Clinical predictors of risk for atrial fibrillation: implications for diagnosis and monitoring

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

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

Exploring clinical indicator variations in stroke patients with multiple risk factors: focus on hypertension and inflammatory reactions

BACKGROUND

Stroke stands as the second leading cause of death worldwide. Currently, extensive research has been conducted on stroke risk factors. However, when stroke patients contend with multiple risk factors, the impact on clinical indicators remains uncertain.

OBJECTIVES

This study seeks to investigate potential significant variations among distinct ranges of clinical indicators in instances where stroke patients experience multiple risk factors and various ischemic stroke subtypes.

MATERIAL AND METHODS

The research encompassed 440 stroke patients admitted to the First People's Hospital of Wenling City, Zhejiang Province, China. These patients were classified based on the type and quantity of risk factors and subtypes of ischemic stroke they presented. The χ2 test was employed to assess the relationship between the risk of comorbid diseases and clinical indicators in stroke patients.

RESULTS

The results of our study have underscored a significant correlation between various comorbid risk factors in stroke patients and the patients' age (P < 0.010). Furthermore, we observed noteworthy disparities in the plasma levels of IL-2, IL-4, IL-6, IL-10, TNF-α, and INF-γ between patients devoid of risk factors and those presenting with comorbid risk factors associated with stroke. Significant differences in INF-γ were observed between the two subtypes of ischemic stroke, namely lacunar infarction and cardioembolic stroke.

CONCLUSION

Age is correlated with an elevated risk of stroke. Individuals exhibiting multiple stroke risk factors and diverse ischemic stroke subtypes commonly present with abnormal lipid levels and imbalances in Th1/Th2 cytokines. These factors significantly contribute to the onset and progression of stroke. Furthermore, inflammatory responses, particularly those induced by atherosclerosis, play a pivotal role in the genesis of stroke and exert a substantial influence on its prognosis.

Dialysis Modality, Transplant Characteristics, and Incident Atrial Fibrillation After Kidney Transplant: An Observational Study Using USRDS Data

Rationale & Objective

Atrial fibrillation is the most common arrhythmia and is increasing in prevalence. The prevalence of atrial fibrillation is high among patients receiving dialysis, affecting ∼21.3% of the patients receiving hemodialysis and 15.5% of those receiving peritoneal dialysis. The association of previous dialysis modality with incident atrial fibrillation in patients after receiving their first kidney transplant has not been studied.

Study Design

We used the United States Renal Data System to retrospectively identify adult, Medicare-insured patients who received their first kidney transplant between January 1, 2005, and September 30, 2012 and who had not previously been diagnosed with atrial fibrillation.

Setting & Participants

The study included 43,621 patients who were aged 18 years older when receiving a first kidney transplant between January 1, 2005, and September 30, 2012 and whose primary payer was Medicare (parts A and B) at the time of transplantation and the 6 months preceding it.

Exposure

Dialysis modality used before transplant.

Outcome

Time to incidence of atrial fibrillation up to 3 years posttransplant.

Analytical Approach

Multivariable Cox regression was used to estimate HRs.

Results

Of 43,621 patients, 84.9% received hemodialysis and 15.1% received peritoneal dialysis before transplant. The mean ± SD age was 51 ± 13.6 years; 60.8% were male, 55.6% White, and 35.8% Black race. The mean dialysis vintage was 4.3 ± 2.8 years. Newly diagnosed atrial fibrillation after kidney transplant occurred in 286 patients (during 15,363 person-years) who had received peritoneal dialysis and in 2,315 patients (during 83,536 person-years) who had received hemodialysis. After multivariable adjustment, atrial fibrillation was 20% (95% CI, 4%-38%) more likely in those who had been receiving hemodialysis versus peritoneal dialysis, regardless of whether death was considered a competing risk or a censoring event. Each year of pretransplant dialysis vintage increased the risk of posttransplant atrial fibrillation by 6% (95% CI, 3%-9%).

Limitations

Residual confounding; data from billing claims does not specify the duration of atrial fibrillation or whether it is valvular.

Conclusions

Pretransplant hemodialysis, as compared with peritoneal dialysis, was associated with higher risk of newly diagnosed atrial fibrillation after a first kidney transplant.

Plain-Language Summary

New-onset atrial fibrillation (AF) occurs in 7% of kidney transplant recipients in the first 3 years posttransplantation. We conducted this study to determine whether pretransplant dialysis modality was associated with posttransplant AF. We identified 43,621 patients; 84.9% used hemodialysis and 15.1% used peritoneal dialysis pretransplant. Multivariable Cox regression was used to estimate hazard ratios. We found that patients receiving hemodialysis pretransplant were at 20% increased risk of developing posttransplant AF as compared with patients receiving peritoneal dialysis. As our understanding of transplant-specific risk factors for AF increases, we may be able to better risk-stratify transplant patients and develop monitoring and management strategies that can improve outcomes.

Probability of Cardioembolic vs. Atherothrombotic Pathogenesis of Cryptogenic Strokes in Older Patients

Some clinical, laboratory, ECG, and echocardiographic parameters could provide useful indications to assess the probability of cardioembolism or atherothrombosis in cryptogenic strokes. We retrospectively examined 290 patients with ischemic stroke aged ≥60 years, divided into 3 groups: strokes originating from large artery atherothrombosis (n = 92), cardioembolic strokes caused by paroxysmal atrial fibrillation (n = 88) and cryptogenic strokes (n = 110). In addition to echocardiographic and routine clinical-laboratory variables, neutrophil:lymphocyte ratio, red blood cell distribution width, mean platelet volume, P wave and PR interval duration and biphasic inferior P waves, both on admission and after 7 to 10 days, were also considered. By multiple logistic regression, cardioembolic strokes were compared with large artery atherothrombosis strokes, and beta coefficients were rounded to produce a scoring system. Late PR interval ≥188 ms, left atrium ≥4 cm, left ventricular end-diastolic volume <65 ml, and posterior circulation syndrome were associated with paroxysmal atrial fibrillation (positive scores). In contrast, male gender, hypercholesterolemia, and initial platelet count ≥290 × 10⁹/L were associated with atherothrombosis of large arteries (negative scores). The algebraic sum of these scores produced values indicative of cardioembolism if >0 (positive predictive value 89.1%), or of atherothrombosis, if ≤0 (positive predictive value 72.5%). The area under the receiver operating characteristic curve was 0.85. Among cryptogenic strokes, 41.5% had a score >0 (probable atrial fibrillation) and 58.5% had a score ≤0 (possible atherothrombosis). In conclusion, a scoring system based on electrocardiogram, laboratory, clinical and echocardiographic parameters can provide useful guidance for further investigations and secondary prevention in older patients with cryptogenic stroke.

Long-Term Outcomes after Stroke in Patients with Atrial Fibrillation: A Single Center Study

Atrial fibrillation (AF) is known to be a significant risk factor for poor prognosis after stroke. In this study, we compared differences in long-term outcomes after ischemic stroke among patients with AF and sinus rhythm (SR). We identified patients admitted to the reference Neurology Center between 1 January 2013 and 30 April 2015, inclusive, with acute ischemic stroke. Of the 1959 surviving patients, 892 were enrolled and followed for five years or until death. We analyzed the risk of stroke recurrence and death between patients with AF and SR at 1, 3, and 5 years after stroke. The rates of death and stroke recurrence were estimated using Kaplan-Meier analysis and multivariate Cox regression. During follow-up, 17.8% of patients died and 14.6% had recurrent stroke. The mortality in the AF group increased relative to the SR group with subsequent years. The risk of death was statistically higher in the AF than SR group at 1 year after stroke (13.5 vs. 7%, p = 0.004). After adjusting for age, stroke severity, and comorbidities, there was also no significant effect of AF on mortality in the first year after stroke (OR = 1.59, p = 0.247). There were no significant differences between the groups in stroke recurrence during follow-up. The results of our study showed that post-stroke patients with AF have a more severe prognosis, although AF itself does not have an independent negative effect on long-term outcomes after stroke. Long-term survival after stroke in patients with AF was strongly associated with age, stroke severity, and heart failure. The impact of other factors on prognosis after stroke in patients with AF should be considered.

Atrial fibrillation and stroke

INTRODUCTION

Stroke is one of the leading causes of mortality and morbidity globally. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. It is set to reach epidemic proportions. AF is associated with a five-fold increase in risk of stroke. Strokes caused by AF more often are fatal or result in severe disability. Even though the incidence of stroke has been significantly reduced by oral anticoagulation, AF is thought to account for a significant proportion of cryptogenic strokes where no etiology is identified.

AREAS COVERED

This article reviews the literature related to AF and stroke, pathophysiological insights, diagnosis of AF in stroke patients, and its management (Graphical Abstract).

EXPERT OPINION

The pathophysiology of thrombogenesis that links AF and stroke is not well understood and is an area of active research to identify new therapeutic targets to prevent AF and stroke. As the nature of AF and stroke is multifaceted, an integrated care approach to managing AF and stroke is increasingly essential.

Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, has a large impact on quality of life and is associated with increased risk of hospitalization, morbidity, and mortality. Over the past two decades advances regarding the clinical epidemiology and management of AF have been established. Moreover, sex differences in the prevalence, incidence, prediction, pathophysiology, and prognosis of AF have been identified. Nevertheless, AF remains to be a complex and heterogeneous disorder and a comprehensive sex- and gender-specific approach to predict new-onset AF is lacking. The exponential growth in various sources of big data such as electrocardiograms, electronic health records, and wearable devices, carries the potential to improve AF risk prediction. Leveraging these big data sources by artificial intelligence (AI)-enabled approaches, in particular in a sex- and gender-specific manner, could lead to substantial advancements in AF prediction and ultimately prevention. We highlight the current status, premise, and potential of big data to improve sex- and gender-specific prediction of new-onset AF.

Early Detection of Atrial Fibrillation in Community Pharmacies-CRIFAFARMA Study

BACKGROUND

Atrial fibrillation (AF) is the most common arrhythmia to appear in clinical practice. People with AF have 5 times the risk of stroke compared to the general population.

OBJECTIVE

This study aimed to determine the prevalence of AF in people over the age of 50 without known AF, who presented to a community pharmacy to check their cardiovascular risk factors, to identify risk factors associated with AF, and to assess the risk of stroke in people who screened positive for AF.

METHODS

A multicenter observational descriptive study of a screening program took place from May to December 2016. A blood pressure monitor (Microlife Watch BP Home) was used to screen for AF, and the CHA2DS2-VASc questionnaire was used to assess stroke risk.

RESULTS

The study included 452 adults over the age of 50. The CRIFAFARMA study detected a prevalence of AF of 9.1%. Risk factors for AF were: age of 75 years or older (P = .024), lack of physical activity (P = .043), diabetes (P < .001), dyslipidemia (P = .003), and history of cardiovascular disease (P = .003). Diabetes (OR 2.79, P = .005) and dyslipidemia (OR 2.16, P = .031) had a combined explanatory capacity in the multivariable logistic regression model adjusted for age. 85% were at high risk of stroke according to the CHA2DS2-VASc scale.

CONCLUSIONS

AF was detected in more than 9% of the included population. Factors associated with AF were advanced age, lack of physical activity, diabetes, dyslipidemia, and history of cardiovascular disease, with diabetes and dyslipidemia standing out as the factors with independent explanatory capacity.

Predictive model for atrial fibrillation in hypertensive diabetic patients

BACKGROUND

Several scores to identify patients at high risk of suffering atrial fibrillation have been developed. Their applicability in hypertensive diabetic patients, however, remains uncertain. Our aim is to develop and validate a diagnostic predictive model to calculate the risk of developing atrial fibrillation at five years in a hypertensive diabetic population.

METHODS

The derivation cohort consisted of patients with both hypertension and diabetes attended in any of the 52 primary healthcare centres of Barcelona; the validation cohort came from the 11 primary healthcare centres of Terres de l'Ebre (Catalonia South) from January 2013 to December 2017. Multivariable Cox regression identified clinical risk factors associated with the development of atrial fibrillation. The overall performance, discrimination and calibration of the model were carried out.

RESULTS

The derivation data set comprised 54 575 patients. The atrial fibrillation rate incidence was 15.3 per 1000 person/year. A 5-year predictive model included age, male gender, overweight, heart failure, valvular heart disease, peripheral vascular disease, chronic kidney disease, number of antihypertensive drugs, systolic and diastolic blood pressure, heart rate, thromboembolism, stroke and previous history of myocardial infarction. The discrimination of the model was good (c-index = 0.692; 95% confidence interval, 0.684-0.700), and calibration was adequate. In the validation cohort, the discrimination was lower (c-index = 0.670).

CONCLUSIONS

The model accurately predicts future atrial fibrillation in a population with both diabetes and hypertension. Early detection allows the prevention of possible complications arising from this disease.

Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

Aims

Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts.

Methods and results

We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA₂DS₂-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window.

Conclusion

CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.

Utility of risk prediction models to detect atrial fibrillation in screened participants

AIMS

Atrial fibrillation (AF) is associated with higher risk of stroke. While the prevalence of AF is low in the general population, risk prediction models might identify individuals for selective screening of AF. We aimed to systematically identify and compare the utility of established models to predict prevalent AF.

METHODS AND RESULTS

Systematic search of PubMed and EMBASE for risk prediction models for AF. We adapted established risk prediction models and assessed their predictive performance using data from 2.5M individuals who attended vascular screening clinics in the USA and the UK and in the subset of 1.2M individuals with CHA2DS2-VASc ≥2. We assessed discrimination using area under the receiver operating characteristic (AUROC) curves and agreement between observed and predicted cases using calibration plots. After screening 6959 studies, 14 risk prediction models were identified. In our cohort, 10 464 (0.41%) participants had AF. For discrimination, six prediction model had AUROC curves of 0.70 or above in all individuals and those with CHA2DS2-VASc ≥2. In these models, calibration plots showed very good concordance between predicted and observed risks of AF. The two models with the highest observed prevalence in the highest decile of predicted risk, CHARGE-AF and MHS, showed an observed prevalence of AF of 1.6% with a number needed to screen of 63. Selective screening of the 10% highest risk identified 39% of cases with AF.

CONCLUSION

Prediction models can reliably identify individuals at high risk of AF. The best performing models showed an almost fourfold higher prevalence of AF by selective screening of individuals in the highest decile of risk compared with systematic screening of all cases.

REGISTRATION

This systematic review was registered (PROSPERO CRD42019123847).

Atrial Fibrillation in Patients Undergoing Allogeneic Hematopoietic Cell Transplantation

To examine the incidence and risk factors for de novo atrial fibrillation (AF) after allogeneic hematopoietic cell transplantation (HCT) and to describe the impact of AF on HCT-related outcomes.

Relationship between lipoproteins, thrombosis and atrial fibrillation

The prothrombotic state in atrial fibrillation (AF) occurs as a result of multifaceted interactions, known as Virchow’s triad of hypercoagulability, structural abnormalities, and blood stasis. More recently, there is emerging evidence that lipoproteins are implicated in this process, beyond their traditional role in atherosclerosis. In this review, we provide an overview of the various lipoproteins and explore the association between lipoproteins and AF, the effects of lipoproteins on haemostasis, and the potential contribution of lipoproteins to thrombogenesis in AF. There are several types of lipoproteins based on size, lipid composition, and apolipoprotein category, namely: chylomicrons, very low-density lipoprotein, low-density lipoprotein (LDL), intermediate-density lipoprotein, and high-density lipoprotein. Each of these lipoproteins may contain numerous lipid species and proteins with a variety of different functions. Furthermore, the lipoprotein particles may be oxidized causing an alteration in their structure and content. Of note, there is a paradoxical inverse relationship between total cholesterol and LDL cholesterol (LDL-C) levels, and incident AF. The mechanism by which this occurs may be related to the stabilizing effect of cholesterol on myocardial membranes, along with its role in inflammation. Overall, specific lipoproteins may interact with haemostatic pathways to promote excess platelet activation and thrombin generation, as well as inhibiting fibrinolysis. In this regard, LDL-C has been shown to be an independent risk factor for thromboembolic events in AF. The complex relationship between lipoproteins, thrombosis and AF warrants further research with an aim to improve our knowledge base and contribute to our overall understanding of lipoprotein-mediated thrombosis.

A general presentation on how to carry out a CHARMS analysis for prognostic multivariate models

The CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist was created to provide methodological appraisals of predictive models, based on the best available scientific evidence and through systematic reviews. Our purpose is to give a general presentation on how to carry out a CHARMS analysis for prognostic multivariate models, making clear what the steps are and how they are applied individually to the studies included in the systematic review. This tutorial is aimed at providing such a resource. In addition to this explanation, we will apply the method to a real case: predictive models of atrial fibrillation in the community. This methodology could be applied to other predictive models using the steps provided in our review so as to have complete information for each included model and determine whether it can be implemented in daily clinical practice.

CHA2DS2-VASc and readmission with new-onset atrial fibrillation, atrial flutter, or acute cerebrovascular accident

BACKGROUND

Although risk factors for atrial fibrillation (AF) and atrial flutter (AFL) are known, identifying patients who will develop AF/AFL within the near future remains challenging. We sought to evaluate if the CHA₂DS₂-VASc risk score (CVRS) can identify hospital readmissions with AF, AFL, or acute cerebrovascular accident (CVA) among hospitalized patients without prior history of AF/AFL.

METHODS

Using the Nationwide Readmission Database, a study cohort included patients without prior AF/AFL or new diagnosis of AF/AFL at the index hospitalization from 2012 to 2014. Patients were stratified based on the CVRS into three groups: Low (CVRS ≤1), Intermediate (CVRS 2-5), and High (CVRS ≥6).The primary outcome of interest was 180-day readmission rate with a primary or secondary diagnosis of AF/AFL. Secondary outcomes of interest were acute CVA and 6-month mortality rate.

RESULTS

A total of 17,820,640 patients were included in our study. Over a 6-month follow up duration from the index hospitalization, the overall re-admission rate for new onset atrial arrhythmias (AF/AFL) was 3.48% (n = 620,986), acute CVA 0.13% (n = 22,522), and all-cause mortality 0.31% (n = 55,632). When compared to other groups, patients with a higher CVRS were readmitted more frequently for AF/AFL [odds ratio (OR) 2.43; 95% confidence interval (CI) 2.41-2.45, P < .0001), acute CVA (OR 3.96; 95%CI 3.85-4.08, P < .0001), and all-cause mortality (OR 2.19; 95%CI 2.14-2.24, P < .0001).

CONCLUSION

In this large contemporary cohort, a CHADS2VA2SC score ≥ 6 identified patients without known prior atrial arrhythmias at an elevated risk of developing AF/AFL or acute CVA within 6 months of hospitalization.

Usefulness of the ACTEL Score to Predict Atrial Fibrillation in Patients with Cryptogenic Stroke

INTRODUCTION

To assess the probability of undetected atrial fibrillation (AF) in patients with ischemic stroke, we previously compared patients who were first diagnosed with AF with patients with large or small artery disease and obtained the MrWALLETS 8-item scoring system. In the present study, we utilized cryptogenic strokes (CS) as the control group, as AF is normally sought among CS patients.

METHODS

We retrospectively examined 191 ischemic stroke patients (72.5 ± 12.6 years), 68 with first diagnosed AF and 123 with CS, who had undergone 2 brain CT scans, echocardiography, carotid/vertebral ultrasound, continuous electrocardiogram monitoring and anamnestic/laboratory search for cardiovascular risk factors.

RESULTS

In logistic regression, 5 variables were independently associated with AF, forming the "ACTEL" score: Age ≥75 years (OR 2.42, 95% CI 1.18-4.96, p = 0.02; +1 point); hyperCholesterolemia (OR 0.38, 95% CI 0.18-0.78, p = 0.009; -1 point); Tricuspid regurgitation ≥ mild-to-moderate (OR 4.99, 95% CI 1.63-15.27, p = 0.005; +1 point); left ventricular End-diastolic volume <65 mL (OR 7.43, 95% CI 2.44-22.6, p = 0.0004; +1 point); Left atrium ≥4 cm (OR 4.57, 95% CI 1.97-10.62, p = 0.0004; +1 point). The algebraic sum of these points may range from -1 to +4. For AF identification, the area under the receiver operating characteristic curve was 0.80 (95% CI 0.73-0.87). With a cutoff of ≥2, positive predictive value was 80.8%, specificity 92.7% and sensitivity 55.9%.

CONCLUSIONS

The ACTEL score, a simplified and improved version of the MrWALLETS score, allows the identification of patients with first diagnosed AF, in the context of CSs, with a high positive predictive value.

Lessons from the current European Heart Rhythm Association consensus document on screening for atrial fibrillation

In this paper, we reviewed the atrial fibrillation screening strategies in a stepwise manner and discussed the uncertainties in the assessment of the need for anticoagulation in light of the recently published European Heart Rhythm Association consensus document. We reviewed not only the methods and tools but also the role of health care professionals and patient organizations in addition to cost-effectiveness issues.

Evaluation of a clinical score for predicting atrial fibrillation in cryptogenic stroke patients with insertable cardiac monitors: results from the CRYSTAL AF study

Background

The HAVOC score was previously developed to predict the risk of atrial fibrillation (AF) after cryptogenic stroke (CS) or transient ischemic attack (TIA). The purpose of this study was to apply the HAVOC score to patients who received insertable cardiac monitors (ICMs) in the CRYSTAL AF study.

Methods

All patients from the CRYSTAL AF study who received an ICM were included. HAVOC score (one point each for peripheral vascular disease and obesity with body mass index >30, two points each for hypertension, age ⩾ 75, valvular heart disease, and coronary artery disease, 4 points for congestive heart failure) was computed for all patients. The primary endpoint was AF detection by 12 months of ICM monitoring.

Results

A total of 214 patients who received ICM were included. AF was detected in 40 patients while the remaining 174 patients were AF negative. The HAVOC score was significantly higher among patients with AF [median 3.0 with interquartile range (IQR) 2-4] than those without AF [median 2.0 (IQR 0-3)], p = 0.01. AF increased significantly across the three HAVOC score groups: 11% in Group A (score 0-1), 18% in Group B (score 2-3), and 32 % in Group C (score ⩾ 4) with p = 0.02.

Conclusions

The HAVOC score was shown in this post hoc analysis of CRYSTAL AF to successfully stratify AF risk post CS or TIA. The 11% AF rate in the lowest HAVOC score group highlights the significance of nontraditional contributors to AF and ischemic stroke.

Effectiveness of screening for atrial fibrillation and its determinants. A meta-analysis

Background

Many atrial fibrillation patients eligible for oral anticoagulants are unaware of the presence of AF, and improved detection is necessary to facilitate thromboprophylaxis against stroke.

Objective

To assess the effectiveness of screening for AF compared to no screening and to compare efficacy outcomes of different screening strategies.

Materials and methods

Cochrane Central Register of Controlled Trials, EMBASE and MEDLINE from Jan 1, 2000 –Dec 31, 2015 were searched. Studies employing systematic or opportunistic screening and using ECG or pulse palpation in populations age ≥40 years were included. Data describing study and patient characteristics and number of patients with new AF were extracted. The outcome was the incidence of previously undiagnosed AF.

Results

We identified 25 unique (3 RCTs and 22 observational) studies (n = 88 786) from 14 countries. The incidence of newly detected AF due to screening was 1.5% (95% CI 1.1 to 1.8%). Systematic screening was more effective than opportunistic: 1.8% (95% CI 1.4 to 2.3%) vs. 1.1% (95% CI 0.6 to 1.6%), p<0.05, GP-led screening than community based: 1.9% (95% CI 1.4 to 2.4%) vs. 1.1% (95% CI 0.7 to 1.6%), p<0.05, and repeated heart rhythm measurements than isolated assessments of rhythm: 2.1% (95% CI 1.5–2.8) vs. 1.2% (95% CI 0.8–1.6), p<0.05. Only heart rhythm measurement frequency had statistical significance in a multivariate meta-regression model (p<0.05).

Conclusions

Active screening for AF, whether systematic or opportunistic, is effective beginning from 40 years of age. The organisation of screening process may be more important than technical solutions used for heart rhythm assessment.

Data Linking Diabetes Mellitus and Atrial Fibrillation-How Strong Is the Evidence? From Epidemiology and Pathophysiology to Therapeutic Implications

According to estimates, around 5% of the world population has hazel eyes. And there are about as many people with diabetes mellitus (DM). Red hair occurs naturally in up to 2% of the human population. And about as many people are estimated to have atrial fibrillation (AF). If a hazel eyed person with red hair does not surprise us, should a diabetic patient with AF? Accumulating epidemiologic data suggest, however, that the DM-AF association may be more than a simple coincidence. But, how strong is this evidence? Experimental studies bring evidence for a DM-induced atrial proarrhythmic remodelling. But how relevant are these data for the clinical setting? In this review, we aim to provide a critical analysis of the existing clinical and experimental, epidemiologic, and mechanistic data that bridge DM and AF, we emphasize a number of questions that remain to be answered, and we identify hotspots for future research. The therapeutic implications of the DM-AF coexistence are also discussed, with a focus on rhythm control and on conventional and DM-specific upstream therapies for AF management.

Anticoagulant use in atrial fibrillation and risk of dementia: review of contemporary knowledge

INTRODUCTION

Atrial fibrillation (AF) is strongly associated with dementia, including idiopathic dementias such as Alzheimer's disease. The relative risk of dementia is highest in AF patients 70 years and younger, and the burden of exposure to arrhythmia appears to underlie part of the risk. Areas covered: Anticoagulation choices and approach influence dementia risk. In warfarin patients, inadequate time spent in therapeutic range is highly associated with the increased dementia risk long-term. This risk is further accentuated with frequent over anticoagulation in patients also receiving aspirin. Direct oral anticoagulant therapies in early observational studies show that there is potential for improving long-term risk of dementia when compared to warfarin, although prospective trials are needed. AF and dementia are end manifestations of systemic disease; a systemic approach is needed with early treatment of shared risk factors to prevent disease presentation altogether. Expert commentary: In this review, we will bring together available data with regards to the link between anticoagulant use for AF and dementia. Anticoagulation initiation timing, use, and efficacy remain critical risk factors for dementia in AF patients and consequently provide opportunities to decrease risk.

Selective screening for atrial fibrillation using multivariable risk models

Objective

Atrial fibrillation can lead to stroke if untreated, and identifying those at higher risk is necessary for cost-effective screening for asymptomatic, paroxysmal atrial fibrillation. Age has been proposed to identify those at risk, but risk models may provide better discrimination. This study compares atrial fibrillation risk models with age for screening for atrial fibrillation.

Methods

Nine atrial fibrillation risk models were compared using the Atherosclerosis Risk in Communities study (11 373 subjects, 60.0±5.7 years old). A new risk model (Screening for Asymptomatic Atrial Fibrillation Events—SAAFE) was created using data collected in the Monitoring Disparities in Chronic Conditions study (3790 subjects, 58.9±15.3 years old). The primary measure was the fraction of incident atrial fibrillation subjects who should receive treatment due to a high CHA2DS2-VASc score identified when screening a fixed number equivalent to the age criterion. Secondary measures were the C statistic and net benefit.

Results

Five risk models were significantly better than age. Age identified 71 (61%) of the subjects at risk for stroke who subsequently developed atrial fibrillation, while the best risk model identified 96 (82%). The newly developed SAAFE model identified 95 (81%), primarily based on age, congestive heart failure and coronary artery disease.

Conclusions

Use of a risk model increases identification of subjects at risk for atrial fibrillation. One of the best performing models (SAAFE) does not require an ECG for its application, so that it could be used instead of age as a screening criterion without adding to the cost.

Differential characteristics, stroke recurrence, and predictors of covert atrial fibrillation of embolic strokes of undetermined source

Background and purpose Identifying embolic strokes of undetermined source (ESUS) patients likely to harbor atrial fibrillation may have diagnostic and therapeutic implications. Our aim was to examine differences between ESUS and cardioembolic strokes, to evaluate stroke recurrence rate among ESUS and to identify baseline characteristics of ESUS patients who were later diagnosed with atrial fibrillation. Materials and methods We assessed all ischemic stroke patients admitted between June 2012 and November 2013. ESUS were compared to cardioembolic strokes at discharge. After at least 12-month follow-up, ESUS patients diagnosed with atrial fibrillation were compared to those who remained as ESUS. Results There were 236 ischemic strokes, 32.6% were ESUS. Compared to cardioembolic strokes, ESUS were younger (p < 0.0001), had milder strokes (p < 0.05), less prevalence of hypertension (p < 0.05), peripheral vascular disease (p < 0.05), and previous ischemic stroke (p < 0.05). After follow-up, 15% of ESUS patients had stroke recurrences and 12% evidenced paroxysmal atrial fibrillation. ESUS patients diagnosed with atrial fibrillation in the follow-up were older (p < 0.0001), had higher erythrocyte sedimentation rate (p < 0.05), and were more likely to have ≥2 infarcts in the same arterial territory in the initial magnetic resonance imaging (p < 0.05). Conclusions Older age, small-scattered infarcts on initial magnetic resonance imaging and high erythrocyte sedimentation rate levels appear to identify ESUS patients more likely to be diagnosed of atrial fibrillation during follow-up.

A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack

OBJECTIVES

Detection of atrial fibrillation (AF) in post-cryptogenic stroke (CS) or transient ischemic attack (TIA) patients carries important therapeutic implications.

METHODS

To risk stratify CS/TIA patients for later development of AF, we conducted a retrospective cohort study using data from 1995 to 2015 in the Stanford Translational Research Integrated Database Environment (STRIDE).

RESULTS

Of the 9,589 adult patients (age ≥40 years) with CS/TIA included, 482 (5%) patients developed AF post CS/TIA. Of those patients, 28.4, 26.3, and 45.3% were diagnosed with AF 1-12 months, 1-3 years, and >3 years after the index CS/TIA, respectively. Age (≥75 years), obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease, and valve disease are significant risk factors, with the following respective odds ratios (95% CI): 1.73 (1.39-2.16), 1.53 (1.05-2.18), 3.34 (2.61-4.28), 2.01 (1.53-2.68), 1.72 (1.35-2.19), 1.37 (1.02-1.84), and 2.05 (1.55-2.69). A risk-scoring system, i.e., the HAVOC score, was constructed using these 7 clinical variables that successfully stratify patients into 3 risk groups, with good model discrimination (area under the curve = 0.77).

CONCLUSIONS

Findings from this study support the strategy of looking longer and harder for AF in post-CS/TIA patients. The HAVOC score identifies different levels of AF risk and may be used to select patients for extended rhythm monitoring.

Prognostic Impact of Atrial Fibrillation and New Risk Score of Its Onset in Patients at High Risk of Heart Failure - A Report From the CHART-2 Study

BACKGROUND

The prognostic impact of atrial fibrillation (AF) among patients at high risk for heart failure (HF) remains unclear. In addition, there is no risk estimation model for AF development in these patients.Methods and Results:The present study included 5,382 consecutive patients at high risk of HF enrolled in the CHART-2 Study (n=10,219). At enrollment, 1,217 (22.6%) had AF, and were characterized, as compared with non-AF patients, by higher age, lower estimated glomerular filtration rate, higher B-type natriuretic peptide (BNP) level and lower left ventricular ejection fraction. A total of 116 non-AF patients (2.8%) newly developed AF (new AF) during the median 3.1-year follow-up. AF at enrollment was associated with worse prognosis for both all-cause death and HF hospitalization (adjusted hazard ratio (aHR) 1.31, P=0.027 and aHR 1.74, P=0.001, for all-cause death and HF hospitalization, respectively) and new AF was associated with HF hospitalization (aHR 4.54, P<0.001). We developed a risk score with higher age, smoking, pulse pressure, lower eGFR, higher BNP, aortic valvular regurgitation, LV hypertrophy, and left atrial and ventricular dilatation on echocardiography, which effectively stratified the risk of AF development with excellent accuracy (AUC 0.76).

CONCLUSIONS

These results indicated that AF is associated with worse prognosis in patients at high risk of HF, and our new risk score may be useful to identify patients at high risk for AF onset.

Oxidant and Inflammatory Mechanisms and Targeted Therapy in Atrial Fibrillation: An Update

Atrial fibrillation (AF) is an important cause of stroke and risk factor for heart failure and death. Current pharmacologic treatments for AF have limited efficacy, and treatments that more directly target the underlying causes of AF are needed. Oxidant stress and inflammatory activation are interrelated pathways that promote atrial electrical and structural remodeling, leading to atrial ectopy, interstitial fibrosis, and increased stroke risk. This review evaluates the impact of common stressors on atrial oxidant stress and inflammatory activation and the contribution of these pathways to atrial remodeling. Recent studies suggest that integrated efforts to target the underlying risk factors, rather than the AF per se, may have a greater impact on health and outcomes than isolated efforts focused on the electrical abnormalities.

Association of left atrial epicardial adipose tissue with electrogram bipolar voltage and fractionation: Electrophysiologic substrates for atrial fibrillation

BACKGROUND

Epicardial adipose tissue (EAdT) is metabolically active and likely contributes to atrial fibrillation (AF) through the release of inflammatory cytokines into the myocardium or through its rich innervation with ganglionated plexi at the pulmonary vein ostia. The electrophysiologic mechanisms underlying the association between EAdT and AF remain unclear.

OBJECTIVE

The purpose of this study was to investigate the association of EAdT with adjacent myocardial substrate.

METHODS

Thirty consecutive patients who underwent cardiac computed tomography as well as electroanatomic mapping in sinus rhythm before an initial AF ablation procedure were studied. Semiautomatic segmentation of atrial EAdT was performed and registered anatomically to the voltage map.

RESULTS

In multivariable regression analysis clustered by patient, age (-0.01 per year) and EAdT (-0.29) were associated with log bipolar voltage as well as low-voltage zones (<0.5 mV). Age (odds ratio [OR]: 1.02 per year), male gender (OR: 3.50), diabetes (OR: 2.91), hypertension (OR: 2.55), and EAdT (OR: 8.56) were associated with fractionated electrograms, and age (OR: 2.80), male gender (OR: 3.00), and EAdT (OR: 7.03) were associated with widened signals. Age (OR: 1.03 per year) and body mass index (OR: 1.06 per kg/m²) were associated with atrial fat.

CONCLUSION

The presence of overlaying EAdT was associated with lower bipolar voltage and electrogram fractionation as electrophysiologic substrates for AF. EAdT was not a statistical mediator of the association between clinical variables and AF substrate. Body mass index was directly associated with the presence of EAdT in patients with AF.

A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study

BACKGROUND

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF risk score was developed to predict incident AF in three American cohorts and it was validated in two European cohorts. The CHA2DS2-VASc risk score was derived to predict risk of stroke, peripheral embolism, and pulmonary embolism in individuals with AF, but it has been increasingly used for AF risk prediction. We compared CHARGE-AF risk score versus CHA2DS2-VASc risk score for incident AF risk in a community-based cohort.

METHODS AND RESULTS

We studied Framingham Heart Study participants aged 46 to 94 years without prevalent AF and with complete covariates. We predicted AF risk using Fine-Gray proportional sub-distribution hazards regression. We used the Wald χ(2) statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ(2) statistic for calibration. We included 9722 observations (mean age 63.9 ± 10.6 years, 56% women) from 4548 unique individuals: 752 (16.5%) developed incident AF and 793 (17.4%) died. The mean CHARGE-AF score was 12.0 ± 1.2 and the sub-distribution hazard ratio (sHR) for AF per unit increment was 2.15 (95% CI, 99-131%; P < .0001). The mean CHA2DS2-VASc score was 2.0 ± 1.5 and the sHR for AF per unit increment was 1.43 (95% CI, 37%-51%; P < .0001). The CHARGE-AF model had better fit than CHA2DS2-VASc (Wald χ(2) = 403 vs 209, both with 1 df), improved discrimination (C-statistic = 0.75, 95% CI, 0.73-0.76 vs C-statistic = 0.71, 95% CI, 0.69-0.73), and better calibration (HL χ(2) = 5.6, P = .69 vs HL χ(2) = 28.5, P < .0001).

CONCLUSION

The CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort.

Long-term Arrhythmia Monitoring in Cryptogenic Stroke: Who, How, and for How Long?

Cryptogenic stroke and transient ischemic attack (TIA) account for approximately one-third of stroke patients [1]. Paroxys-mal atrial fibrillation (PAF) has been suggested as a major etiology of these cryptogenic strokes [2, 3]. PAF can be difficult to diagnose because it is intermittent, often brief, and asymptomatic. PAF might be more prevalent than persistent atrial fibrillation in stroke and TIA patients, especially in younger populations [4, 5]. In patients with atrial fibrillation, anticoagulation provides significant risk reduction [6]. A new generation of oral anticoagulants has been approved for non-valvular atrial fibrillation, providing a variety of therapeutic options for patients with atrial fibrillation and risk of stroke [7]. Prior practice included an admission electrocardiogram (ECG) and continuous telemetry monitoring while in hospital [8]. However, this approach can lead to under-detection of brief asymptomatic events, which can occur at variable intervals, often outside of the hospital setting. Technological advancements have led to devices that can monitor cardiac rhythms outside of the hospital for longer durations resulting in higher yield of detection of atrial fibrillation events. Moreover, recent studies show that the normal monitoring time for arrhythmias may be shorter than ideal in order to detect atrial fibrillation, and increasing this interval could significantly improve detection of atrial fibrillation in these patients [9, 10]. The aim of this study is to review the literature in order to define what subgroup of patients, with what methodologies, and for how long monitoring for atrial fibrillation should occur in patients presenting with cryptogenic stroke.

Research on Algorithm of Extracting PPG Signal for Detecting Atrial Fibrillation based on Probability Density Function

The paper introduced a new method based on probability density function (PDF) and phase space diagram method for photoplethysmography (PPG) signal extracting. In the paper, PPG information was generated from human fingertips by smartphones. The pulse wave period was then separated and reconstructed into probability density function (PDF) by the phase space diagram algorithm. The difference between normal sinus rhythm (NSR) and atrial fibrillation (AF) was finally found by skewness of the PDF. The results of the present study demonstrates that the new method is vividly viable for detecting AF on the smartphone.

Early detection of occult atrial fibrillation and stroke prevention

Atrial fibrillation (AF) is a very common arrhythmia and significantly increases stroke risk. This risk can be mitigated with oral anticoagulation, but AF is often asymptomatic, or occult, preventing timely detection and treatment. Accordingly, occult AF may cause stroke before it is clinically diagnosed. Currently, guidelines for the early detection and treatment of occult AF are limited. This review addresses recent advancements in occult AF detection methods, identification of populations at high risk for occult AF, the treatment of occult AF with oral anticoagulation, as well as ongoing trials that may answer critically important questions regarding occult AF screening.