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

Unveiling Atrial Fibrillation: The Risk Factors, Prediction, and Primary Prevention

Atrial fibrillation (AF) is a highly prevalent, progressive cardiac arrhythmia that significantly impacts the patient's health-related quality of life. AF is linked to a 5-fold and 2-fold higher risk of stroke and cognitive dysfunction, respectively. With advancements in cardiac electrophysiology, many risk factors have been identified, which increase the risk for the development of AF. These risk factors encompassing age, hypertension, smoking, diabetes mellitus, male gender, obesity, alcohol intake, obstructive sleep apnea and so on, can be categorized into 3 major groups: modifiable, non-modifiable, and cardiac. Multiple AF prediction models have been successfully validated to identify people at high risk of AF development using these risk factors. These prediction models, such as CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) and HARMS2-AF score can be used in clinical practice because of their easy applicability. It is crucial to address modifiable risk factors in individuals with a high risk of developing AF. Furthermore, the implementation of primary AF prevention in individuals at high risk can contribute to improved long-term outcomes. This review aims to provide the most recent, concise explanation of the risk factors linked to AF, the prediction of AF, and strategies for the primary prevention of AF.

Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study

BACKGROUND AND AIMS

Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk.

METHODS

An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed.

RESULTS

The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), -.1 (6.0), 4.7 (8.7), and -1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24-2.78), 1.89 (1.46-2.43), 1.90 (1.55-2.33), and 1.76 (1.67-1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47-3.37), 1.94 (1.39-2.70), 1.58 (1.06-2.35), and 1.79 (1.62-1.97) in these cohorts compared with the normal group.

CONCLUSIONS

The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.

Atrial Fibrillation-Induced Cardiomyopathy

Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias in the world. Patients with AF also suffer from heart failure (HF). The relationship between AF and HF is often considered bidirectional and both share very similar risk factors. The mechanism of AF-induced cardiomyopathy lies in 3 distinct components: tachycardia-related cardiac dysfunction, heart rhythm irregularity, and AF-induced atrial myopathy. These components are mediated by calcium mishandling, neurohormonal activation, oxidative stress, myocardial supply-demand mismatch, and irreversible fibrosis and remodeling. Managing AF-induced cardiomyopathy should focus on early rhythm control to mitigate the development of irreversible remodeling and atrial myopathy.

Atrial Fibrillation Ablation in Heart Failure with Reduced Ejection Fraction

Multiple randomized clinical trials have demonstrated catheter ablation in heart failure with reduced ejection fraction reduces mortality and hospitalization as well as improves ventricular function, quality of life, and functional status. Catheter ablation has been shown to be superior to alternative rate and rhythm control strategies in these outcomes. Guidelines strongly support the use of catheter ablation to maintain sinus rhythm in patients with atrial fibrillation and heart failure.

Impact of a Clinical Atrial Fibrillation Risk Estimation Tool on Cardiac Rhythm Monitor Utilization Following Acute Ischemic Stroke: A Pre-Post Clinical Trial

BACKGROUND

Detection of undiagnosed atrial fibrillation (AF) after ischemic stroke through extended cardiac monitoring is important for preventing recurrent stroke. We evaluated whether a tool that displays clinically predicted AF risk to clinicians caring for stroke patients was associated with the use of extended cardiac monitoring.

METHODS

We prospectively included hospitalized ischemic stroke patients without known AF in a pre-intervention (October 2018 - June 2019) and intervention period (March 11, 2021 - March 10, 2022). The intervention consisted of an electronic health record (EHR)-based best-practice advisory (BPA) alert which calculated and displayed 5-year risk of AF. We used a multivariable Fine and Gray model to test for an interaction between predicted AF risk and period (pre-intervention vs. intervention) with regards to incidence of extended cardiac monitoring. We compared the incidence of extended cardiac monitoring within 6-months of discharge between periods, stratified by BPA completion.

RESULTS

We included 805 patients: 493 in the pre-intervention cohort and 312 in the intervention cohort. In the intervention cohort, the BPA was completed for 180 (58%) patients. The association between predicted clinical risk of AF and incidence of 6-month extended cardiac monitoring was not different by time period (interaction HR = 1.00 [95% Confidence Interval (CI) 0.98; 1.02]). The intervention period was associated with an increased cumulative incidence of cardiac monitoring (adjusted HR = 1.32 [95% CI 1.03-1.69]).

CONCLUSIONS

An embedded EHR tool displaying predicted AF risk in a post-stroke setting had limited clinician engagement and predicted risk was not associated with the use of extended cardiac monitoring.

CLINICAL TRIAL REGISTRATION

NCT04637087.

Association of Coagulation Factor XI Level With Cardiovascular Events and Cardiac Function in Community-Dwelling Adults: From ARIC and CHS

BACKGROUND

Coagulation factor XI (FXI) inhibitors are a promising and novel class of anticoagulants, but a recent animal study found that FXI inhibition exacerbated diastolic dysfunction and heart failure (HF). In the ARIC study (Atherosclerosis Risk in Communities), we investigated whether plasma FXI level was associated with cardiovascular events and cardiac function.

METHODS

ARIC was our primary analytic cohort. We included 4471 participants (median age, 75 years; 57% female; 17% Black) who attended visit 5 (2011-2013) with Somalogic-quantified plasma FXI levels and echocardiographic cardiac function. Prevalent HF and atrial fibrillation (AF) cases were defined as having HF or AF diagnosed at or before each participant's visit 5 exam date. Incident HF and AF events were ascertained through 2021. Associations were assessed using Cox, logistic, and linear regression models. Primary prospective associations were also validated in the CHS (Cardiovascular Health Study) using an orthogonal FXI assay (enzyme-linked immunosorbent assay).

RESULTS

At ARIC visit 5, there were 665 and 419 participants with prevalent HF and AF, respectively. During a median follow-up of 9 years, there were 580 and 788 incident HF and AF events, respectively. Lower FXI level was associated prospectively with higher incidence of HF (hazard ratio [HR], 1.36 [for each 1-unit decrement of log2-transformed FXI level] [95% CI, 1.01-1.83]) but not incident AF, and cross-sectionally with increased odds of AF (odds ratio [OR], 1.96 [95% CI, 1.23-3.07]) but not HF. In age-stratified analyses, decreased FXI was associated with higher incidence of HF in participants ≥75 years of age (HR, 1.57 [95% CI, 1.08-2.28]) but not <75 years of age (HR, 1.11 [95% CI, 0.68-1.79]). The inverse FXI-HF association was validated in CHS (HR, 1.18 [95% CI, 1.02-1.36]). At ARIC visit 5, lower FXI level was also associated with higher prevalence of diastolic dysfunction and worse E/A ratio, left atrial (LA) volume index, LA function, and left ventricular mass index, but not left ventricular ejection fraction or global longitudinal strain.

CONCLUSIONS

Decreased FXI level is associated with greater incidence of HF, especially in older adults. It is also associated with prevalent AF, worse diastolic function, worse LA function, and greater LA size. More research is needed to assess potential unwanted effects of FXI inhibition on the risk of cardiovascular events and cardiac function.

Racial differences and similarities in atrial fibrillation epidemiology and risk factors in UK Biobank and Korean NHIS-HEALS cohort studies

BACKGROUND

The increasing prevalence of atrial fibrillation (AF) requires efforts to understand racial differences in disease distribution and risk factors.

OBJECTIVE

We aimed to compare associations between risk factors and AF in White Europeans from the UK Biobank and Asians from the Korean National Health Insurance Service-Health Screening (NHIS-HEALS) study.

METHODS

This study included participants from the Korean NHIS-HEALS and UK Biobank. After matching for age and sex, 206,704 participants in the Korean NHIS-HEALS and 206,704 participants in the UK Biobank were enrolled in the study. The incidence of AF, its associations with biomarkers, prevalent cardiovascular disease, and population attributable risk by race were examined.

RESULTS

During a median follow-up of 7.1 years in the Korean NHIS-HEALS and 11.9 years in the UK Biobank, those in the UK Biobank showed a higher incidence and risk of AF (3.99 vs 3.41 per 1000 person-years; hazard ratio, 1.20; 95% confidence interval [CI], 1.15-2.25) compared with the population in the Korean NHIS-HEALS. Body mass index (BMI), systolic blood pressure, alcohol, heart failure, myocardial infarction, and stroke were associated with an increased risk of new-onset AF in both cohorts. Higher BMI and smoking were more strongly related to the increased risk of new-onset AF in the UK Biobank compared with the Korean NHIS-HEALS, with a relative risk ratio of 1.21 (95% CI, 1.17-1.25) and 1.12 (95% CI, 1.02-1.21), respectively.

CONCLUSION

In this first large-scale comparison of White and Asian populations, the cumulative risk for development of AF was higher in the United Kingdom than in Korea. Higher BMI and smoking were associated with a higher risk of AF in the United Kingdom than in Korea.

Atrial fibrillation outcomes in patients from Asia and non-Asia countries: insights from GARFIELD-AF

BACKGROUND

Differences in the clinical outcomes and level of risk among Asian versus non-Asian patients with atrial fibrillation (AF) have been sparsely investigated.

OBJECTIVE

To provide a contemporary prospective comparison of outcomes for newly diagnosed patients with AF, between Asian and non-Asian regions.

METHODS

Six Asian countries (China, Japan, India, Singapore, South Korea and Thailand) and 29 countries outside Asia participated in the Global Anticoagulant Registry in the FIELD-AF (GARFIELD-AF) study. Newly diagnosed patients with AF, enrolled between 2010 and 2016, were followed up for≥2 years. The outcome studies were all-cause, cardiovascular and non-cardiovascular mortality, non-haemorrhagic stroke/systemic embolism (SE), major bleeding. The association of geographical region with clinical outcomes (event rates per 100 person-years) were estimated using multivariable Cox models.

RESULTS

13 841/52 057 (26.6%) GARFIELD-AF participants were enrolled in Asia. Average age and prevalence of cardiovascular comorbidities were lower than in non-Asian countries and patients at high risk of stroke (ie, CHA2DS2-VASc≥2 excl. sex) were less frequently anticoagulated (60.1% vs 73.2%). Non-vitamin K oral anticoagulant (NOAC) was similar in both regions (∼28%), though Asian patients were more frequently underdosed. Both Asian and non-Asian patients who received NOAC at enrolment experienced lower all-cause mortality and non-haemorrhagic stroke/SE compared with patients on other treatments or none.All-cause mortality, non-cardiovascular mortality and major bleeding were less frequent in patients from Asia versus non-Asia (HR (95% CI): 0.62 (0.39 to 0.99), 0.52 (0.28 to 0.97), 0.58 (0.36 to 0.96), respectively). Associations of moderate-to-severe chronic kidney disease and vascular disease with increased risk of all-cause mortality were stronger in Asian versus non-Asian patients (interaction p values: 0.0250 and 0.0076, respectively). There was notable heterogeneity in oral anticoagulant (OAC) usage within the Asian countries.

CONCLUSIONS

Patients in Asian countries had a lower risk of all-cause mortality and major bleeding compared to the rest of the world. NOAC had evident benefits for reducing mortality and stroke across populations. Further studies on sociocultural impacts on OAC outcomes are needed.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT01090362.

Role of primary and secondary care data in atrial fibrillation ascertainment: impact on risk factor associations, patient management, and mortality in UK Biobank

AIMS

Electronic healthcare records (EHR) are at the forefront of advances in epidemiological research emerging from large-scale population biobanks and clinical studies. Hospital admissions, diagnoses, and procedures (HADP) data are often used to identify disease cases. However, this may result in incomplete ascertainment of chronic conditions such as atrial fibrillation (AF), which are principally managed in primary care (PC). We examined the relevance of EHR sources for AF ascertainment, and the implications for risk factor associations, patient management, and outcomes in UK Biobank.

METHODS AND RESULTS

UK Biobank is a prospective study, with HADP and PC records available for 230 000 participants (to 2016). AF cases were ascertained in three groups: from PC records only (PC-only), HADP only (HADP-only), or both (PC + HADP). Conventional statistical methods were used to describe differences between groups in terms of characteristics, risk factor associations, ascertainment timing, rates of anticoagulation, and post-AF stroke and death. A total of 7136 incident AF cases were identified during 7 years median follow-up (PC-only: 22%, PC + HADP: 49%, HADP-only: 29%). There was a median lag of 1.3 years between cases ascertained in PC and subsequently in HADP. AF cases in each of the ascertainment groups had comparable baseline demographic characteristics. However, AF cases identified in hospital data alone had a higher prevalence of cardiometabolic comorbidities and lower rates of subsequent anticoagulation (PC-only: 44%, PC + HADP: 48%, HADP-only: 10%, P < 0.0001) than other groups. HADP-only cases also had higher rates of death [PC-only: 9.3 (6.8, 12.7), PC + HADP: 23.4 (20.5, 26.6), HADP-only: 81.2 (73.8, 89.2) events per 1000 person-years, P < 0.0001] compared to other groups.

CONCLUSION

Integration of data from primary care with that from hospital records has a substantial impact on AF ascertainment, identifying a third more cases than hospital records alone. However, about a third of AF cases recorded in hospital were not present in the primary care records, and these cases had lower rates of anticoagulation, as well as higher mortality from both cardiovascular and non-cardiovascular causes. Initiatives aimed at enhancing information exchange of clinically confirmed AF between healthcare settings have the potential to benefit patient management and AF-related outcomes at an individual and population level. This research underscores the importance of access and integration of de-identified comprehensive EHR data for a definitive understanding of patient trajectories, and for robust epidemiological and translational research into AF.

The mC2HEST Score for Incident Atrial Fibrillation: MESA (Multi-Ethnic Study of Atherosclerosis)

Background

Assessing individuals' risk of developing incident atrial fibrillation (AF) is important for making preventive and screening strategies.

Objectives

The performance of the mC2HEST score for predicting incident AF has scarcely been evaluated, especially in a multi-ethnic population.

Methods

Participants from the MESA (Multi-Ethnic Study of Atherosclerosis were enrolled in the present study, which involved population of different ethnicities (Caucasian, African-American, Chinese-American, and Hispanic) aged between 45 and 84 from 6 communities in the United States. The discriminative and calibration performance of the mC2HEST score was compared with other risk models.

Results

A total of 4,524 subjects (mean age 60.2 ± 9.5 years; 53.0% female) were included; 565 (mean age 67.0 ± 7.9 years; 46.5% female) developed AF during 13.6 ± 2.5 years of follow-up, with an incidence of 0.93%/year. The mC2HEST score had good prediction at 10 years (C-index, 0.72; 95% CI: 0.701 to 0.753), and 15 years (0.773, 95% CI: 0.749 to 0.798). The risk of incident AF increased with higher mC2HEST score points and risk groups (log-rank P < 0.001). The mC2HEST score showed positive net reclassification indexes (0.057, 0.090, 0.128, and 0.143) and integrated discriminative improvement (3.2%, 3.9%, 5.7%, and 4.9%) compared with C2HEST, HAVOC, HATCH, and CHA2DS2-VASc scores, respectively. Optimal calibration was seen in the mC2HEST score (P = 0.41).

Conclusions

The mC2HEST score is a practical model for predicting individuals' risk of incident AF that may be used for guiding AF surveillance, resource allocation, and screening strategies.

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.

Atrial Fibrillation: A Review

Importance

In the US, approximately 10.55 million adults have atrial fibrillation (AF). AF is associated with significantly increased risk of stroke, heart failure, myocardial infarction, dementia, chronic kidney disease, and mortality.

Observations

Symptoms of AF include palpitations, dyspnea, chest pain, presyncope, exertional intolerance, and fatigue, although approximately 10% to 40% of people with AF are asymptomatic. AF can be detected incidentally during clinical encounters, with wearable devices, or through interrogation of cardiac implanted electronic devices. In patients presenting with ischemic stroke without diagnosed AF, an implantable loop recorder (ie, subcutaneous telemetry device) can evaluate patients for intermittent AF. The 2023 American College of Cardiology (ACC)/American Heart Association (AHA)/American College of Clinical Pharmacy (ACCP)/Heart Rhythm Society (HRS) Guideline writing group proposed 4 stages of AF evolution: stage 1, at risk, defined as patients with AF-associated risk factors (eg, obesity, hypertension); stage 2, pre-AF, signs of atrial pathology on electrocardiogram or imaging without AF; stage 3, the presence of paroxysmal (recurrent AF episodes lasting ≤7 days) or persistent (continuous AF episode lasting >7 days) AF subtypes; and stage 4, permanent AF. Lifestyle and risk factor modification, including weight loss and exercise, to prevent AF onset, recurrence, and complications are recommended for all stages. In patients with estimated risk of stroke and thromboembolic events of 2% or greater per year, anticoagulation with a vitamin K antagonist or direct oral anticoagulant reduces stroke risk by 60% to 80% compared with placebo. In most patients, a direct oral anticoagulant, such as apixaban, rivaroxaban, or edoxaban, is recommended over warfarin because of lower bleeding risks. Compared with anticoagulation, aspirin is associated with poorer efficacy and is not recommended for stroke prevention. Early rhythm control with antiarrhythmic drugs or catheter ablation to restore and maintain sinus rhythm is recommended by the 2023 ACC/AHA/ACCP/HRS Guideline for some patients with AF. Catheter ablation is first-line therapy in patients with symptomatic paroxysmal AF to improve symptoms and slow progression to persistent AF. Catheter ablation is also recommended for patients with AF who have heart failure with reduced ejection fraction (HFrEF) to improve quality of life, left ventricular systolic function, and cardiovascular outcomes, such as rates of mortality and heart failure hospitalization.

Conclusions and Relevance

AF is associated with increased rates of stroke, heart failure, and mortality. Lifestyle and risk factor modification are recommended to prevent AF onset, recurrence, and complications, and oral anticoagulants are recommended for those with an estimated risk of stroke or thromboembolic events of 2% or greater per year. Early rhythm control using antiarrhythmic drugs or catheter ablation is recommended in select patients with AF experiencing symptomatic paroxysmal AF or HFrEF.

Clinical significance of a new diagnosis of atrial fibrillation in patients with vascular disease

BACKGROUND

The Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial enrolled patients with vascular disease but excluded patients requiring oral anticoagulation.

OBJECTIVE

We aimed to explore the clinical significance of a new diagnosis of atrial fibrillation (AF) during follow-up.

METHODS

New AF was identified from hospitalization, study drug discontinuation, and adverse event reports. Multivariable Cox regression was used to determine risk factors for new AF. Time-updated covariate analysis was used to study the association of new AF with outcomes.

RESULTS

During a mean follow-up of 23 months, 655 of 27,395 participants (2.4%) were diagnosed with AF (incidence, 1.3 per 100 patient-years). In adjusted analyses, advanced age, male sex, White ethnicity, higher body mass index, higher systolic blood pressure, heart failure, and prior myocardial infarction were associated with new AF. Compared with participants without a new diagnosis of AF during follow-up or before receiving a diagnosis of new AF, participants were at increased risk of a composite outcome of cardiovascular death, stroke, or myocardial infarction after a new diagnosis of AF (8.8 vs 2.4 per 100 patient-years; hazard ratio [HR], 3.66; 95% confidence interval [CI], 2.81-4.75). Risk increases with new AF were also observed for hospitalization for heart failure (6.8 vs 0.8 per 100 patient-years; HR, 8.64; 95% CI, 6.31-11.83) and major bleeding (3.9 vs 1.3 per 100 patient-years; HR, 3.18; 95% CI, 2.15-4.69).

CONCLUSION

In patients with vascular disease, a new diagnosis of AF was associated with a marked increase in risk of adverse outcomes, especially hospitalization for heart failure.

Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study

Aging affects the 12-lead electrocardiogram (ECG) and correlates with cardiovascular disease (CVD). AI-ECG models estimate aging effects as a novel biomarker but have only been evaluated on single ECGs-without utilizing longitudinal data. We validated an AI-ECG model, originally trained on Brazilian data, using a German cohort with over 20 years of follow-up, demonstrating similar performance (r2 = 0.70) to the original study (0.71). Incorporating longitudinal ECGs revealed a stronger association with cardiovascular risk, increasing the hazard ratio for mortality from 1.43 to 1.65. Moreover, aging effects were associated with higher odds ratios for atrial fibrillation, heart failure, and mortality. Using explainable AI methods revealed that the model aligns with clinical knowledge by focusing on ECG features known to reflect aging. Our study suggests that aging effects in longitudinal ECGs can be applied on population level as a novel biomarker to identify patients at risk early.

Long-term changes in frailty and incident atrial fibrillation, heart failure, coronary heart disease, and stroke: A prospective follow-up study

BACKGROUND

People with frailty have increased prevalence and incidence of atrial fibrillation (AF).

OBJECTIVE

The study aimed to further investigate the association of long-term changes in frailty with risk of new-onset AF. Its associations with heart failure (HF), coronary heart disease (CHD), and stroke were also evaluated as a secondary aim.

METHODS

More than 50,000 participants from UK Biobank cohort were included, with frailty index (FI) data and free of AF, HF, CHD, or stroke in baseline and follow-up assessments. Frailty status of the participants was categorized into nonfrail, prefrail, and frail based on their FI scores. FI in baseline and follow-ups are used to calculate the trajectories of frailty (ΔFI).

RESULTS

During a median of 5.1 years of follow-up from the final assessment, 1729 cases of AF were recorded. Frailty trajectory analysis showed that even a 0.01 point per year increase in ΔFI was associated with 14% (95% confidence interval [CI] 1.08-1.20) higher risk of AF, independent of baseline FI after adjusting for potential confounders. Compared with maintained nonfrail participants, those with sustained frail status had the highest risk of incident AF (hazard ratio [HR] 1.95, 1.61-2.36). The risk declined by 30% (95% CI 0.53-0.94) when frail participants regressed to nonfrail or prefrail status, compared with sustained frail participants. These associations were similar in HF and CHD however not significant in stroke.

CONCLUSION

In middle-aged and elderly individuals, frailty remission or nonfrailty maintenance was associated with lower risk of AF, HF, and CHD compared with persistent frailty, regardless of previous frailty status and established risk factors.

Micronutrient deficiencies and new-onset atrial fibrillation in a community-based cohort: data from PREVEND

AIM

Malnutrition has been linked to cardiovascular diseases. Both selenium and iron deficiency have been associated with worse prognosis in patients with heart failure (HF). Yet, little is known about the role of micronutrients in the development of atrial fibrillation (AFib). In this study, we aimed to elucidate the association of micronutrient deficiencies with new-onset AFib.

METHODS

Selenium, magnesium, and iron parameters were measured in a well-characterized prospective cohort study (N = 5452). Selenium deficiency was defined as serum selenium < 70 μg/L, iron deficiency as serum ferritin < 30 μg/L, and magnesium deficiency as plasma magnesium < 0.85 mmol/L. New-onset AFib was the primary outcome. Additionally, we tested for previously reported effect-modifiers where applicable.

RESULTS

Selenium, iron, and magnesium deficiency was observed in 1155 (21.2%), 797 (14.6%), and 3600 (66.0%) participants, respectively. During a mean follow-up of 6.2 years, 136 (2.5%) participants developed new-onset AFib. Smoking status significantly interacted with selenium deficiency on outcome (p = 0.079). After multivariable adjustment for components of the CHARGE-AF model, selenium deficiency was associated with new-onset AFib in non-smokers (HR 1.69, 95% CI 1.09-2.64, p = 0.020), but not in smokers (HR 0.78, 95% CI 0.29-2.08, p = 0.619). Magnesium deficiency (HR 1.40, 95% CI 0.93-2.10, p = 0.110) and iron deficiency (HR 0.62, 95% CI 0.25-1.54, p = 0.307) were not significantly associated with new-onset AFib.

CONCLUSION

Selenium deficiency was associated with new-onset AFib in non-smoking participants. Interventional studies that investigate the effects of optimizing micronutrients status in a population at risk are needed to assess causality, especially in those with selenium deficiency.

Multimodal data integration to predict atrial fibrillation

Aims

Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.

Methods and results

We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF. We constructed a (i) clinical risk score using CHARGE-AF clinical variables, (ii) polygenic risk score using pre-determined weights, (iii) protein risk score using regularized logistic regression, and (iv) ECG risk score from a convolutional neural network. Risk prediction performance was measured using regularized logistic regression. After a median follow-up of 15.1 years, 1910 AF events occurred since Visit 3 and 229 participants had prevalent AF at Visit 5. The area under curve (AUC) improved from 0.660 to 0.752 (95% CI, 0.741-0.763) and from 0.737 to 0.854 (95% CI, 0.828-0.880) after addition of the polygenic risk score to the CHARGE-AF clinical variables for predicting incident and prevalent AF, respectively. Further addition of ECG and protein risk scores improved the AUC to 0.763 (95% CI, 0.753-0.772) and 0.875 (95% CI, 0.851-0.899) for predicting incident and prevalent AF, respectively.

Conclusion

A combination of clinical and polygenic risk scores was the most effective and parsimonious approach to predicting AF. Further addition of an ECG risk score or protein risk score provided only modest incremental improvement for predicting AF.

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/m 2 (men) and greater than 95 g/m 2 (women), and LAE was defined as LA volume index greater than 42 ml/m 2 . 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.

Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology

BACKGROUND

Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To address this issue, a deep-learning model was developed to diagnose AF even during periods of arrhythmia-free windows.

METHODS

The proposed method introduces a novel approach that integrates clinical data and electrocardiograms (ECGs) using a colorization technique. This technique recolors ECG images based on patients' demographic information while preserving their original characteristics and incorporating color correlations from statistical data features. Our primary objective is to enhance atrial fibrillation (AF) detection by fusing ECG images with demographic data for colorization. To ensure the reliability of our dataset for training, validation, and testing, we rigorously maintained separation to prevent cross-contamination among these sets. We designed a Dual-input Mixed Neural Network (DMNN) that effectively handles different types of inputs, including demographic and image data, leveraging their mixed characteristics to optimize prediction performance. Unlike previous approaches, this method introduces demographic data through color transformation within ECG images, enriching the diversity of features for improved learning outcomes.

RESULTS

The proposed approach yielded promising results on the independent test set, achieving an impressive AUC of 83.4%. This outperformed the AUC of 75.8% obtained when using only the original signal values as input for the CNN. The evaluation of performance improvement revealed significant enhancements, including a 7.6% increase in AUC, an 11.3% boost in accuracy, a 9.4% improvement in sensitivity, an 11.6% enhancement in specificity, and a substantial 25.1% increase in the F1 score. Notably, AI diagnosis of AF was associated with future cardiovascular mortality. For clinical application, over a median follow-up of 71.6 ± 29.1 months, high-risk AI-predicted AF patients exhibited significantly higher cardiovascular mortality (AF vs. non-AF; 47 [18.7%] vs. 34 [4.8%]) and all-cause mortality (176 [52.9%] vs. 216 [26.3%]) compared to non-AF patients. In the low-risk group, AI-predicted AF patients showed slightly elevated cardiovascular (7 [0.7%] vs. 1 [0.3%]) and all-cause mortality (103 [9.0%] vs. 26 [6.4%]) than AI-predicted non-AF patients during six-year follow-up. These findings underscore the potential clinical utility of the AI model in predicting AF-related outcomes.

CONCLUSIONS

This study introduces an ECG colorization approach to enhance atrial fibrillation (AF) detection using deep learning and demographic data, improving performance compared to ECG-only methods. This method is effective in identifying high-risk and low-risk populations, providing valuable features for future AF research and clinical applications, as well as benefiting ECG-based classification studies.

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.

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

BACKGROUND

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.

OBJECTIVE

We assessed the value of including multiple PRS for cardiovascular risk factors (CV-PRS) for incident AF and VA prediction.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores

BACKGROUND AND AIMS

Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS).

METHODS

Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set.

RESULTS

A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of .78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02-4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC = .62) and PGS (AUC-ROC = .59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P < .001), with minimal changes to the AUC-ROC (.76-.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC = .77).

CONCLUSIONS

ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.

Validation, bias assessment, and optimization of the UNAFIED 2-year risk prediction model for undiagnosed atrial fibrillation using national electronic health data

Background

Prediction models for atrial fibrillation (AF) may enable earlier detection and guideline-directed treatment decisions. However, model bias may lead to inaccurate predictions and unintended consequences.

Objective

The purpose of this study was to validate, assess bias, and improve generalizability of "UNAFIED-10," a 2-year, 10-variable predictive model of undiagnosed AF in a national data set (originally developed using the Indiana Network for Patient Care regional data).

Methods

UNAFIED-10 was validated and optimized using Optum de-identified electronic health record data set. AF diagnoses were recorded in the January 2018-December 2019 period (outcome period), with January 2016-December 2017 as the baseline period. Validation cohorts (patients with AF and non-AF controls, aged ≥40 years) comprised the full imbalanced and randomly sampled balanced data sets. Model performance and bias in patient subpopulations based on sex, insurance, race, and region were evaluated.

Results

Of the 6,058,657 eligible patients (mean age 60 ± 12 years), 4.1% (n = 246,975) had their first AF diagnosis within the outcome period. The validated UNAFIED-10 model achieved a higher C-statistic (0.85 [95% confidence interval 0.85-0.86] vs 0.81 [0.80-0.81]) and sensitivity (86% vs 74%) but lower specificity (66% vs 74%) than the original UNAFIED-10 model. During retraining and optimization, the variables insurance, shock, and albumin were excluded to address bias and improve generalizability. This generated an 8-variable model (UNAFIED-8) with consistent performance.

Conclusion

UNAFIED-10, developed using regional patient data, displayed consistent performance in a large national data set. UNAFIED-8 is more parsimonious and generalizable for using advanced analytics for AF detection. Future directions include validation on additional data sets.

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.

Cardiac mechanics and the risk of atrial fibrillation in a community-based cohort of older adults

AIMS

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 AND RESULTS

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 two-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. 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).

CONCLUSION

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.

AI-Enabled CT Cardiac Chamber Volumetry Predicts Atrial Fibrillation and Stroke Comparable to MRI

Background

AI-CAC provides more actionable information than the Agatston coronary artery calcium (CAC) score. We have recently shown in the MESA (Multi-Ethnic Study of Atherosclerosis) that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) as early as 1 year.

Objectives

In this study, the authors evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting incident AF and stroke and compared them with Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF) risk score, Agatston score, and N-terminal pro b-type natriuretic peptide (NT-proBNP).

Methods

We used 15-year outcomes data from 3,552 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. CMRI LA volume was previously measured by human experts. Data on NT-proBNP, CHARGE-AF risk score, and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve.

Results

Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The area under the curve for AI-CAC versus CMRI volumetry for AF (0.802 vs 0.798) and stroke (0.762 vs 0.751) were not significantly different. AI-CAC LA significantly improved the continuous net reclassification index for prediction of 5-year AF when added to CHARGE-AF risk score (0.23), NT-proBNP (0.37, 0.37), and Agatston score (0.44) (P for all <0.0001).

Conclusions

AI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years. Further studies to investigate the clinical utility of AI-CAC for AF and stroke prediction are warranted.

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.

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.

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.

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.