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

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.

Epidemiology, prognosis, and challenges in the management of hyperthyroidism-related atrial fibrillation

Atrial fibrillation (AF) is a common condition with a global estimated prevalence of 60 million cases, and the most common cardiac complication of hyperthyroidism, occurring in 5-15% of overtly hyperthyroid patients. Additionally, subclinical hyperthyroidism and high-normal free T4 have been associated with an increased risk in the development of AF. Hyperthyroidism-related AF is a reversible cause of AF, and the majority of patients spontaneously revert to sinus rhythm in 4-6 months during or after restoration of euthyroidism. Therefore, restoring thyroid function is an indispensable element in hyperthyroidism-related AF management. Rate control with beta-blockers consists another first-line therapy, reserving rhythm control in cases of persistent hyperthyroidism-related AF. It is still controversial whether hyperthyroidism is an independent risk factor of stroke in nonvalvular AF. As a result, initiating anticoagulation should be guided by the clinical thromboembolic risk score CHA2DS2-VASc score in the same way it is applied in patients with non-hyperthyroidism-related AF. Treatment with the novel direct oral anticoagulants appears to be as beneficial and may be safer than warfarin in patients with hyperthyroidism-related AF. In this review, we address the epidemiology, prognosis, and diagnosis of hyperthyroidism-related AF, and we discuss the management strategies and controversies in patients with hyperthyroidism-related AF.

Detection rates of asymptomatic carotid artery stenosis and atrial fibrillation by selective screening of patients without cardiovascular disease

BACKGROUND

Individuals with significant asymptomatic carotid artery stenosis (ACAS) and atrial fibrillation (AF) could benefit from specific interventions to prevent heart attack and stroke, but are often clinically 'silent'. We aimed to determine detection rate of ACAS and AF by screening, targeting a population at increased cardiovascular risk.

METHODS

Data on adults who attended voluntary and self-funded commercial screening clinics in the United States or the United Kingdom between 2008 and 2013 were used. The Atherosclerotic Cardiovascular Disease (ASCVD) risk equation was applied to each participants and detection rates of targeted screening for ≥50% ACAS and AF to those at highest risk of CVD was assessed.

RESULTS

Among 0.4 million individuals between 40 and 80 years, without CVD, 6191 (1.6%) had ACAS and 1026 (0.3%) had AF. Selective screening of participants with a predicted 10-year CVD risk of ≥20% identified 40% of ACAS cases, a prevalence of 3.7%, leading to a number needed to screen (NNS) of 27, as well as 39% of AF cases, a prevalence of 0.6%, with a NNS of 170. Selective screening of those with a predicted 10-year CVD risk of ≥15% identified 54% of ACAS cases, a prevalence of 3.3%, and an NNS of 31, as well as 51% of AF cases, a prevalence of 0.5%, with an NNS of 195.

CONCLUSIONS

Selective screening for ACAS and AF implemented in ASCVD risk assessment greatly reduces the NNS when compared with population-level screening with detection rates of ACAS and AF substantially greater in people at higher predicted CVD risk.

End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks

BACKGROUND

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas- cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil.

METHODS

We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition.

RESULTS

The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years.

CONCLUSION

We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes.

Analyses of m6A regulatory genes and subtype classification in atrial fibrillation

Objective

To explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.

Methods

We downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.

Results

The data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.

Conclusion

The m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis

BACKGROUND

Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk.

OBJECTIVE

The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF.

METHODS

Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals' 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m²), as well as the relation between self-reported physical activity and predicted 5-year AF risk.

RESULTS

We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk.

CONCLUSIONS

Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation.

CHARGE-AF: A Useful Score For Atrial Fibrillation Prediction?

Atrial fibrillation (AF) is the commonest arrhythmia in clinical practice and is associated with increased morbidity and mortality. Various predictive scores for new-onset AF have been proposed, but so far, none have been widely used in clinical practice. CHARGE-AF score was developed from a pooled diverse population from three large cohorts (Atherosclerosis Risk in Communities study, Cardiovascular Health Study and Framingham Heart Study). A simple 5-year predictive model includes the variables of age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction and heart failure. Recent studies report that the CHARGE-AF score has good discrimination for incident AF and seems to be a promising prediction model for this arrhythmia. New screening tools (smartphone apps, smartwatches) are rapidly developing for AF detection. Therefore, the wide application of the CHARGE-AF score in clinical practice and the upcoming usage of mobile health technologies and smartwatches may result in better AF prediction and adequate stroke prevention, especially in high-risk patients.

Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network

Aims

Atrial fibrillation (AF) is associated with significant morbidity but remains underdiagnosed. A 24 h ambulatory electrocardiogram (ECG) is largely used as a tool to document AF but yield remains limited. We hypothesize that a deep learning model can identify patients at risk of AF in the 2 weeks following a 24 h ambulatory ECG with no documented AF.

Methods and results

We identified a training set of Holter recordings of 7-15 days duration, in which no AF could be found in the first 24 h. We trained a neural network to predict the presence or absence of AF in the 15 following days, using only the first 24 h of the recording. We evaluated the neural network on a testing set and an external data set not used during algorithm development. In the testing data set, out of 9993 Holters with no AF on the first day, we found 361 (4%) recordings with AF within the 15 subsequent days of monitoring [5808, 218 (4%), respectively in the external data set]. The neural network could discriminate future AF with an area under the receiver operating curve, a sensitivity, and specificity of 79.4%, 76%, and 69%, respectively (75.8%, 78%, and 58% in the external data set), and outperformed ECG features previously shown to be predictive of AF.

Conclusion

We show here the very first study of short-term AF prediction using 24 h Holter monitoring. This could help identify patients who would benefit the most from longer recordings and proactively initiate treatment and AF mitigation strategies in high-risk patients.

Validating risk models versus age alone for atrial fibrillation in a young Dutch population cohort: should atrial fibrillation risk prediction be expanded to younger community members?

BACKGROUND

Advancing age is the primary selection criterion for community screening for atrial fibrillation (AF), with selection often restricted to those aged ≥65 years. If multivariable models were shown to have considerable additional value over age alone in predicting AF risk among younger individuals, AF screening could be expanded to patients with lower age, but with high AF risk as per a validated risk model.

METHODS

We validated risk models CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology model for AF) and FHS-AF (Framingham Heart Study model for AF), and risk scores CHA₂DS₂-VASc and CHA₂DS₂-VA, and presented their predictive abilities for 5-year and 10-year AF risk versus that of age alone in a young Dutch population cohort (PREVEND) free from AF at baseline. We assessed discrimination by the C-statistic and calibration by the calibration plot and stratified Kaplan-Meier plot using survey-weighted Cox models.

RESULTS

During 5-year and 10-year follow-up there were n=98 (2.46/1000 person-years) and n=249 (3.29/1000 person-years) new AF cases, respectively, among 8265 participants with mean age 49±13 years. CHARGE-AF and FHS-AF both showed good discrimination for 5-year and 10-year AF (C-statistic range 0.83-0.86) with accurate calibration for 5-year AF, but overestimation of 10-year AF risk in highest-risk individuals. CHA₂DS₂-VASc and CHA₂DS₂-VA relatively underperformed. Age alone showed similar discrimination to that of CHARGE-AF and FHS-AF both in the overall, young PREVEND cohort and in subgroups for lower age and lower stroke risk.

CONCLUSION

Multivariable models accurately discriminate for 5-year and 10-year AF risk among young European community-dwelling individuals. However, their additional discriminatory value over age alone was limited. Selection strategies for primary AF screening using multivariable models should not be expanded to younger individuals.

Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis

Objective

Atrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community.

Methods

Ovid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation.

Results

Eleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526–0.815), CHA₂DS₂-VASc (Congestive heart failure, Hypertension, Age>75 (2 points), Stroke/transient ischemic attack/thromboembolism (2 points), Vascular disease, Age 65–74, Sex category) (summary c-statistic 0.679; 95% CI 0.620 to 0.736; 95% PI 0.531–0.811) and HATCH (Hypertension, Age, stroke or Transient ischemic attack, Chronic obstructive pulmonary disease, Heart failure) (summary c-statistic 0.669; 95% CI 0.600 to 0.732; 95% PI 0.513–0.803) models resulted in a c-statistic with a statistically significant 95% PI and moderate discriminative performance. No model met eligibility for inclusion in meta-analysis if studies at high risk of bias were excluded and certainty of effect estimates was ‘low’. Models derived by machine learning demonstrated strong discriminative performance, but lacked rigorous external validation.

Conclusions

Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance.

Systematic review registration

PROSPERO CRD42021245093.

Joint Associations Between Body Mass Index and Waist Circumference With Atrial Fibrillation in Men and Women

Background Associations between adiposity and atrial fibrillation (AF) might differ between sexes. We aimed to determine precise estimates of the risk of AF by body mass index (BMI) and waist circumference (WC) in men and women. Methods and Results Between 2008 and 2013, over 3.2 million adults attended commercial screening clinics. Participants completed health questionnaires and underwent physical examination along with cardiovascular investigations, including an ECG. We excluded those with cardiovascular and cardiac disease. We used multivariable logistic regression and determined joint associations of BMI and WC and the risk of AF in men and women by comparing likelihood ratio χ2 statistics. Among 2.1 million included participants 12 067 (0.6%) had AF. A positive association between BMI per 5 kg/m2 increment and AF was observed, with an odds ratio of 1.65 (95% CI, 1.57-1.73) for men and 1.36 (95% CI, 1.30-1.42) for women among those with a BMI above 20 kg/m2. We found a positive association between AF and WC per 10 cm increment, with an odds ratio of 1.47 (95% CI, 1.36-1.60) for men and 1.37 (95% CI, 1.26-1.49) for women. Improvement of likelihood ratio χ2 was equal after adding BMI and WC to models with all participants. In men, WC showed stronger improvement of likelihood ratio χ2 than BMI (30% versus 23%). In women, BMI showed stronger improvement of likelihood ratio χ2 than WC (23% versus 12%). Conclusions We found a positive association between BMI (above 20 kg/m2) and AF and between WC and AF in both men and women. BMI seems a more informative measure about risk of AF in women and WC seems more informative in men.