIS-despite-AC (48%). The pattern of the acute infarcts (numbers, embolic features) was not different. The calculated acute infarct volume was large (34ml) and mean NIHSS was 10.4 for AIS-despite-AC. Intravenous thrombolysis was used much less commonly for AIS-despite-AC (10.5% vs 32.1%, p&lt;0.001). Symptomatic carotid disease was suspected in 2.4%, a hypercoagulable condition in 5.7%, whereas acute lacunar infarct was found in 6.5% of all patients. Death or significant disability (mRS 3-5) at hospital discharge were common in AIS on vs off AC (70.6% vs 75.2%, p&lt;0.001). Conclusions: Based on a large multicenter cohort, AF patients who have AIS-despite-AC have higher AF load and risk scores, most don't have a concurrent etiology and they are more likely to have recurrent embolic events. The stroke severity, common presence of LVO, infarct size, and poor outcomes are alarming features, and most patients are unable to receive thrombolysis because of AC use. Overall, detection of AF patients at risk of AIS-despite-AC and development of specific preventive modalities should be priorities.

Polygenic risk scores for atrial fibrillation and heart failure and the risk of stroke and dementia - a population-based study

Polygenic risk scores for atrial fibrillation and heart failure and the risk of stroke and dementia - a population-based study

Genetic risk, modifiable risk factors, and atrial fibrillation in the Danish diet, cancer, and health cohort

Abstract Background The etiology of AF is multifactorial. Genetic susceptibility and lifestyle contribute to atrial fibrillation (AF) risk.(1,2) However, the relationship between combined genetic predisposition and lifestyle risk factors on AF risk is unclear. Purpose We aimed to assess a possible interaction between modifiable risk factors and genetics on AF risk. Methods We included AF cases and a randomly drawn subcohort of 4,040 participants from the Danish Diet, Cancer, and Health study. Modifiable risk factors were assessed including smoking, body mass index, physical activity, diet, alcohol consumption, total cholesterol, and blood pressure. Based on cumulation of risk factors, a risk factor profile was determined for all participants and categorized as poor, intermediate, and ideal. Based on DNA sequence polymorphisms, we calculated a genetic risk score for AF, and categorized participants as having a low (quintile 1), intermediate (quintile 2-4) or high (quintile 5) genetic risk. Diagnoses of AF and/or atrial flutter were obtained from the Danish National Patient Register. We used weighted Cox proportional hazards regression to assess hazard ratios of AF. Results 3,094 AF cases occurred during a median follow-up of 12.9 years. Median age at baseline in the subcohort was 55 years (25th-75th percentile 52-60) and 2,092 (55%) were women. Participants at intermediate and high genetic risk had higher rates of AF compared with those at low genetic risk. Likewise, intermediate, and poor risk factor profiles were associated with higher rates of AF compared with those having ideal risk factor profiles. Weighted cumulative incidence proportions and hazard ratios of AF according to genetic risk and modifiable risk factors are shown in Figure 1. Within each category of genetic risk, incidence rates per 1,000 person-years were gradually higher with worse modifiable risk factor profiles. (Figure 2). For participants with high genetic risk, the incidence rates of AF per 1,000 person years were 5.0 (95% CI 3.4-7.3) for ideal, 6.6 (95% CI 5.4-8.1) for intermediate, and 10.4 (95% CI 9.2-11.8) for poor risk factor profiles. On an additive scale, there was a statistically significant interaction between genetic risk and modifiable risk factors (relative excess risk due to interaction=0.86 (95% 0.68-1.03, p&amp;lt;0.001). Conclusions A genetic risk score and modifiable risk factors were each associated with AF risk. The rates of AF increased gradually with cumulation of modifiable risk factors within each category of the genetic risk score, and we found an additive interaction between genetic risk and the modifiable risk factor profile. This underlines the importance of risk factor modification in primary AF prevention.Figure 1Figure 2

Prediction of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms Based on Deep Neural Networks: Analysis of Time Intervals and Longitudinal Study

ObjectiveArtificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures. MethodsWe designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics. ResultsAfter balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09). ConclusionThis study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability.

Predictive value of bleeding risk scores in elderly patients with atrial fibrillation and oral anticoagulation.

The predictive value of bleeding risk scores for atrial fibrillation in older patients is not as well known. The goal of this study was to evaluate the predictive value of HASBLED, ORBIT and ATRIA for major bleeding (MB) and intracranial hemorrhage (ICH) in patients ≥ 75 years with atrial fibrillation and oral anticoagulation (OAC). A retrospective unicenter study including patients ≥ 75 years with atrial fibrillation (AF) and OAC. A total of 7613 patients ≥ 75 years with AF and OAC included between 2014 and 2018 (registry: NCT04364516). We analyzed the discriminative value of HASBLED, ATRIA and ORBIT scores for bleeding endpoints (major bleeding as primary endpoint and intracerebral hemorrhage as secondary). Cox regression was used to predict major bleeding with each scale and also for searching other variables potentially predictor of major bleeding. Model discrimination was assessed using Harrell's C-statistic. Calibration was assessed with goodness-of-fit test proposed by Gronnesby and Borgan. During a mean follow up of 4.0 years (IQR: 2.4-5.7 years), 729 patients developed MB (2.61 per 100 patients/year) and 243 patients developed ICH (0.85 per 100 patients/year). Three scores showed a low discrimination for major bleeding, being ORBIT the best (HASBLED C statistic = 0.557; ATRIA C statistic = 0.568; ORBIT C statistic = 0.595) and also a low discrimination for ICH (HASBLED C statistic = 0.509; ATRIA C statistic = 0.522; ORBIT C statistic = 0.526). Among the variables that are part of the scores and other baseline characteristics, after multivariable adjustment only sex (male), dementia, prior admission for bleeding, anemia and liver disease were found as a predictors of MB. In older patients under oral anticoagulation with atrial fibrillation, the risk scores HASBLED, ATRIA and ORBIT showed a weak discrimination for major bleeding and intracranial hemorrhage. Therefore, other better alternatives should be evaluated for this purpose.