Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium

Left atrial strain measured by three-dimensional echocardiography predicts atrial fibrillation in the general population

BACKGROUND

Left atrial (LA) strain by three-dimensional echocardiography (3DE), has been proposed as a more accurate measure of LA function, providing incremental prognostic benefits over traditional two-dimensional approaches.

OBJECTIVES

Our aim was to evaluate the prognostic value of LA strain by 3DE in predicting incident atrial fibrillation (AF) in the general population.

METHODS

The study included 4466 participants from a prospective longitudinal cohort study in the general population, among these 3DE LA strain was analysed in 1935 participants. The endpoint was incident AF. Adjustments were made for the CHARGE-AF clinical risk score.

RESULTS

Mean age was 54 ± 17 years, 43 % were male. During a median follow-up time of 4.8 years (interquartile range 4.3-5.5 years) 59 participants (3.0 %) developed AF. In univariable analysis, all three parameters were associated with incident AF (p value for all <0.01). After multivariable adjustments, only LA reservoir strain (LASr) and LA contractile strain (LASct) were associated with incident AF (LASr: HR 1.12 (1.07-1.17), p < 0.001, per 1 % decrease; LASct: HR 1.16 (1.09-1.24), p < 0.001, per 1 % decrease), whereas LA conduit strain (LAScd) was not (HR 1.04 (0.98-1.10), p = 0.17, per 1 % decrease). Both LASr (continuous net reclassification index 0.37 ± 0.14; p = 0.003) and LASct (continuous net reclassification index 0.41 ± 0.14; p = 0.002) provided incremental prognostic information beyond the CHARGE-AF risk score.

CONCLUSION

LASr and LASct measured by 3DE are independently associated with incident AF and provided incremental prognostic information beyond existing risk scores.

Usefulness of the C2HEST score to predict new onset atrial fibrillation. A systematic review and meta-analysis on >11 million subjects

BACKGROUND

The incidence of new-onset atrial fibrillation (NOAF) is increasing in the last decades. NOAF is associated with worse long-term prognosis. The C2HEST score has been recently proposed to stratify the risk of NOAF. Pooled data on the performance of the C2HEST score are lacking.

METHODS

Systematic review and meta-analysis of observational studies reporting data on NOAF according to the C2HEST score. We searched PubMed, Web of Science and Google scholar databases without time restrictions until June 2023 according to PRISMA guidelines. Meta-analysis of the area under the curve (AUC) with 95% confidence interval (95% CI) and a sensitivity analysis according to setting of care and countries were performed.

RESULTS

Of 360 studies, 17 were included in the analysis accounting for 11,067,496 subjects/patients with 307,869 NOAF cases. Mean age ranged from 41.3 to 71.2 years. The prevalence of women ranged from 10.6 to 54.75%. The pooled analysis gave an AUC of .70 (95% CI .66-.74). A subgroup analysis on studies from general population/primary care yielded an AUC of 0.69 (95% CI 0.64-0.75). In the subgroup of patients with cardiovascular disease, the AUC was .71 (.69-.79). The C2HEST score performed similarly in Asian (AUC .72, 95% CI .68-.77), and in Western patients (AUC .68, 95% CI .62-.75). The best performance was observed in studies with a mean age <50 years (n = 3,144,704 with 25,538 NOAF, AUC .78, 95% CI .76-.79).

CONCLUSION

The C2HEST score may be used to predict NOAF in primary and secondary prevention patients, and in patients across different countries. Early detection of NOAF may aid prompt initiation of management and follow-up, potentially leading to a reduction of AF-related complications.

External validation of the FAscore scale to evaluate the risk of atrial fibrillation in patients with arterial hypertension

BACKGROUND AND AIM

To use a risk scale or predictive model outside the population of origin, it is necessary to evaluate the predictive indicators through external validation. The aim was to validate the FAscore, originally constructed in hypertensive patients in primary care in the Valencian Region, in an external cohort with hypertension in primary care in the Basque Country.

METHODS

A retrospective cohort study was designed to perform an external validation of the FAscore app in patients affiliated with 26 health centers in the municipality of Bilbao. The area under the ROC curve and predictive indicators were calculated with their 95% confidence intervals.

RESULTS

Thirty-six thousand eight hundred nine patients were included: 53.6% (n=19,719) were women, the mean age was 75.1 years, 41.8% (n=15,381). Over the four-year follow-up period, 1420 patients were diagnosed with AF (cumulative incidence 3.9%). The median risk estimated by FAscore was 4.5%, and the 5th, 25th, 75th, and 95th percentiles were 1.0%, 2.5%, 6.1%, and 14.8%, respectively. The ROC curve for the risk estimated by FAscore and the cases of atrial fibrillation observed was AUC 0.715 (95% CI 0.703-0.727). The 5% risk cutoff provides a sensitivity of 70.8%, specificity of 61.0%, positive predictive value of 6.8%, negative predictive value of 98.1%, and positive and negative likelihood ratios of 1.82 and 0.48, respectively.

CONCLUSION

This study reports on the external validation of the atrial fibrillation risk scale in hypertensive patients, which shows an acceptable predictive capacity. The best-performing risk cutoff, providing good predictive indicators, can be set at 5%.

Sex- and age-specific differences in the use of antiarrhythmic therapies among atrial fibrillation patients: a nationwide cohort study

AIMS

Atrial fibrillation (AF) patients frequently require active rhythm control therapy to maintain sinus rhythm and reduce symptom burden. Our study assessed whether antiarrhythmic therapies (AATs) are used disproportionately between men and women after new-onset AF.

METHODS AND RESULTS

The nationwide Finnish anticoagulation in AF registry-based linkage study covers all patients with new-onset AF in Finland during 2007-2018. Study outcomes included initiation of AATs in the form of antiarrhythmic drugs (AADs), cardioversion, or catheter ablation. The study population constituted of 229 565 patients (50% females). Women were older than men (76.6 ± 11.8 vs. 68.9 ± 13.4 years) and had higher prevalence of hypertension or hyperthyroidism, but lower prevalence of vascular disease, diabetes, renal disease, and cardiomyopathies than men. Overall, 17.6% of women and 25.1% of men were treated with any AAT. Women were treated with AADs more often than men in all age groups [adjusted subdistribution hazard ratio (aSHR) 1.223, 95% confidence interval (CI) 1.187-1.261]. Cardioversions were also performed less often on women than on men aged <65 years (aSHR 0.722, 95% CI 0.695-0.749), more often in patients ≥ 75 years (aSHR 1.166, 95% CI 1.108-1.227), while no difference between the sexes existed in patients aged 65-74 years. Ablations were performed less often in women aged <65 years (aSHR 0.908, 95% CI 0.826-0.998) and ≥75 years (aSHR 0.521, 95% CI 0.354-0.766), whereas there was no difference in patients aged 65-74 years.

CONCLUSION

Women used more AAD than men in all age groups but underwent fewer cardioversion and ablation procedures when aged <65 years.

Plasma Proteomic Insights for Identification of Novel Predictors and Potential Drug Targets in Atrial Fibrillation: A Prospective Cohort Study and Mendelian Randomization Analysis

BACKGROUND

Currently, there are no reliable methods for predicting and preventing atrial fibrillation (AF) in its early stages. This study aimed to identify plasma proteins associated with AF to discover biomarkers and potential drug targets.

METHODS

The UK Biobank Pharma Proteomics Project examined 2923 circulating proteins using the Olink platform, forming the basis of this prospective cohort study. The UK Biobank Pharma Proteomics Project included a randomly selected discovery cohort and the consortium-selected replication cohort. The study's end point was incident AF, identified using International Classification of Diseases, Tenth Revision codes. The association between plasma proteins and incident AF was evaluated using Cox proportional hazard models in both cohorts. Proteins present in both cohorts underwent Mendelian randomization analysis to delineate causal connections, utilizing cis-protein quantitative trait loci as genetic tools. The predictive efficacy of the identified proteins for AF was assessed using the area under the receiver operating characteristic curve, and their druggability was explored.

RESULTS

Data from 38 784 participants were included in this study. Incident AF cases were identified in the discovery cohort (1894; 5.5%) within a median follow-up of 14.5 years and in the replication cohort (451; 10.6%) within a median follow-up of 14.4 years. Twenty-one proteins linked to AF were identified in both cohorts. Specifically, COL4A1 (collagen IV α-1; odds ratio, 1.11 [95% CI, 1.04-1.19]; false discovery rate, 0.016) and RET (proto-oncogene tyrosine-protein kinase receptor Ret; odds ratio, 0.96 [95% CI, 0.94-0.98]; false discovery rate, 0.013) demonstrated a causal link with AF, and RET is druggable. COL4A1 improved the short- and long-term predictive performance of established AF models, as evidenced by significant enhancements in the area under the receiver operating characteristic, integrated discrimination improvement, and net reclassification index, all with P values below 0.05.

CONCLUSIONS

COL4A1 and RET are associated with the development of AF. RET is identified as a potential drug target for AF prevention, while COL4A1 serves as a biomarker for AF prediction. Future studies are needed to evaluate the effectiveness of targeting these proteins to reduce AF risk.

Association of Angina, Myocardial Infarction and Atrial Fibrillation-A Bidirectional Mendelian Randomization Study

Aims/Background Coronary heart disease (CHD) and atrial fibrillation (AF) exhibit a close relationship, yet the existing body of research predominantly relies on observational study methodologies, posing challenges in establishing causal relationships. The objective of our study is to investigate the causal linkages between coronary atherosclerosis (CAAs), angina pectoris, myocardial infarction (MI), and AF. Methods This study utilizes a two-sample Mendelian randomization (TSMR) methodology, leveraging genetic variation as a means of evaluating causality. Mendelian randomization is grounded in three primary assumptions: (1) the genetic variant is linked to the exposure, (2) the genetic variant is independent of confounding factors, and (3) the genetic variant influences the outcome solely through the exposure. Results The results of our study suggest a genetic predisposition in which CAAs, angina, and MI may enhance susceptibility to AF, while AF may reciprocally elevate the risk of CAAs. Conclusion In light of these findings, it is recommended that patients with CHD undergo regular cardiac rhythm monitoring, and that patients with AF receive anticoagulant and antiplatelet therapy whenever feasible. This study posits a practical implication for clinical practice.

Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank

OBJECTIVES

Despite rising rates of multimorbidity, existing risk assessment tools are mostly limited to a single outcome of interest. This study tests the feasibility of producing multiple disease risk estimates with at least 70% discrimination (area under the receiver operating curve, AUROC) within the time and information constraints of the existing primary care health check framework.

DESIGN

Observational prospective cohort study SETTING: UK Biobank.

PARTICIPANTS

228 240 adults from the UK population.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

Myocardial infarction, atrial fibrillation, heart failure, stroke, all-cause dementia, chronic kidney disease, fatty liver disease, alcoholic liver disease, liver cirrhosis and liver failure.

RESULTS

Using a set of predictors easily gathered at the standard primary care health check (such as the National Health Service Health Check), we demonstrate that it is feasible to simultaneously produce risk estimates for multiple disease outcomes with AUROC of 70% or greater. These predictors can be entered once into a single form and produce risk scores for stroke (AUROC 0.727, 95% CI 0.713 to 0.740), all-cause dementia (0.823, 95% CI 0.810 to 0.836), myocardial infarction (0.785, 95% CI 0.775 to 0.795), atrial fibrillation (0.777, 95% CI 0.768 to 0.785), heart failure (0.828, 95% CI 0.818 to 0.838), chronic kidney disease (0.774, 95% CI 0.765 to 0.783), fatty liver disease (0.766, 95% CI 0.753 to 0.779), alcoholic liver disease (0.864, 95% CI 0.835 to 0.894), liver cirrhosis (0.763, 95% CI 0.734 to 0.793) and liver failure (0.746, 95% CI 0.695 to 0.796).

CONCLUSIONS

Easily collected diagnostics can be used to assess 10-year risk across multiple disease outcomes, without the need for specialist computing or invasive biomarkers. Such an approach could increase the utility of existing data and place multiorgan risk information at the fingertips of primary care providers, thus creating opportunities for longer-term multimorbidity prevention. Additional work is needed to validate whether these findings would hold in a larger, more representative cohort outside the UK Biobank.

Biomarkers of atrial fibrillation-related pathways and left atrial structure and function in an overweight and obese population

Background

Exploring longitudinal associations of blood biomarkers with left atrial (LA) structure and function can enhance our understanding of atrial fibrillation (AF) etiopathogenesis.

Methods

We studied 532 participants of the PREDIMED-Plus trial, a multicenter randomized trial in overweight and obese adults with metabolic syndrome. At baseline, 3 and 5 years after randomization, participants underwent transthoracic echocardiography and provided blood for serum biomarker measurements [propeptide of procollagen type I (PICP), high-sensitivity (hs) troponin T (hsTnT), hs C-reactive protein (hsCRP), 3-nitrotyrosine (3-NT), and N-terminal propeptide of B-type natriuretic peptide (NT-proBNP)]. Outcomes of interest included LA peak systolic longitudinal strain (LA PSLS), LA volume index (LAVi), LA function index (LAFi), and LA stiffness index (LASi). We performed cross-sectional and longitudinal analyses to evaluate relationships between log-transformed biomarkers and echocardiographic measurements using multiple linear regression and mixed models.

Results

The participants in this analysis had a mean age of 65.0 (SD 4.8) years, and 40% were females. At baseline, increased NT-proBNP and hsTnT were associated with larger LAVi and worse LA function as measured by the LAFi, LASi, and LA PSLS. Longitudinally, higher NT-proBNP, but not higher hsTnT, was associated with increased LAVi and worsening LA function. Over 5 years, 1 unit increase in log(NT-proBNP) was associated with steeper decline in LA PSLS (-0.19%, 95% CI -0.35%, -0.02%) and greater increase in LAVi (0.28 mL/m2, 95% CI 0.10, 0.45) each year. PICP, hsCRP, and 3-NT did not show consistently significant associations with LA outcomes at baseline and through 5 years.

Conclusion

In an overweight and obese population, higher NT-proBNP was associated with LA volume enlargement and worsening LA function over 5 years. The implications of these findings for the prevention and prediction of AF warrant further investigation.

Management of atrial fibrillation in older adults

Most people with atrial fibrillation are older adults, in whom atrial fibrillation co-occurs with other chronic conditions, polypharmacy, and geriatric syndromes such as frailty. Yet most randomized controlled trials and expert guidelines use an age agnostic approach. Given the heterogeneity of aging, these data may not be universally applicable across the spectrum of older adults. This review synthesizes the available evidence and applies rigorous principles of aging science. After contextualizing the burden of comorbidities and geriatric syndromes in people with atrial fibrillation, it applies an aging focused approach to the pillars of atrial fibrillation management, describing screening for atrial fibrillation, lifestyle interventions, symptoms and complications, rate and rhythm control, coexisting heart failure, anticoagulation therapy, and left atrial appendage occlusion devices. Throughout, a framework is suggested that prioritizes patients' goals and applies existing evidence to all older adults, whether atrial fibrillation is their sole condition, one among many, or a bystander at the end of life.

Impact of changes in left heart geometry on predicting new-onset atrial fibrillation in patients with hypertension

BACKGROUND

Hypertension-induced left ventricular hypertrophy (LVH) increases end-diastolic LV pressure and contributes to left atrial enlargement (LAE), which are associated with development of atrial fibrillation. However, the impact of LVH and LAE and their regression following antihypertensive therapy on atrial fibrillation incidence remains unclear.

METHODS

This retrospective analysis included consecutive patients with sinus rhythm who underwent echocardiography at hypertension diagnosis and after 6-18 months between 2006 and 2021 at tertiary care centres in Korea. LVH was defined as LV mass index greater than 115 g/m2 (men) and greater than 95 g/m2 (women), and LAE was defined as LA volume index greater than 42 ml/m2. The occurrence of new-onset atrial fibrillation (NOAF) was assessed in relation to changes in LVH and LAE status.

RESULTS

Among the 1464 patients included, 163 (11.1%) developed NOAF during a median 63.8 [interquartile range (IQR) 35.9-128.5] months of surveillance period. New-onset LVH [adjusted hazard ratio (aHR) 1.88, 95% confidence interval (CI) 1.20-2.94, P = 0.006] and LAE (aHR 1.89, 95% CI 1.05-3.40, P = 0.034) were significant predictors of NOAF. Conversely, regression of LVH (aHR 0.51, 95% CI 0.28-0.91, P = 0.022) or LAE (aHR 0.30, 95% CI 0.15-0.63, P = 0.001) was associated with a reduced risk for developing NOAF. Patients with both LVH and LAE at follow-up echocardiography had a higher risk for NOAF (aHR 4.30, 95% CI 2.81-6.56, P < 0.001) than those with either LVH or LAE or those with neither.

CONCLUSION

The changes in left heart geometry can serve as a predictive marker for NOAF in patients with hypertension.

A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.

The association of exhaled carbon monoxide with atrial fibrillation and left atrial size in the Framingham Heart Study

Background

Exhaled carbon monoxide (eCO) is associated with subclinical and overt cardiovascular disease and stroke. The association between eCO with left atrial size, prevalent, or incident atrial fibrillation (AF) are uncertain.

Methods

eCO was measured using an Ecolyzer instrument among Framingham Heart Study Offspring and Omni participants who attended an examination from 1994 to 1998. We analyzed multivariable-adjusted (current smoking, and other covariates including age, race, sex, height, weight, systolic blood pressure, diastolic blood pressure, diabetes, hypertension treatment, prevalent myocardial infarction [MI], and prevalent heart failure [HF]). Cox and logistic regression models assessed the relations between eCO and incident AF (primary model), and prevalent AF and left atrial (LA) size (pre-specified secondary analyses). We also conducted secondary analyses adjusting for biomarkers, and interim MI and interim HF.

Results

Our study sample included 3814 participants (mean age 58 ± 10 years; 54.4 % women, 88.4 % White). During an average of 18.8 ± 6.5 years follow-up, 683 participants were diagnosed with AF. eCO was associated with incident AF after adjusting for established AF risk factors (HR, 1.31 [95 % CI, 1.09-1.58]). In secondary analyses the association remained significant after additionally adjusting for C-reactive protein and B-type natriuretic peptide, and interim MI and CHF, and in analyses excluding individuals who currently smoked. eCO was not significantly associated with LA size and prevalent AF.

Conclusion

In our community-based sample of individuals without AF, higher mean eCO concentrations were associated with incident AF. Further investigation is needed to explore the biological mechanisms linking eCO with AF.

Predictive value of NT pro BNP for new-onset atrial fibrillation in heart failure and preserved ejection fraction

AIMS

The prognostic significance of N-terminal pro B-type natriuretic peptide (NT-proBNP) in heart failure with preserved ejection fraction (HFpEF) has been well established. HFpEF and atrial fibrillation (AF) commonly coexist, and each contributes to poor outcomes independently. Nevertheless, the ability of NT-proBNP to predict AF in HFpEF patients remains uncertain.

METHODS AND RESULTS

A total of 367 HFpEF patients without baseline AF from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial were included. The Cox proportional hazard model was used to assess the association of NT-proBNP with the risk of AF. The C-statistic, categorical net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the ability of NT-proBNP in new-onset AF prediction. During a median follow-up of 2.91 years, 17 (4.63%) new-onset AF cases occurred. Every 1000 pg/mL increase in NT-proBNP was associated with a 16% increase in the risk of AF occurrence after adjustments (hazard ratio, 1.16 [95% CI, 1.02-1.32]). NT-proBNP showed a moderate performance for new-onset AF at 3 years (C-statistic, 0.67). Adding NT-proBNP to CHADS2/R2CHADS2/CHA2DS2-VASc/C2HSET scores improved their predictive performance for AF risk (CHADS2: C-statistic, 0.63, CHADS2+NT: C-statistic, 0.69, NRI, 47.46%, IDI, 1.18%; R2CHADS2: C-statistic, 0.65, R2CHADS2+NT: C-statistic, 0.70, NRI, 48.03%, IDI, 0.51%; CHA2DS2-VASc: C-statistic, 0.67, CHA2DS2-VASc+NT: C-statistic, 0.72, NRI, 49.41%, IDI, 0.86%; C2HSET: C-statistic, 0.77, C2HSET+NT: C-statistic, 0.80, NRI, 50.32%, IDI, 1.58%).

CONCLUSIONS

Among patients with HFpEF, the NT-proBNP level was positively associated with the incidence of new-onset AF and may be a promising predictor.

Identifying a Heterogeneous Effect of Atrial Fibrillation Screening in Older Adults: A Secondary Analysis of the VITAL-AF Trial

Background

One-time atrial fibrillation (AF) screening trials have produced mixed results; however, it is unclear if there is a subset for whom screening is effective. Identifying such a subgroup would support targeted screening.

Methods

We conducted a secondary analysis of VITAL-AF, a randomized trial of one-time, single-lead ECG screening during primary care visits. We tested two approaches to identify a subgroup where screening is effective. First, we developed an effect-based model using a T-learner. Specifically, we separately predicted the likelihood of AF diagnosis under screening and usual care conditions; the difference in probabilities was the predicted screening effect. Second, we used a validated AF risk model to test for a heterogeneous screening effect. We used interaction testing to determine if observed AF diagnosis rates in the screening and usual care groups differed when stratified by decile of the predicted screening effect and predicted AF risk.

Results

Baseline characteristics were similar between the screening (n=15187) and usual care (n=15078) groups (mean age 74 years, 59% female). In the effect-based analysis, in the highest decile of predicted screening effectiveness (n=3026), AF diagnosis rates were higher in the screening group (6.50 vs. 3.06 per 100 person-years, rate difference 3.45, 95%CI 1.62 to 5.28). In this group, the mean age was 84 years and 68% were female. The risk-based analysis did not identify a subgroup where screening was more effective. Predicted screening effectiveness and predicted baseline AF risk were poorly correlated (Spearman coefficient 0.13).

Conclusions

In a secondary analysis of the VITAL-AF trial, we identified a small subgroup where one-time screening was associated with increased AF diagnoses using an effect-based approach. In this study, predicted AF risk was a poor proxy for predicted screening effectiveness. These data caution against the assumption that high AF risk is necessarily correlated with high screening effectiveness.

Emergence of Atrial Fibrillation and Flutter in COVID-19 Patients: A Retrospective Cohort Study

COVID-19 is associated with various cardiovascular complications, including arrhythmias. This study investigated the incidence of new-onset atrial fibrillation (AFB) and atrial flutter (AFL) in COVID-19 patients and identified potential risk factors. We conducted a retrospective cohort study at a tertiary-care safety-net community hospital including 647 patients diagnosed with COVID-19 from March 2020 to March 2021. Patients with a prior history of AFB or AFL were excluded. Data on demographics, clinical characteristics, and outcomes were collected and analyzed using chi-square tests, t-tests, and binary logistic regression. We found that 69 patients (10.66%) developed AFB or AFL, with 41 patients (6.34%) experiencing new-onset arrhythmias. The incidence rates for new-onset AFB and AFL were 5.4% and 0.9%, respectively. Older age (≥65 years) was significantly associated with new-onset AFB/AFL (OR: 5.43; 95% CI: 2.31-12.77; p < 0.001), as was the development of sepsis (OR: 2.73; 95% CI: 1.31-5.70; p = 0.008). No significant association was found with patient sex. Our findings indicate that new-onset atrial arrhythmias are a significant complication in COVID-19 patients, particularly among the elderly and those with sepsis. This highlights the need for targeted monitoring and management strategies to mitigate the burden of atrial arrhythmias in high-risk populations during COVID-19 infection.

Construction and validation of a risk prediction model for 3- and 5-year new-onset atrial fibrillation in HFpEF patients

Background

Patients with heart failure (HF) with preserved ejection fraction (HFpEF) are more prone to atrial fibrillation (AF) compared to those with heart failure with reduced ejection fraction (HFrEF). Nevertheless, a risk prediction model for new-onset atrial fibrillation (NOAF) in HFpEF patients remains a notable gap, especially with respect to imaging indicators.

Methods

We retrospectively analyzed 402 HFpEF subjects reviewed at the Affiliated Hospital of Qingdao University from 2017 to 2023. Cox regression analysis was performed to screen predictors of NOAF. A nomogram was constructed based on these factors and internally validated through the bootstrap resampling method. A performance comparison between the nomogram and the mC2HEST score was performed.

Results

Out of the 402 participants, 62 (15%) developed atrial fibrillation. The risk factors for NOAF were finally screened out to include age, chronic obstructive pulmonary disease (COPD), hyperthyroidism, renal dysfunction, left atrial anterior-posterior diameter (LAD), and pulmonary artery systolic pressure (PASP), all of which were identified to create the nomogram. We calculated the bootstrap-corrected C-index (0.819, 95% CI: 0.762-0.870) and drew receiver operator characteristic (ROC) curves [3-year areas under curves (AUC) = 0.827, 5-year AUC = 0.825], calibration curves, and clinical decision curves to evaluate the discrimination, calibration, and clinical adaptability of the six-factor nomogram. Based on two cutoff values calculated by X-tile software, the moderate- and high-risk groups had more NOAF cases than the low-risk group (P < 0.0001). Our nomogram showed better 3- and 5-year NOAF predictive performance than the mC2HEST score estimated by the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI) (P < 0.05).

Conclusions

The nomogram combining clinical features with echocardiographic indices helps predict NOAF among HFpEF patients.

Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation

Background

AF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis.

Objective

To test whether integrating these distinct risk signals improves AF risk estimation.

Methods

In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP).

Results

Among 49,293 individuals (mean age 65±8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The inclusion of all components ("Predict-AF3") was the best performing model (AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI), followed by the two component model of CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]). Using Predict-AF3, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]), and Predict-AF3 achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]).

Conclusions

Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.

Evaluation of risk factors for long-term atrial fibrillation development in patients undergoing typical atrial flutter ablation: a multicenter pilot study

BACKGROUND

Atrial flutter (AFL) and atrial fibrillation (AF) are the most commonly detected supraventricular arrhythmias and share similar pathophysiological mechanisms. After the successful ablation of AFL, AF frequently occurs in the long-term follow-up. As emphasized in some studies, certain mechanisms seem to predispose to the development of AF in AFL patients, and approximately 20% of these patients have accompanying AFL.

PURPOSE

We aimed to analyze independent risk factors that predict the development of AF in patients who underwent typical AFL ablation.

METHODS

This was a multicenter, cross-sectional, and retrospective study. A total of 442 patients who underwent typical AFL ablation at three different centers between January 1, 2018 and January 1, 2022 were included retrospectively. After the ablation procedure the patients were divided into those who developed AF and those who did not. The patients were followed up for an average of 12 (4-20) months. In the post-procedural period, atrial arrhythmias were investigated with 24‑h Holter and ECG at 1 month, 6 months, and 12 months and then at 6‑month intervals thereafter.

RESULTS

Overall, AF developed in 206 (46.6%) patients in the long-term follow-up. Age, hypertension (HT), obstructive sleep apnea syndrome (OSAS), previous cerebrovascular accident (CVA), left atrium anteroposterior diameter, severe mitral regurgitation, hemoglobin, blood glucose, and HbA1c values were found to be significant in univariable analysis. According to multivariable analysis, HT (p = 0.014; HR: 1.483 [1.084-2.030]), OSAS (p = 0.008; HR: 1.520 [1.117-2.068]) and previous CVA (p = 0.038; HR: 1.749 [1.031-2.968]) were independently associated with the development of AF in AFL patients who underwent ablation procedure.

CONCLUSION

In the present study, we found that HT, OSAS, and previous CVA were independently correlated with the development of AF in the long-term follow-up of patients who underwent typical AFL ablation. We consider that AFL patients with such risk factors should be followed up closely following cavotricuspid isthmus ablation for the development of AF.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women's Health Initiative

BACKGROUND

Frequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring.

OBJECTIVE

The objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring.

METHODS

Participants from the Women's Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models.

RESULTS

There were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring.

CONCLUSION

In postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.

Prediction of new-onset atrial fibrillation with the C2HEST score in patients admitted with community-acquired pneumonia

PURPOSE

Patients hospitalized for community-acquired pneumonia (CAP) may have a higher risk of new-onset atrial fibrillation (NOAF). The C2HEST score was developed to evaluate the NOAF risk in the general population. Data on the value of the C2HEST score in acute patients admitted with CAP are lacking. We want to establish the predictive value of C2HEST score for NOAF in patients with CAP.

METHODS

Patients with CAP enrolled in the SIXTUS cohort were enrolled. C2HEST score was calculated at baseline. In-hospital NOAF was recorded. Receiver-operating Characteristic (ROC) curve and multivariable Cox proportional hazard regression analysis were performed.

RESULTS

We enrolled 473 patients (36% women, mean age 70.6 ± 16.5 years), and 54 NOAF occurred. Patients with NOAF were elderly, more frequently affected by hypertension, heart failure, previous stroke/transient ischemic attack, peripheral artery disease and hyperthyroidism. NOAF patients had also higher CURB-65, PSI class and CHA2DS2-VASc score. The C-index of C2HEST score for NOAF was 0.747 (95% confidence interval [95%CI] 0.705-0.786), higher compared to CURB-65 (0.611, 95%CI 0.566-0.655, p = 0.0016), PSI (0.665, 95%CI 0.621-0.708, p = 0.0199) and CHA2DS2-VASc score (0.696, 95%CI 0.652-0.737, p = 0.0762). The best combination of sensitivity (67%) and specificity (70%) was observed with a C2HEST score ≥ 4. This result was confirmed by the multivariable Cox analysis (Hazard Ratio [HR] for C2HEST score ≥ 4 was 10.7, 95%CI 2.0-57.9; p = 0.006), independently from the severity of pneumonia.

CONCLUSION

The C2HEST score was a useful predictive tool to identify patients at higher risk for NOAF during hospitalization for CAP.

CLINICAL TRIAL REGISTRATION

www.

CLINICALTRIALS

gov (NCT01773863).

Identification of atrial fibrillation-related genes through transcriptome data analysis and Mendelian randomization

Background

Atrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.

Methods

We obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A "Veen" diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.

Results

The combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956-0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934-0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875-0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943-0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004-1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017-1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010-0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003-1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.

Conclusion

By integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

Role of the Electrocardiogram for Identifying the Development of Atrial Fibrillation

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.

Cardiac Mechanics and the Risk of Atrial Fibrillation in a Community-Based Cohort of Older Adults

BACKGROUND

Assessment of cardiac structure and function improves risk prediction of new-onset atrial fibrillation (AF) in different populations. We aimed to comprehensively compare standard and newer measures of cardiac structure and function in improving prediction of AF in a cohort of older adults without history of AF and stroke.

METHODS

We included 5050 participants without prevalent AF and stroke (mean age 75 ± 5 years, 59% women and 22% Black) from the Atherosclerosis Risk in Communities (ARIC) study who underwent complete 2-dimensional echocardiography, including speckle-tracking analysis of the left ventricle (LV) and left atrium (LA). We assessed the association of cardiac measures with incident AF (including atrial flutter) and quantified the extent to which these measures improved model discrimination and risk classification of AF compared with the CHARGE-AF score.

RESULTS

Over a median follow-up time of 7 years, 676 participants developed AF (incidence rate, 2.13 per 100 person-years). LV mass index and wall thickness, E/e' and measures of LA structure and function, but not LV systolic function, were associated with incident AF, after accounting for confounders. Above all, LA reservoir strain, contraction strain, and LA minimal volume index (C-statistics [95%Confidence interval]: 0.73 [0.70,0.75], 0.72 [0.70,0.75] and 0.72 [0.69,0.75], respectively) significantly improved the risk discrimination of the CHARGE-AF score (baseline C-statistic: 0.68 [0.65,0.70]) and achieved the highest category-based net reclassification improvement (29%, 24% and 20%, respectively).

CONCLUSIONS

In a large cohort of older adults without prevalent AF and stroke, measures of LA function improved the prediction of AF more than other conventional cardiac measures.

Sodium Intake and Incident Atrial Fibrillation in Individuals With Vascular Disease

Importance

Numerous prospective cohort studies have reported a J-shaped association of urinary sodium excretion with cardiovascular events and mortality.

Objective

To study the association between sodium intake and incident atrial fibrillation (AF).

Design, Setting, and Participants

This cohort study included participants in the Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET) and Telmisartan Randomised Assessment Study in ACE Intolerant Subjects With Cardiovascular Disease (TRANSCEND) multicenter, randomized clinical trials comparing the effect of ramipril 10 mg daily with telmisartan 80 mg daily, or their combination (ONTARGET) or 80 mg telmisartan daily with placebo (TRANSCEND) for the outcome of death from cardiovascular causes, myocardial infarction, stroke, or hospitalization for heart failure. ONTARGET and TRANSCEND included 31 546 participants with vascular disease or high-risk diabetes, and this study excluded participants without a urine sample for sodium measurement, missing data for key covariates, a history of AF, or AF detected in the first year after enrollment. Analyses were performed in July 2023 to May 2024.

Exposure

Estimated sodium intake from a morning fasting urine sample (Kawasaki formula).

Main Outcomes and Measures

The main outcome was incident AF. The association between estimated sodium intake and incident AF was modeled using multivariable adjusted Cox regression and cubic splines.

Results

A total of 27 391 participants (mean [SD] age, 66.3 [7.2] years; 19 310 [70.5%] male) were included. Mean (SD) estimated sodium intake was 4.8 (1.6) g/d. During a mean (SD) follow-up of 4.6 (1.0) years, 1562 participants (5.7%) had incident AF. After multivariable adjustment, a J-shaped association between sodium intake and AF risk was observed (P for nonlinearity = .03). Sodium intake of 8 g/d or greater (3% of participants) was associated with incident AF (hazard ratio, 1.32; 95% CI, 1.01-1.74) compared with sodium intake of 4 to 5.99 g/d. Cubic splines showed that sodium intake greater than 6 g/d (19% of participants) was associated with a 10% increased AF risk per additional 1-g/d sodium intake (hazard ratio, 1.10; 95% CI, 1.03-1.18), but with no further lowering of AF risk at lower levels of sodium intake.

Conclusions and Relevance

In this cohort study of sodium intake and AF risk, there was a J-shaped association between sodium intakes and AF risk in patients with cardiovascular disease or diabetes. Lowering sodium intake for AF prevention is best targeted at individuals who consume high sodium diets.

The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.

Screening and detection of atrial fibrillation in primary care: current practice and future perspectives

Atrial fibrillation (AF) is a common arrhythmia associated with an increased risk of stroke, which can be effectively reduced by prophylaxis initiation and integrated care to reduce cardiovascular risk and AF-related complications. Screening for AF has the potential to improve long-term clinical outcomes through timely AF detection in asymptomatic patients. With the central role of primary care in most European healthcare systems in terms of disease detection, treatment, as well as record keeping, primary care is ideally situated as a setting for AF screening efforts. In this review, we provide an overview of evidence relating to AF screening in primary care. We discuss current practices of AF detection and screening, evidence from AF screening trials conducted in primary care settings, stakeholder views on barriers and facilitators for AF screening in primary care, and important aspects that will likely shape routine primary care AF detection as well as AF screening efforts. Finally, we present a potential outline for a primary care-centred AF screening trial coupled to integrated AF care that could further improve the benefit of AF screening.

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset AF that incorporated the use electrocardiogram to diagnose AF, data from participants with a wide age range, and considered hypertension and measures of atrial stiffness. In Japan, Industrial Safety and Health Law requires employers to provide annual health check-ups to their employees. This study included 13 410 individuals who underwent health check-ups on at least four successive years between 2005 and 2015 (new-onset AF, n = 110; non-AF, n = 13 300). Data were entered into a risk prediction model using machine learning methods (eXtreme Gradient Boosting and Shapley Additive Explanation values). Data were randomly split into a training set (80%) used for model construction and development, and a test set (20%) used to test performance of the derived model. The area under the receiver operator characteristic curve for the model in the test set was 0.789. The best predictor of new-onset AF was age, followed by the cardio-ankle vascular index, estimated glomerular filtration rate, sex, body mass index, uric acid, γ-glutamyl transpeptidase level, triglycerides, systolic blood pressure at cardio-ankle vascular index measurement, and alanine aminotransferase level. This new model including arterial stiffness measure, developed with data from a general population using machine learning methods, could be used to identify at-risk individuals and potentially facilitation the prevention of future AF development.

Atrial fibrillation: age at diagnosis, incident cardiovascular events, and mortality

BACKGROUND AND AIMS

Patients with atrial fibrillation (AF) are at increased risks of cardiovascular diseases and mortality, but risks according to age at diagnosis have not been reported. This study investigated age-specific risks of outcomes among patients with AF and the background population.

METHODS

This nationwide population-based cohort study included patients with AF and controls without outcomes by the application of exposure density matching on the basis of sex, year of birth, and index date. The absolute risks and hazard rates were stratified by age groups and assessed using competing risk survival analyses and Cox regression models, respectively. The expected differences in residual life years among participants were estimated.

RESULTS

The study included 216 579 AF patients from year 2000 to 2020 and 866 316 controls. The mean follow-up time was 7.9 years. Comparing AF patients with matched controls, the hazard ratios among individuals ≤50 years was 8.90 [95% confidence interval (CI), 7.17-11.0] for cardiomyopathy, 8.64 (95% CI, 7.74-9.64) for heart failure, 2.18 (95% CI, 1.89-2.52) for ischaemic stroke, and 2.74 (95% CI, 2.53-2.96) for mortality. The expected average loss of life years among individuals ≤50 years was 9.2 years (95% CI, 9.0-9.3) years. The estimates decreased with older age.

CONCLUSIONS

The findings show that earlier diagnosis of AF is associated with a higher hazard ratio of subsequent myocardial disease and shorter life expectancy. Further studies are needed to determine causality and whether AF could be used as a risk marker among particularly younger patients.

The relationship between ambulatory arterial stiffness index and incident atrial fibrillation

BACKGROUND

The ambulatory arterial stiffness index (AASI) is an indirect measure of blood pressure variability and arterial stiffness which are atrial fibrillation (AF) risk factors. The relationship between AASI and AF development has not been previously investigated and was the primary aim of this study.

METHODS

This was an observational cohort study of adults (aged 18-85 years) in sinus rhythm, who underwent 24-h ambulatory blood pressure monitoring (ABPM) for the diagnosis of hypertension or its control.

RESULTS

Eight hundred and twenty-one patients (49% men) aged 58.7 ± 15.3 years were followed up for a median of 4.0 years (3317 patient-years). In total, 75 patients (9.1%) developed ≥1 AF episode during follow-up. The mean AASI was 0.46 ± 0.17 (median 0.46). AASI values (0.52 ± 0.16 vs. 0.45 ± 0.17; p < .001) and the proportion of AASI values above the median (65.3% vs. 48.4%; p = .005) were greater among the patients who developed AF versus those that did not respectively. AASI significantly correlated with age (r = .49; 95% confidence interval: 0.44-0.54: p < .001). On Kaplan-Meier analysis, higher baseline AASI by median, tertiles, and quartiles were all significantly associated with AF development (X2: 10.13; p < .001). On Cox regression analyses, both a 1-standard deviation increase and AASI > median were independent predictors of AF, but this relationship was no longer significant when age was included in the model.

CONCLUSIONS

AASI is an independent predictor of AF development. However, this relationship becomes insignificant after adjustment for age which is higher correlated with AASI.

Left ventricular hypertrophy phenotype to predict incident atrial fibrillation: The Multi-Ethnic Study of Atherosclerosis

BACKGROUND AND AIM

Left ventricular hypertrophy (LVH) has been shown to be associated with the occurrence of atrial fibrillation (AF). However, the predictive value of the LVH phenotype for incident AF remains uncertain. This study aimed to investigate the predictive value of LVH phenotype for incident AF.

METHODS AND RESULTS

This study utilized the Multi-Ethnic Study of Atherosclerosis (MESA) data. LVH was defined by cardiac magnetic resonance measured LV mass index. Isolated LVH was determined as LVH without elevated cardiac biomarker and malignant LVH was determined as LVH with at least 1 elevated biomarker. Receiver-operating characteristic (ROC) analysis was performed to calculate areas under the curves (AUC) for predicting AF. A total of 4983 community-dwelling participants were included, with a mean age of 61.5 years. 279 (5.6 %) had isolated LVH, and 222 (4.5 %) had malignant LVH. During a median follow-up of 8.5 years, 272 incident AF was observed. Compared to participants without LVH and elevated cardiac biomarkers, those with isolated LVH (HR, 1.82; 95 % CI, 1.03-3.20) and malignant LVH (HR, 4.13; 95 % CI, 2.77-6.16) had a higher risk of incident AF. Malignant LVH carried a 1.5-fold increased risk of AF compared to isolated LVH (HR: 2.48, 95 % CI: 1.30-4.73). Including the LVH phenotype in the CHARGE-AF model improved model discrimination (AUC increase: 0.03, p < 0.001).

CONCLUSIONS

The risks of AF incidence varied across LVH phenotypes. Malignant LVH carried the highest risk among LVH phenotypes. LVH phenotype provides incremental predictive value over the variables included in the CHARGE-AF model.

Risk calculator for incident atrial fibrillation across a range of prediction horizons

BACKGROUND

The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years.

METHODS

We used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc and C2HEST scores.

RESULTS

Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHA2DS2-VASc and C2HEST scores. The models are available as a web-based FIND-AF calculator.

CONCLUSIONS

The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study.

Deep learning of left atrial structure and function provides link to atrial fibrillation risk

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.

Modifiable lifestyle factors and lifetime risk of atrial fibrillation: longitudinal data from the Korea NHIS-HealS and UK Biobank cohorts

BACKGROUND

The reason for higher incidence of atrial fibrillation (AF) in Europe compared with East Asia is unclear. We aimed to investigate the association between modifiable lifestyle factors and lifetime risk of AF in Europe and East Asia, along with race/ethnic similarities and disparities.

METHODS

1:1 propensity score matched pairs of 242,763 East Asians and 242,763 White Europeans without AF were analyzed. Modifiable lifestyle factors considered were blood pressure, body mass index, cigarette smoking, diabetes, alcohol consumption, and physical activity, categorized as non-adverse or adverse levels. Lifetime risk of AF was estimated from the index age of 45 years to the attained age of 85 years, accounting for the competing risk of death.

RESULTS

The overall lifetime risk of AF was higher in White Europeans than East Asians (20.9% vs 15.4%, p < 0.001). The lifetime risk of AF was similar between the two races in individuals with non-adverse lifestyle factor profiles (13.4% vs 12.9%, p = 0.575), whereas it was higher in White Europeans with adverse lifestyle factor profiles (22.1% vs 15.8%, p < 0.001). The difference in the lifetime risk of AF between the two races increased as the burden of adverse lifestyle factors worsened (1 adverse lifestyle factor; 4.3% to ≥ 3 adverse lifestyle factors; 11.2%). Compared with East Asians, the relative risk of AF in White Europeans was 23% and 62% higher for one (hazard ratio [HR] 1.23, 95% confidence interval [CI] 1.16-1.29) and ≥ 3 adverse lifestyle factors (HR 1.62, 95% CI 1.51-1.75), respectively.

CONCLUSIONS

The overall higher lifetime risk of AF in White Europeans compared with East Asians might be attributable to adverse lifestyle factors. Adherence to healthy lifestyle factors was associated with the lifetime risk of AF of about 1 in 8 regardless of race/ethnicity.

Microvascular disease, modifiable risk factor profiles and incident arrhythmias in type 2 diabetes

BACKGROUND

To assess the roles of diabetic microvascular disease and modifiable risk factors and their combination in the development of arrhythmias.

METHODS

We included participants with type 2 diabetes (T2D) who were free of arrhythmias during recruitment in the UK Biobank study. The associations of microvascular disease states (defined by the presence of retinopathy, peripheral neuropathy or chronic kidney disease), four modifiable arrhythmic risk factors (body mass index, smoking, systolic blood pressure and glycosylated haemoglobin) and their joint associations with incident arrhythmias were examined.

RESULTS

Among the 25 632 participants with T2D, 1705 (20.1%) of the 8482 with microvascular disease and 2017 (11.8%) of the 17 150 without microvascular disease developed arrhythmias during a median follow-up of 12.3 years. Having any of the three microvascular diseases was associated with a 48% increase in the hazard of developing arrhythmias. Incorporating microvascular disease states into a model alongside 11 traditional risk factors significantly enhanced arrhythmia prediction. Furthermore, individuals with microvascular disease who had optimal levels of zero to one, two, three or four arrhythmic risk factors showed an HR of 2.05 (95% CI 1.85, 2.27), 1.67 (95% CI 1.53, 1.83), 1.35 (95% CI 1.22, 1.50) and 0.91 (95% CI 0.73, 1.13), respectively, compared with those without microvascular disease.

CONCLUSIONS

Although microvascular disease, a non-traditional risk factor, was associated with incident arrhythmias in individuals with T2D, having optimal levels of risk factors may mitigate this risk.

Lower Atrial Fibrillation Risk With Sodium-Glucose Cotransporter 2 Inhibitors Than With Dipeptidyl Peptidase-4 Inhibitors in Individuals With Type 2 Diabetes: A Nationwide Cohort Study

BACKGROUND AND OBJECTIVES

Accumulating evidence shows that sodium-glucose cotransporter 2 inhibitors (SGLT2is) reduce adverse cardiovascular outcomes. However, whether SGLT2i, compared with other antidiabetic drugs, reduce the new development of atrial fibrillation (AF) is unclear. In this study, we compared SGLT2i with dipeptidyl peptidase-4 inhibitors (DPP-4is) in terms of reduction in the risk of AF in individuals with type 2 diabetes.

METHODS

We included 42,786 propensity score-matched pairs of SGLT2i and DPP-4i users without previous AF diagnosis using the Korean National Health Insurance Service database between May 1, 2016, and December 31, 2018.

RESULTS

During a median follow-up of 1.3 years, SGLT2i users had a lower incidence of AF than DPP-4i users (1.95 vs. 2.65 per 1,000 person-years; hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.55-0.97; p=0.028]). In individuals without heart failure, SGLT2i users was associated with a decreased risk of AF incidence (HR, 0.70; 95% CI, 0.52-0.94; p=0.019) compared to DPP-4i users. However, individuals with heart failure, SGLT2i users was not significantly associated with a change in risk (HR, 1.04; 95% CI, 0.44-2.44; p=0.936).

CONCLUSIONS

In this nationwide cohort study of individuals with type 2 diabetes, treatment with SGLT2i was associated with a lower risk of AF compared with treatment with DPP-4i.

Even mild mitral regurgitation is associated with incident atrial fibrillation in the general population

AIMS

Mitral regurgitation (MR) can be difficult to quantify. We sought to investigate whether the MR jet area to left atrial (LA) area ratio (MR/LA ratio) method for quantifying MRs can be used to predict incident atrial fibrillation (AF) in the general population.

METHODS AND RESULTS

The study included 4466 participants from the 5th Copenhagen City Heart Study, a prospective general population study, who underwent transthoracic echocardiography. MR jet area was measured and indexed to LA area. The endpoint was incident AF. MR was quantified in 4042 participants (mean age: 57 years, 43% men). Of these, 198 (4.9%) developed AF during a median follow-up period of 5.3 years (interquartile range: 4.4-6.1 years). MR was present in 1938 participants (48%) including 1593 (39%) trace/mild MRs (MR/LA ratio ≤ 20% and ≤4 cm2). In unadjusted analysis, MR/LA ratio was associated with incident AF [HR: 1.06 (1.00-1.13), P = 0.042 per 5% increase] but not after adjusting for CHARGE-AF score. However, the association was modified by age (P for interaction = 0.034), such that MR/LA ratio was associated with AF only in participants ≤ 73 years. In these participants, MR/LA ratio 'was' independently associated with AF after adjusting for CHARGE-AF score [HR: 1.14 (1.06-1.24), P = 0.001, per 5% increase]. This finding persisted when restricting the analysis to participants without moderate or severe MR and normal LA size [HR: 1.35 (1.09-1.68), P = 0.005, per 5% increase].

CONCLUSION

MR, including even trace regurgitations quantified by MR/LA ratio, is independently associated with incident AF in individuals ≤ 73 years of age.

Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population

Atrial fibrillation (AF) is an arrhythmic disease. Prediction of AF development in healthy individuals is important before serious complications occur. We aimed to develop a risk prediction score for future AF using participants' data, including electrocardiogram (ECG) measurements and information such as age and sex. We included 88,907 Japanese participants, aged 30-69 years, who were randomly assigned to derivation and validation cohorts in a ratio of 1:1. We performed multivariate logistic regression analysis and obtained the standardised beta coefficient of relevant factors and assigned scores to them. We created a score based on prognostic factors for AF to predict its occurrence after five years and applied it to validation cohorts to assess its reproducibility. The risk score ranged from 0 to 17, consisting of age, sex, PR prolongation, QT corrected for heart rate prolongation, left ventricular hypertrophy, premature atrial contraction, and left axis deviation. The area under the curve was 0.75 for the derivation cohort and 0.73 for the validation cohort. The incidence of new-onset AF reached over 2% at 10 points of the risk score in both cohorts. Thus, in this study, we showed the possibility of predicting new-onset AF using ECG findings and simple information.

Deep learning evaluation of echocardiograms to identify occult atrial fibrillation

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

Effect of an Intensive Lifestyle Intervention on Circulating Biomarkers of Atrial Fibrillation-Related Pathways among Adults with Metabolic Syndrome: Results from a Randomized Trial

Background: Lifestyles influence atrial fibrillation (AF) risk. Determining the effect of lifestyle interventions on blood concentrations of biomarkers of AF-related pathways could help understand AF pathophysiology and contribute to AF prevention. Methods: We studied 532 participants enrolled in the PREDIMED-Plus trial, a Spanish randomized trial conducted in adults (55-75 years) with metabolic syndrome and body mass index between 27-40 kg/m2. Eligible participants were randomized 1:1 to an intensive lifestyle intervention, emphasizing physical activity, weight loss, and adherence to an energy-reduced Mediterranean diet or to a control group. Serum biomarkers [carboxy-terminal propeptide of procollagen type I (PICP), high-sensitivity troponin T (hsTnT), high-sensitivity C reactive protein (hsCRP), 3-nitrotyrosine (3-NT), and N-terminal propeptide of B-type natriuretic peptide (NT-proBNP)] were measured at baseline, 3 and 5 years after randomization. Mixed models were used to evaluate the effect of intervention on changes in biomarkers through year 5. Mediation analysis was performed to examine the proportion mediated by each component of the intervention. Results: At baseline, participants' mean age was 65, 40% were female, and 50% were assigned to the intervention. After five years, mean changes in log-transformed biomarkers were -0.01 (PICP), 0.20 (hsTnT), -0.17 (hsCRP), 0.12 (3-NT), and 0.27 (NT-proBNP). Compared to the control group, participants in the intervention group experienced greater decreases in hsCRP (-14%, 95% confidence interval (CI) -26%, 0%) or smaller increases in 3-NT (-16%, 95% CI -25%, -5%) and NT-proBNP (-12%, 95% CI -23%, 1%). The intervention had minimal impact on hsTnT (-3%, 95% CI -7%, 2%) or PICP concentrations (-2%, 95% CI -9%, 6%). The effect of the intervention on hsCRP was primarily mediated by weight loss (89% at year 5). Conclusions: Over five years, a dietary and lifestyle intervention for weight-loss favorably affected concentrations of hsCRP, 3-NT, and NT-proBNP, pointing to specific mechanisms in pathways linking lifestyles and AF.

Upregulated Genes in Atrial Fibrillation Blood and the Left Atrium

INTRODUCTION

Atrial fibrillation (AF) is a common arrhythmia associated with aging. Many known risk factors are associated with AF, but many senior individuals do not develop AF despite having multiple risk factors. This finding suggests that other factors may be involved in AF onset. This study aimed to identify upregulated genes in the peripheral blood and left atrium of patients with AF. These genes may serve as potential biomarkers to predict AF onset risk and its complications.

METHODS

Gene expression data were analyzed from blood (n = 3) and left atrial samples (n = 15) of patients with AF and sinus rhythm. We evaluated the significant genes identified using p value analysis of weighted average difference to confirm their rankings. We created figures for the genes using GeneMANIA and performed a functional analysis using Cytoscape3.10.1. Hub and bottleneck genes were identified based on degree and betweenness centrality. We used reference expression (RefEx) to confirm the organs in which the extracted genes were expressed. Heatmaps and Gene ontology term evaluation were performed to further elucidate the biological functions of the genes.

RESULTS

We identified 12 upregulated genes (CAST, ASAH1, MAFB, VCAN, DDIT4, FTL, HEXB, PROS1, BNIP3L, PABPC1, YBX3, and S100A6) in both the blood and left atrium of patients with AF. We analyzed the gene functions using GeneMANIA and Cytoscape. The identified genes were involved in a variety of pathways, including lysosomal function and lipid and sphingolipid catabolism. Next, we investigated whether the 12 identified genes identified were systemically expressed or had high organ specificity. Finally, RefEx was used to analyze the gene expression levels in various tissues. Four genes, FTL, ASAH1, S100A6, and PABPC1, were highly expressed in the normal heart tissue. Finally, we evaluated the expression levels of the 12 genes in the blood of patients with AF using a heatmap. Our findings suggest that the 12 genes identified in this study, especially the lysosome-related genes (FTL and ASAH1), may be involved in AF pathogenesis.

CONCLUSION

Lysosome-related genes may be important to understand the AF pathophysiology and to develop AF-related future studies.

Left Atrial Strain Predicts Subclinical Atrial Fibrillation Detected by Long-term Continuous Monitoring in Elderly High-Risk Individuals

BACKGROUND

Left atrial (LA) speckle tracking provides detailed information on atrial function. Its utility for predicting subclinical atrial fibrillation (SCAF) is unclear. Therefore, we sought to investigate whether LA strain measures could predict SCAF detected by long-term continuous rhythm monitoring.

METHODS

This was an echocardiographic substudy of the LOOP study, where elderly at risk of stroke were randomized to receive a loop recorder (Reveal LINQ) or control. Participants who received a loop recorder were included in this analysis. Echocardiography included LA reservoir, conduit, and contraction strain. Participants were followed with continuous rhythm monitoring for SCAF (≥6 minutes). Cox proportional hazards regression was applied to adjust for atrial fibrillation risk factors.

RESULTS

In total, 956 participants were eligible for analysis. Median continuous rhythm monitoring was 35 months (IQR, 20-40 months), during which 278 (29%) were diagnosed with SCAF. The mean age was 74 years, 56% were male, median CHA2DS2-VASc-score was 4. LA reservoir strain was an independent predictor of SCAF after multivariable adjustments (HR, 1.04 [1.02-1.05], per 1% decrease) and so was contraction strain. The findings were unchanged in competing risk analyses and in participants with normal LA size and diastolic function. Participants with low reservoir strain (<33%) had a significantly higher risk of SCAF compared with those with high reservoir strain (incidence rate, 14.5 [12.4-16.9] versus 9.8 [8.2-11.8] events/100 person-years). The same was noted for low versus high contraction strain.

CONCLUSIONS

LA reservoir and contraction strain are independent predictors of SCAF in elderly at risk of stroke. This also applies to individuals with normal LA size and diastolic function.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02036450.

The added value of temporal data and the best way to handle it: A use-case for atrial fibrillation using general practitioner data

INTRODUCTION

Temporal data has numerous challenges for deep learning such as irregularity of sampling. New algorithms are being developed that can handle these temporal challenges better. However, it is unclear how the performance ranges from classical non-temporal models to newly developed algorithms. Therefore, this study compares different non-temporal and temporal algorithms for a relevant use case, the prediction of atrial fibrillation (AF) using general practitioner (GP) data.

METHODS

Three datasets with a 365-day observation window and prediction windows of 14, 180 and 360 days were used. Data consisted of medication, lab, symptom, and chronic diseases codings registered by the GP. The benchmark discarded temporality and used logistic regression, XGBoost models and neural networks on the presence of codings over the whole year. Pattern data extracted common patterns of GP codings and tested using the same algorithms. LSTM and CKConv models were trained as models incorporating temporality.

RESULTS

Algorithms which incorporated temporality (LSTM and CKConv, (max AUC 0.734 at 360 days prediction window) outperformed both benchmark and pattern algorithms (max AUC 0.723, with a significant improvement using the 360 days prediction window (p = 0.04). The difference between the benchmark and the LSTM or CKConv algorithm decreased with smaller prediction windows, indicating temporal importance for longer prediction windows. The CKConv and LSTM algorithm performed similarly, possibly due to limited sequence length.

CONCLUSION

Temporal models outperformed non-temporal models for the prediction of AF. For temporal models, CKConv is a promising algorithm to handle temporal data using GP data as it can handle irregular data.

Epicardial adipose tissue and subclinical incident atrial fibrillation as detected by continuous monitoring: a cardiac magnetic resonance imaging study

Epicardial adipose tissue (EAT) has endocrine and paracrine functions and has been associated with metabolic and cardiovascular disease. This study aimed to investigate the association between EAT, determined by cardiac magnetic resonance imaging (CMR), and incident atrial fibrillation (AF) following long-term continuous heart rhythm monitoring by implantable loop recorder (ILR). This study is a sub-study of the LOOP study. In total, 203 participants without a history of AF received an ILR and underwent advanced CMR. All participants were at least 70 years of age at inclusion and had at least one of the following conditions: hypertension, diabetes, previous stroke, or heart failure. Volumetric measurements of atrial- and ventricular EAT were derived from CMR and the time to incident AF was subsequently determined. A total of 78 participants (38%) were diagnosed with subclinical AF during a median of 40 (37-42) months of continuous monitoring. In multivariable Cox regression analyses adjusted for age, sex, and various comorbidities, we found EAT indexed to body surface area to be independently associated with the time to AF with hazard ratios (95% confidence intervals) up to 2.93 (1.36-6.34); p = 0.01 when analyzing the risk of new-onset AF episodes lasting ≥ 24 h. Atrial EAT assessed by volumetric measurements on CMR images was significantly associated with the incident AF episodes as detected by ILR.

Association between the Albumin-to-Globulin Ratio and Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy

Background

Atrial fibrillation (AF), which occurs four to six times more frequently in hypertrophic cardiomyopathy (HCM) patients than in the general population, is the most common persistent arrhythmia and has a substantial therapeutic consequence. In HCM patients, there are currently no discovered signs that could be utilized to identify AF.

Methods

From 2018 to 2022, 493 individuals with a continuous diagnosis of HCM were examined at Beijing Anzhen Hospital. AF was proven using routine electrocardiography (ECG), 24-hour Holter ECGs, or bedside ECGs. Echocardiography and blood tests were performed for all patients. Analysis and comparison of the traits were performed in HCM patients with AF (n = 77) and without AF (n = 416).

Results

Age (p < 0.001), prevalence of ventricular tachycardia (VT, p < 0.001), prevalence of pulmonary artery hypertension (p = 0.027), and albumin-to-globulin ratio (AGR, p = 0.046) were all significantly higher in patients with AF, compared to patients without AF. In multivariate logistic analysis, age (odds ratio [OR], 1.063; 95% confidence interval [CI], 1.032-1.095; p < 0.001), history of VT (OR, 2.702; 95% CI, 1.007-7.255; p = 0.048), AGR (OR, 3.477; 95% CI, 1.417-8.536; p = 0.007), left atrial diameter (OR, 1.132; 95% CI, 1.073-1.194; p < 0.001), left ventricular end-diastolic diameter (OR, 0.861; 95% CI, 0.762-0.974; p = 0.017), left ventricular end-systolic diameter (OR, 1.239; 95% CI, 1.083-1.417; p = 0.002), and peak A wave velocity (OR, 0.983; 95% CI, 0.972-0.994; p = 0.002) were independently associated with AF in HCM patients. In the receiver operating characteristic curve analysis, the area under the curve for the established model was 0.819 (95% CI, 0.755-0.883, p = 0.033), with a sensitivity and specificity of 0.763 and 0.816, respectively, for AF occurrence in HCM patients.

Conclusions

In individuals with HCM, a history of VT and a higher AGR are independently linked to AF. Further investigation is necessary to determine whether increased AGR represents a risk factor for embolic stroke or cardiovascular death.