Silent Atrial Fibrillation Research Articles

JCS/JHRS 2021 guideline focused update on non-pharmacotherapy of cardiac arrhythmias.

JCS/JHRS 2021 guideline focused update on non-pharmacotherapy of cardiac arrhythmias.

Silent atrial fibrillation episodes and cognitive impairment in patients with cardiac implantable devices. Protocol of the SAFE-COG ongoing observational single-center non-experimental case-control clinical study.

Silent atrial fibrillation episodes and cognitive impairment in patients with cardiac implantable devices. Protocol of the SAFE-COG ongoing observational single-center non-experimental case-control clinical study.

Association of Diabetes With Atrial Fibrillation Phenotype and Cardiac and Neurological Comorbidities: Insights From the Swiss-AF Study.

BackgroundDiabetes is a major risk factor for atrial fibrillation (AF). However, it remains unclear whether individual AF phenotype and related comorbidities differ between patients who have AF with and without diabetes. This study investigated the association of diabetes with AF phenotype and cardiac and neurological comorbidities in patients with documented AF.Methods and ResultsParticipants in the multicenter Swiss‐AF (Swiss Atrial Fibrillation) study with data on diabetes and AF phenotype were eligible. Primary outcomes were parameters of AF phenotype, including AF type, AF symptoms, and quality of life (assessed by the European Quality of Life‐5 Dimensions Questionnaire [EQ‐5D]). Secondary outcomes were cardiac (ie, history of hypertension, myocardial infarction, and heart failure) and neurological (ie, history of stroke and cognitive impairment) comorbidities. The cross‐sectional association of diabetes with these outcomes was assessed using logistic and linear regression, adjusted for age, sex, and cardiovascular risk factors. We included 2411 patients with AF (27.4% women; median age, 73.6 years). Diabetes was not associated with nonparoxysmal AF (odds ratio [OR], 1.01; 95% CI, 0.81–1.27). Patients with diabetes less often perceived AF symptoms (OR, 0.74; 95% CI, 0.59–0.92) but had worse quality of life (β=−4.54; 95% CI, −6.40 to −2.68) than those without diabetes. Patients with diabetes were more likely to have cardiac (hypertension [OR, 3.04; 95% CI, 2.19–4.22], myocardial infarction [OR, 1.55; 95% CI, 1.18–2.03], heart failure [OR, 1.99; 95% CI, 1.57–2.51]) and neurological (stroke [OR, 1.39, 95% CI, 1.03–1.87], cognitive impairment [OR, 1.75, 95% CI, 1.39–2.21]) comorbidities.ConclusionsPatients who have AF with diabetes less often perceive AF symptoms but have worse quality of life and more cardiac and neurological comorbidities than those without diabetes. This raises the question of whether patients with diabetes should be systematically screened for silent AF.RegistrationURL: https://www.clinicaltrials.gov; Unique Identifier: NCT02105844.

Predicting silent atrial fibrillation in the elderly – NOMED-AF study

Abstract Background Asymptomatic (“silent”) atrial fibrillation is common and associated with poor outcomes. It is important to determine the risk factors that predispose elderly individuals from the general population to atrial fibrillation (AF). However, population-based data for silent AF (SAF) are limited. Design First, to study the risk factors for symptomatic AF and SAF in an elderly (≥65 years) general population. Second, to develop a risk stratification model for predicting SAF. Methods Continuous ECG monitoring was performed for up to 30 days using a vest-based system in a cohort from NOMED-AF, a cross-sectional study based on a nationwide population sample. The independent risk factors for AF and SAF were determined using multiple logistic regression. ROC analysis was applied to validate developed risk stratification score. Results From the total cohort of 3014 subjects, AF was diagnosed in 680 individuals (mean age, 77.5±7.9; 50.1% men) with AF, and of these, 279 (41%) had SAF. Independent associations with an increased risk of AF were age, male gender, coronary heart disease, thyroid diseases, prior ischemic stroke or transient ischemic attack (ICS/TIA), diabetes, heart failure, chronic kidney disease (CKD), obesity (BMI>30) and NT-proBNP >125 ng/ml. Prior revascularization was negatively associated with risk of AF. The main risk factors for SAF were age, male gender, prior ICS/TIA, diabetes, heart failure, CKD and NT-proBNP >125 ng/ml. We developed a simple clinical risk scale (MR-DASH score) which had good prediction in the derivation cohort (AUC 0.726) and the validation cohort (AUC 0.730). Conclusions SAF is associated with various clinical risk factors in a population sample of individuals ≥65 years. Stratifying individuals from the general population according to their risk for SAF may be possible using the MR-DASH score, facilitating targeted screening programs of individuals with high risk of SAF Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): National Centre for Research and Development

Left atrial deformation parameters among beta-thalassemia major patients

Abstract Introduction Atrial cardiomyopathy is present in a significant proportion of beta thalassemia major (β-TM) patients, complicating their clinical condition. The diagnosis of atrial cardiomyopathy is challenging using conventional echocardiographic techniques. Purpose In our study we aimed to identify the presence of atrial cardiomyopathy by applying novel echocardiographic techniques in these patients. Methods 56 β-TM patients (mean age 39.3±9 years, 50% male sex) and 30, age and sex matched, healthy controls were examined by transthoracic echocardiography. Conventional echocardiographic parameters were estimated alongside with deformation indices (left atrial strain at reservoir (LASr), conduit (LAScd) and contraction (LASct) phase respectively as well as left ventricular global longitudinal strain (GLS)). T2* was calculated by cardiac magnetic resonance imaging in β-TM patients. Results LAVI, E/e' ratio, GLS and left atrial deformation parameters differed between patients and controls. In patient group, left atrial deformation indices were correlated with LAVI, E/e' ratio, GLS and T2* (Table 1). GLS was also correlated with LAVI, but not with T2* or E/e' ratio. T2* was correlated only with left atrial deformation indices. Patient with prior episodes of atrial fibrillation were older, had increased E/e' and LAVI and impaired left atrial deformation indices but did not differ in terms of GLS or T2* (Figure 1). Patients with iron overload differed only in terms of left atrial deformation parameters. Conclusions Atrial deformation indices could be of clinical use in the early detection of atrial cardiomyopathy. Impaired left atrial strain may be associated with silent atrial fibrillation and be indicative of myocardial iron overload. Funding Acknowledgement Type of funding sources: None. Correlation tableScatter plot of T2* and LASr

Diabetes is associated with atrial fibrillation phenotype, cardiac and neurological comorbidities: insights from the Swiss-AF study

Abstract Background Diabetes mellitus is a major risk factor for atrial fibrillation (AF). However, it remains unclear whether individual AF phenotype and related comorbidities differ between AF patients with and without diabetes. Purpose To investigate the association of diabetes with AF phenotype, cardiac and neurological comorbidities in patients with documented AF. Methods Participants of the multicenter Swiss-AF study with available data on diabetes and AF phenotype were eligible. The primary outcomes were parameters of AF phenotype, including AF type (paroxysmal vs non-paroxysmal), AF symptoms (yes vs no), and quality of life (assessed by EQ-5D score). The secondary outcomes were cardiac (ie, history of hypertension, myocardial infarction, heart failure) and neurological comorbidities (ie, history of stroke, cognitive impairment). The cross-sectional association of diabetes with these outcomes was assessed using logistic and linear regression. Results were adjusted for age, sex, and cardiovascular risk factors. Results We included 2411 AF patients (27.4% women; median age, 73.6 years). Diabetes was not associated with non-paroxysmal AF (odds ratio [OR]=1.01; 95% confidence interval [CI]=0.81 to 1.27). Patients with diabetes less often perceived AF symptoms (OR=0.73; CI=0.59 to 0.91), but had worse quality of life (predicted mean difference in EQ-5D score: β=−4.54; CI=−6.40 to −2.68) than those without diabetes. Patients with diabetes were more likely to have cardiac comorbidities [history of hypertension (OR=3.04; CI=2.19 to 4.22), myocardial infarction (OR=1.55; CI=1.18 to 2.03), heart failure (OR=1.99; CI=1.57 to 2.51)] and neurological comorbidities [history of stroke (OR=1.39; CI=1.03 to 1.87), cognitive impairment (OR=1.75; CI=1.39 to 2.21)]. Conclusions In the Swiss-AF cohort population, patients with diabetes less often perceived AF symptoms, but had worse quality of life, more cardiac and neurological comorbidities than those without diabetes. These findings have significant clinical implications. The reduced perception of AF symptoms in patients with diabetes might result in a delayed AF diagnosis and consequently more adverse events, especially cardioembolic stroke. This raises the question whether patients with diabetes should be systematically screened for silent AF. Moreover, patients with concomitant AF and diabetes have increased likelihood of comorbidities and therefore deserve more attentive care. Funding Acknowledgement Type of funding sources: None.

Atrial fibrillation self-screening, management and guideline recommended therapy (AF SELF SMART): improving AF screening in general practice by utilising self-screening stations in GP waiting rooms

Abstract Background Opportunistic screening for silent atrial fibrillation (AF) is recommended to reduce stroke, but screening rates are sub-optimal in Australian general practice (<15%), with practitioners reporting time constraints being the main barrier. Previous AF screening interventions in general practice have not been able to exceed screening rates of ∼34% eligible patients which is sub-optimal for stroke prevention. Purpose To increase the proportion of patients screened for AF in general practice by implementing AF self-screening stations in waiting rooms. Methods We developed and tested an AF self-screening station using a single-lead ECG and app with automated ECG analysis. We also developed customised software which fully integrates the self-screening station with GP medical software and the practice workflow (Figure 1). The system: 1) automatically identifies eligible patients (aged ≥65 years, no AF diagnosis) from the practice appointment diary, and sends an SMS regarding self-screening prior to their appointment; 2) automatically creates individualised patient QR codes, which provided to the patient by the receptionist; 3) the patient then scans QR code and self-screens at station in waiting room; 4) the ECG and result are automatically imported into patient record before the GP consultation. Results Three general practices in New South Wales, Australia, were recruited. Each practice participated for approximately 3-months (between August 2020 and March 2021). During this period 825 patients completed AF self-screening, mean age 74.2 years and 45% male. When expressed as a proportion of the actual eligible patients attending the practice during the study period, the mean proportion screened in practices was 52% (range 38–65%). The device algorithm indicated “Possible AF” in 37/825 patients (4.5%); mean age 77.2 years 43% male. Conclusion An AF self-screening station placed in GP waiting rooms that is integrated with the practice software and workflow may achieve higher screening rates for AF in general practice than standard practice, and therefore contribute to greater reductions in stroke. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Investigator-lead grant from Bristol Myers Squibb and Pfizer

Left Atrium Assessment by Speckle Tracking Echocardiography in Cryptogenic Stroke: Seeking Silent Atrial Fibrillation.

Silent atrial fibrillation (AF) may be the cause of some cryptogenic strokes (CrS). The aim of the study was to analyse atrial size and function by speckle tracking echocardiography in CrS patients to detect atrial disease. Patients admitted to the hospital due to CrS were included prospectively. Echocardiogram analysis included left atrial ejection fraction (LAEF) and atrial strain. Insertable cardiac monitor was implanted, and AF was defined as an episode of ≥1 min in the first year after stroke. Left atrial enlargement was defined as indexed volume > 34 mL/m2. Seventy-five consecutive patients were included, aged 76 ± 9 years (arterial hypertension 75%). AF was diagnosed in 49% of cases. The AF group had higher atrial volume and worse atrial function: peak atrial longitudinal strain (PALs) 19.6 ± 5.7% vs. 29.5 ± 7.2%, peak atrial contraction strain (PACs) 8.9 ± 3.9% vs. 16.5 ± 6%, LAEF 46.8 ± 11.5% vs. 60.6 ± 5.2%; p < 0.001. AF was diagnosed in 20 of 53 patients with non-enlarged atrium, and in 18 of them, atrial dysfunction was present. The multivariate logistic regression analysis demonstrated an independent association between detection of AF and atrial volume, LAEF, and strain. Cut-off values were obtained: LAEF < 55%, PALs < 21.4%, and PACs < 12.9%. In conclusion, speckle tracking echocardiography in CrS patients improves silent atrial disease diagnosis, with or without atrial enlargement.

Optimizing prognosis in atrial fibrillation: A call to action in Portugal.

Atrial fibrillation (AF), the most common arrhythmia in the adult population worldwide, represents a significant burden in terms of cardiovascular mortality and morbidity and has repercussions on health economics. Oral anticoagulation (OAC) is key to stroke prevention in AF and, in recent years, results from landmark clinical trials of non-vitamin K oral anticoagulants (NOAC) have triggered a paradigm shift in thrombocardiology. Despite these advances, there is still a significant residual vascular risk associated with silent AF, bleeding, premature sudden death and heart failure. The authors review AF epidemiologic data, the importance of new tools for early AF detection, the current role of catheter ablation for rhythm control in AF, the state-of-the-art in periprocedural OAC, the optimal management of major bleeding, the causes of residual premature death and future strategies for improvements in AF prognosis.

The catch in atrial fibrillation detection: don't forget to treat

In The Lancet Healthy Longevity Wei Zhang and colleagues1 present a randomised controlled study on atrial fibrillation screening strategies in Shanghai, China, over 2 years, and thereby provide yet more clarification on how to best screen for atrial fibrillation. Knowledge on screening for silent (asymptomatic) atrial fibrillation becomes more consolidated with increasing evidence; but the best screening setting has yet to be determined (panel): which age or (risk) population to target, which device to use, and which time intervals and time span are optimal?

Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source

ObjectivesEmbolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was associated with the results of prolonged ambulatory cardiac rhythm monitoring. Materials and methodsWe reviewed consecutive patients admitted with acute ischemic stroke to a comprehensive stroke center between January 2018 and August 2019 and employed the TOAST classification to categorize the mechanisms of ischemia. Use and results of ambulatory cardiac rhythm monitoring after discharge were gathered. We ran the AI-ECG model to obtain AF probabilities from all ECGs acquired during the hospitalization and compared those probabilities in patients with ESUS versus those with known stroke causes (apart from AF), and between patients with and without AF detected by ambulatory cardiac rhythm monitoring. ResultsThe study cohort had 930 patients, including 263 patients (28.3%) with known AF or AF diagnosed during the index hospitalization and 265 cases (28.5%) categorized as ESUS. Ambulatory cardiac rhythm monitoring was performed in 226 (85.3%) patients with ESUS. AF probability by AI-ECG was not associated with ESUS. However, among patients with ESUS, the probability of AF by AI-ECG was associated with a higher likelihood of AF detection by ambulatory monitoring (P = 0.004). A probability of AF by AI-ECG greater than 0.20 was associated with AF detection by ambulatory cardiac rhythm monitoring with an OR of 5.47 (95% CI 1.51–22.51). ConclusionsAI-ECG may help guide the use of prolonged ambulatory cardiac rhythm monitoring in patients with ESUS to identify those who might benefit from anticoagulation.

Supraventricular Runs in 7-Day Holter Monitoring Are Related to Increased Incidence of Atrial Fibrillation in a 3-Year Follow-Up of Cryptogenic Stroke Patients Free from Arrhythmia in a 24 h-Holter.

Introduction: Silent atrial fibrillation (AF) is a common cause of cryptogenic ischemic stroke (CIS). The 24-h-Holter is insufficient to reveal an occult arrhythmic cause of stroke and the strategy to select the patients for long-term monitoring is missing. Objectives: The aim of the study was to evaluate 7-day-Holter monitoring to identify cases with the arrhythmic cause of stroke in CIS patients in whom 24-h-Holter was free from arrhythmia, and to assess the relation between supraventricular (SV) runs in baseline Holter and the incidence of AF in a 3-year follow-up period. Methods: 78 patients (aged 60 ± 9 years, 45 males) with CIS and no arrhythmic findings in 24-h-Holter were enrolled. All patients had 7-day-Holter monitoring after stroke and were followed up for 36 months, and then 7-day Holter was repeated. We assessed SV runs (≥5 QRS) in the initial 7-day Holter and analyzed the relation of the findings with clinical characteristics of novel AF episodes revealed early after stroke and during a 3-year follow-up. Results: Baseline 7-day-Holter revealed SV runs in 36% of patients and AF in 9% of cases. During a 3-year follow-up, 8 additional cases were confirmed, both in standard care and in repeated Holter (a total of 19% of AF cases). There was no difference with regard to CHADS2VASc score (3.6 ± 1.1 vs. 3.4 ± 1.5; p = NS) and left atrium parameters between patients with SV runs and the non-arrhythmic group. Patients with SV runs had a higher incidence of AF both after stroke and in a 3-year follow-up (46% vs. 4%, RR 11.6, p < 0.001). In 8 cases, patent foramen ovale was detected during follow-up. Conclusions: A strategy of baseline 7-day-Holter monitoring after stroke allows for disclosing SV runs in every third case and AF in 9% of stroke survivors. Patients with SV runs have a higher incidence of AF (RR 11.6, p < 0.001) and should be considered for extended continuous ECG monitoring.

Relationship between diabetes mellitus and atrial fibrillation prevalence in the Polish population: a report from the Non-invasive Monitoring for Early Detection of Atrial Fibrillation (NOMED-AF) prospective cross-sectional observational study

BackgroundThe global burden of atrial fibrillation (AF) and diabetes mellitus (DM) is constantly rising, leading to an increasing healthcare burden of stroke. AF often remains undiagnosed due to the occurrence in an asymptomatic, silent form, i.e., silent AF (SAF). The study aims to evaluate the relationships between DM and AF prevalence using a mobile long-term continuous ECG telemonitoring vest in a representative Polish and European population ≥ 65 years for detection of AF, symptomatic or silent.MethodsA representative sample of 3014 participants from the cross-sectional NOMED-AF study was enrolled in the analyses (mean age 77.5, 49.1% female): 881 (29.2%) were diagnosed with DM. AF was screened using a telemonitoring vest for a mean of 21.9 ± 9.1days.ResultsOverall, AF was reported in 680 (22.6%) of the whole study population. AF prevalence was higher among subjects with concomitant DM (DM+) versus those without DM (DM−) [25%, 95% CI 22.5-27.8% vs 17%; 95% CI 15.4–18.5% respectively, p < 0.001]. DM patients were commonly associated with SAF [9%; 95% CI 7.9–11.4 vs 7%; 95% CI 5.6–7.5 respectively, p < 0.001], and persistent/permanent AF [12.2%; 95% CI 10.3–14.3 vs 6.9%; 95% CI 5.9–8.1 respectively, p < 0.001] compared to subjects without DM. The prolonged screening was associated with a higher percentage of newly established AF diagnosis in DM+ vs DM− patients (5% vs 4.5% respectively, p < 0.001). In addition to shared risk factors, DM+ subjects were associated with different AF and SAF independent risk factors compared to DM− individuals, including thyroid disease, peripheral/systemic thromboembolism, hypertension, physical activity and prior percutaneous coronary intervention/coronary artery bypass graft surgery.ConclusionsAF affects 1 out of 4 subjects with concomitant DM. The higher prevalence of AF and SAF among DM subjects than those without DM highlights the necessity of active AF screening specific AF risk factors assessment amongst the diabetic population.Trial registration: NCT03243474

Predicting Silent Atrial Fibrillation in the Elderly: A Report from the NOMED-AF Cross-Sectional Study.

Background: Silent atrial fibrillation (SAF) is common and is associated with poor outcomes. Aims: to study the risk factors for AF and SAF in the elderly (≥65 years) general population and to develop a risk stratification model for predicting SAF. Methods: Continuous ECG monitoring was performed for up to 30 days using a vest-based system in a cohort from NOMED-AF, a cross-sectional study based on a nationwide population sample. The independent risk factors for AF and SAF were determined using multiple logistic regression. ROC analysis was applied to validate the developed risk stratification score. Results: From the total cohort of 3014 subjects, AF was diagnosed in 680 individuals (mean age, 77.5 ± 7.9; 50.1% men) with AF, and, of these, 41% had SAF. Independent associations with an increased risk of AF were age, male gender, coronary heart disease, thyroid diseases, prior ischemic stroke or transient ischemic attack (ICS/TIA), diabetes, heart failure, chronic kidney disease (CKD), obesity, and NT-proBNP >125 ng/mL. The risk factors for SAF were age, male gender, ICS/TIA, diabetes, heart failure, CKD, and NT-proBNP >125 ng/mL. We developed a clinical risk scale (MR-DASH score) that achieved a good level of prediction in the derivation cohort (AUC 0.726) and the validation cohort (AUC 0.730). Conclusions: SAF is associated with various clinical risk factors in a population sample of individuals ≥65 years. Stratifying individuals from the general population according to their risk for SAF may be possible using the MR-DASH score, facilitating targeted screening programs of individuals with a high risk of SAF.

Prevalence of silent atrial fibrillation in high-risk patients - preliminary results from a European three-country handheld ECG-screening study

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Helsefonden Lilly and Herbert Hansens Foundation Introduction Patients with atrial fibrillation (AF) should in most cases be offered prophylactic anticoagulation treatment to prevent a stroke. However, the arrhythmia can appear without symptoms, so-called silent AF. Even without symptoms AF constitutes a risk for stroke. Purpose To screen high-risk patients with diabetes type 2 (DMII) or heart failure (CHF) for silent AF. Methods We included patients &amp;gt; 64 years with either DMII or CHF from out-patient clinics and local health centers. Exclusion criteria were known AF, anticoagulation treatment, recent stroke, or an implanted pacemaker or ICD. Patients were recruited from a total of eleven study centers in three countries. All underwent 14-days of intermittent ECG screening with a handheld ECG recording four times each day; the recordings were digitally stored. AF was diagnosed in cases of irregular heart rhythm and absence of P waves on at least one recording (thirty seconds) or on at least two recordings for a minimum of ten seconds. Results In total, 813 patients were included, 541 of these with DMII. The mean age was 73,4 years ± 5,8 SD, 40,7% of the patients were female. In the DMII group thirteen patients (2.4%) were diagnosed with silent AF and offered anticoagulation. In the CHF group six (2.2%) patients had diagnosed silent AF on the handheld ECG. The prevalence of AF increased with increasing age, see Table 1. Thus, in the youngest group AF was diagnosed in 1.3% of the patients compared to 3.9 % in the age group 75 years or older. Conclusions Screening for silent AF in high-risk patients with DMII or CHF seems worthwhile, especially in patients 75 years or older. Abstract Figure. ECG with atrial fibrillation

Identification of novel biomarkers for predicting atrial fibrillation outcomes in patients with cardiovascular risk factors

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): CATCH ME European Commission H2020 grant (no. 633196) Background Several novel blood biomarkers were recently found to reflect underlying pathophysiology implicated in atrial fibrillation (AF). These biomarkers could be used for stratifying patients at risk of developing AF or AF-related adverse events. Purpose We combined 9 clinical risk factors and 12 biomarkers to model composite outcomes of 1) incident AF, hospitalisation for arrhythmias, and cardiovascular death in sinus rhythm patients at risk of AF, and 2) recurrent AF, hospitalisation for arrhythmias, and cardiovascular death in patients with AF. Methods 1455 patients presenting acutely to hospital with either diagnosed AF (n = 648) or sinus rhythm and ≥2 CHA2DS2-VASc risk factors (n = 807, silent AF ruled out by 7-day ECG monitoring) were followed up for two years. Outcomes were collected from linked hospital episode statistics (HES) and Office of National Statistics (ONS) data from NHS Digital. We univariately evaluated 12 cardiovascular biomarkers quantified from EDTA plasma collected at baseline (ANG2, BMP10, CA125, CRP, ESM1, FABP3, FGF23, GDF15, IGFBP7, IL6, NTproBNP, Troponin T). Two predictive models combining clinical characteristics and biomarkers were developed for each patient group, using Cox regression with backward elimination and considering non-cardiovascular death as a competing risk. Results In sinus rhythm patients (n = 117/807 with outcome), elevated BMP10, ANG2, CA125, IGFBP7, NTproBNP univariately predicted the composite outcome (adjusted for age, sex, body mass index (BMI), eGFR, heart failure, stroke/TIA, hypertension, diabetes, coronary artery disease – see Figure part A). In the combined model, age, prior stroke/TIA, coronary artery disease, ANG2, IGFBP7 and NTproBNP predicted the outcome (C-statistic [95% confidence interval (CI)] 0.733 [0.683, 0.784]). In patients with AF (n = 193/648 with outcome), elevated BMP10, ANG2, CA125, troponin T, GDF15, IGFBP7, NTproBNP univariately predicted the composite outcome (adjusted for same variables as above – see Figure part B). In the combined model, high BMI, low eGFR, hypertension, IGFBP7, NTproBNP and troponin T were predictive of the composite outcome (C-statistic [95% CI]: 0.643 [0.596, 0.689]). Conclusion Combinations of clinical risk factors and biomarkers were predictive of two-year AF-related adverse events in sinus rhythm patients at risk of AF and in patients with AF. These markers could be used to identify patients for more intensive follow-up or therapy. IGFBP7 and NTproBNP were present in both models, implicating pathways involved with cardiac overload, inflammation, and oxidative stress. These findings call for external validation of these markers and prospective evaluation in at-risk populations. Abstract Figure. Biomarkers predicting 2-year AF outcomes

Screening for Atrial Fibrillation in American Indian Adults in a Tribal Primary Care Clinic.

BackgroundAmerican Indian adults have a higher risk of atrial fibrillation (AF) compared with other racial groups. We implemented opportunistic screening to detect silent AF in American Indian adults attending a tribal health system using a mobile, single‐lead ECG device.Methods and ResultsAmerican Indian patients aged ≥50 years followed in a tribal primary care clinic with no history of AF underwent a 30‐second ECG. A cardiologist overread all tracings to confirm the diagnosis of AF. After AF was confirmed, patients were referred to their primary care physician for initiation of anticoagulation. Patients seen over the same time period, who were not undergoing screening, served as controls. A total of 1019 patients received AF screening (mean age, 61.5±8.9 years, 62% women). Age and sex distribution of those screened was similar to the overall clinic population. New AF was diagnosed in 15 of 1019 (1.5%) patients screened versus 4 of 1267 (0.3%) patients who were not screened (mean difference, 1.2%; 95% CI, 0.3%–2.2%, P=0.002). Eight of 15 with new screen‐detected AF were aged <65 years. Those with screen‐detected AF were slightly older and had a higher CHA2DS2‐VASc score than those without AF. Fourteen of 15 patients diagnosed with new AF had a CHA2DS2‐VASc score ≥1 and initiated anticoagulation.ConclusionsOpportunistic, mobile single‐lead ECG screening for AF is feasible in tribal clinics, and detects more AF than usual care, leading to appropriate initiation of anticoagulation. AF develops at a younger age in American Indian adults who would likely benefit from earlier AF screening.RegistrationURL: https://www.clinicaltrials.gov; Unique identifier: NCT03740477.

Atrial Fibrillation and Ischemic Stroke: A Clinical Review.

Atrial fibrillation (AF) is an important risk factor for ischemic stroke resulting in a fivefold increased stroke risk and a twofold increased mortality. Our understanding of stroke mechanisms in AF has evolved since the concept of atrial cardiopathy was introduced as an underlying pathological change, with both AF and thromboembolism being common manifestations and outcomes. Despite the strong association with stroke, there is no evidence that screening for AF in asymptomatic patients improves clinical outcomes; however, there is strong evidence that patients with embolic stroke of undetermined source may require long-term monitoring to detect silent or paroxysmal AF. Stroke prevention in patients at risk, assessed by the CHA2DS2-VASc score, was traditionally achieved with warfarin; however, direct oral anticoagulants have solidified their role as safe and effective alternatives. Additionally, left atrial appendage exclusion has emerged as a viable option in patients intolerant of anticoagulation. When patients with AF have an acute stroke, the timing of initiation or resumption of anticoagulation for secondary stroke prevention has to be balanced against the risk of hemorrhagic conversion. Multiple randomized clinical trials are currently underway to determine the best timing for administration of anticoagulants following acute ischemic stroke.

Abstract P708: Artificial Intelligence Enabled-Electrocardiography for the Detection of Cerebral Infarcts in Patients With Atrial Fibrillation

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score &gt; 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score &gt; 0.5 was associated with infarcts ( p &lt; 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

Cardiac events monitoring

Cardiac events recorders have been developed in order to record the heart rhythm during symptoms such as palpitations or presyncope, to first make a diagnosis, and subsequently drive the treatment strategy. In other circumstances, they can be also used in asymptomatic patients (to record silent atrial fibrillation for instance). Because they are non-invasive, potentially cost-saving and relatively easy to use, the external rhythm recording devices have shown some great advances in the last years, spreading from photoplethysmographic technique to real ECG reconstruction. Technological advances in the field of microelectronics, as well as in the field of data transmission have contributed to their widespread use in cardiology. The trend for miniaturization was also expanded to the implantable recorders. This paper will review will review advantages and limitations of the different existing available well-established recording devices, as well as the last technological developments in terms of ECG recordings.