Atrial Cardiopathy and Cryptogenic Stroke

Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12-Lead ECGs Based on Left Atrial Remodeling

BACKGROUND

We hypothesized that analysis of serial ECGs could predict new-onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs immediately before AF occurrence. Our aim in this study was to compare the performance of 2 types of machine learning (ML) algorithms.

METHODS AND RESULTS

Standard 12-lead ECGs of patients selected by cardiologists between January 2010 and May 2021 were used for ML model development. Two ML models (single ECG and serial ECG) were developed using a light gradient boosting machine-learning algorithm. Model performance was evaluated based on the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and F1 score. We trained the ML models on 415 964 ECGs from 176 090 patients. When testing the 2 ML models using external validation data sets, the performance of the serial-ML model was significantly better than that of the single-ML model for predicting new-onset AF (single- versus serial-ML model: sensitivity 0.744 versus 0.810; specificity 0.742 versus 0.822; accuracy 0.743 versus 0.816; F1 score 0.743 versus 0.815; area under the receiver operating characteristic curve 0.812 versus 0.880; P<0.001). The Shapley Additive Explanations analysis ranked P-wave duration and amplitude among the top 10 ECG parameters.

CONCLUSIONS

An ML model based on serial ECGs from an individual had greater ability to predict new-onset AF than the ML model based on a single ECG. P-wave morphologies were associated with future AF prediction.

Cancer and left atrial enlargement in patients with ischemic stroke

BACKGROUND

Cancer is associated with an increased risk of atrial fibrillation. Whether cancer is also associated with atrial cardiopathy, another atrial pathology associated with heightened ischemic stroke risk, is uncertain.

METHODS

We conducted a retrospective cross-sectional study among consecutive patients hospitalized with acute ischemic stroke at a quaternary care center in New York, United States from 2011 through 2016. The study exposure was active cancer. The study outcome was atrial cardiopathy, defined as a left atrial volume index ≥35 mL/m2 on echocardiography. We used multivariable logistic regression, adjusting for baseline characteristics, to evaluate the relationship between cancer (active or historical) and atrial cardiopathy. We performed a subgroup analysis among patients with embolic stroke of undetermined source (ESUS).

RESULTS

The final cohort included 1104 patients with acute ischemic stroke, of whom 10 % had active cancer and 47 % had atrial cardiopathy. Patients with atrial cardiopathy, compared to those without, were older (median age, 77 versus 68 years), and more frequently had hypertension, coronary disease, and atrial fibrillation. Active cancer was present in 9.6 % of patients with atrial cardiopathy (n = 50/520) and 10.4 % of patients without (n = 61/584). There was no association between active cancer and atrial cardiopathy among the overall ischemic stroke cohort (adjusted odds ratio [OR], 0.91; 95 % confidence interval [CI], 0.60-1.37) nor in patients with ESUS (aOR, 0.64; 95 % CI, 0.30-1.36). When the cancer exposure was broadened to include any history of cancer (n = 236, 21.4 %), there still was no significant association with atrial cardiopathy (aOR, 0.93; 95 % CI, 0.68-1.25).

CONCLUSIONS

When defining atrial cardiopathy by left atrial volume, we did not find an association between cancer and atrial cardiopathy in patients with ischemic stroke, including among those with ESUS. Future studies, evaluating other atrial cardiopathy biomarkers and settings, are needed to further investigate any potential link between cancer and atrial cardiopathy.

Atrial cardiomyopathy revisited-evolution of a concept: a clinical consensus statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), the Asian Pacific Heart Rhythm Society (APHRS), and the Latin American Heart Rhythm Society (LAHRS)

AIMS

The concept of "atrial cardiomyopathy" (AtCM) had been percolating through the literature since its first mention in 1972. Since then, publications using the term were sporadic until the decision was made to convene an expert working group with representation from four multinational arrhythmia organizations to prepare a consensus document on atrial cardiomyopathy in 2016 (EHRA/HRS/APHRS/SOLAECE expert consensus on atrial cardiomyopathies: definition, characterization, and clinical implication). Subsequently, publications on AtCM have increased progressively.

METHODS AND RESULTS

The present consensus document elaborates the 2016 AtCM document further to implement a simple AtCM staging system (AtCM stages 1-3) by integrating biomarkers, atrial geometry, and electrophysiological changes. However, the proposed AtCM staging needs clinical validation. Importantly, it is clearly stated that the presence of AtCM might serve as a substrate for the development of atrial fibrillation (AF) and AF may accelerates AtCM substantially, but AtCM per se needs to be viewed as a separate entity.

CONCLUSION

Thus, the present document serves as a clinical consensus statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), the Asian Pacific Heart Rhythm Society (APHRS), and the Latin American Heart Rhythm Society (LAHRS) to contribute to the evolution of the AtCM concept.

Cardioembolic Stroke: Past Advancements, Current Challenges, and Future Directions

Cardioembolic stroke accounts for over 20% of ischemic strokes and is associated with worse outcomes than other types of strokes. Atrial fibrillation (AF) is the most common risk factor for cardioembolic stroke. In this narrative review, we present an update about cardioembolic stroke mainly related to AF and atrial cardiopathy. Direct oral anticoagulants (DOACs) have revolutionized stroke prevention in patients with AF; however, their efficacy in preventing recurrent embolic stroke of unknown source remains uncertain. Various cardiac monitoring methods are used to detect AF, which is crucial for preventing stroke recurrence. DOACs are preferred over warfarin for AF-related stroke prevention; however, the timing of initiation after acute ischemic stroke is debated. Resuming anticoagulation after intracerebral hemorrhage in AF patients requires careful assessment of the risks. While catheter ablation may reduce the incidence of cardiovascular events, its effect on stroke prevention is unclear, especially in heart failure patients. Atrial cardiopathy is the emerging cause of embolic stroke of unknown source, which indicates atrial structural and functional disorders that can precede AF. Future research should focus on refining stroke risk prediction models, optimizing AF detection, understanding the roles of ablation and anticoagulation in stroke prevention, and establishing atrial cardiopathy as a therapeutic target, which could significantly reduce the burden of stroke.

Association between left atrial volume index and infarct volume in patients with ischemic stroke

Background

Left atrial volume index (LAVI) is one marker of atrial myopathy, which is increasingly being recognized as a cause of cardioembolic stroke even in the absence of atrial fibrillation. Cardiac embolism is associated with larger strokes than other stroke mechanisms. The purpose of this study was to examine the association between LAVI and total brain infarct volume in patients with ischemic stroke.

Methods

This was a retrospective study of 545 patients prospectively enrolled in the Cornell ActuE Stroke Academic Registry (CAESAR), which includes all acute ischemic stroke patients admitted to our hospital since 2011. LAVI measurements were obtained from our echocardiography image store system (Xclera, Philips Healthcare). Brain infarcts on diffusion-weighted images (DWI) were manually segmented and infarct volume was obtained on 3D Slicer. We used multiple linear regression models adjusted for age, sex, race, and vascular comorbidities including atrial fibrillation.

Results

Among 2,945 CAESAR patients, 545 patients had both total infarct volume and LAVI measured. We found an association between LAVI and log-transformed total brain infarct volume in both unadjusted (β = 0.018; p = 0.002) and adjusted (β = 0.024; p = 0.001) models.

Conclusion

We found that larger left atrial volume was associated with larger brain infarcts. This association was independent of known cardioembolic risk factors such as atrial fibrillation and heart failure. These findings support the concept that atrial myopathy may be a source of cardiac embolism even in the absence of traditionally recognized mechanisms such as atrial fibrillation.

Atrial Cardiomyopathy in Valvular Heart Disease: From Molecular Biology to Clinical Perspectives

This review discusses the evolving topic of atrial cardiomyopathy concerning valvular heart disease. The pathogenesis of atrial cardiomyopathy involves multiple factors, such as valvular disease leading to atrial structural and functional remodeling due to pressure and volume overload. Atrial enlargement and dysfunction can trigger atrial tachyarrhythmia. The complex interaction between valvular disease and atrial cardiomyopathy creates a vicious cycle of aggravating atrial enlargement, dysfunction, and valvular disease severity. Furthermore, atrial remodeling and arrhythmia can predispose to atrial thrombus formation and stroke. The underlying pathomechanism of atrial myopathy involves molecular, cellular, and subcellular alterations resulting in chronic inflammation, atrial fibrosis, and electrophysiological changes. Atrial dysfunction has emerged as an essential determinant of outcomes in valvular disease and heart failure. Despite its predictive value, the detection of atrial fibrosis and dysfunction is challenging and is not included in the clinical routine. Transthoracic echocardiography and cardiac magnetic resonance imaging are the main diagnostic tools for atrial cardiomyopathy. Recently published data have revealed that both left atrial volumes and functional parameters are independent predictors of cardiovascular events in valvular disease. The integration of atrial function assessment in clinical practice might help in early cardiovascular risk estimation, promoting early therapeutic intervention in valvular disease.

Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study

BACKGROUND

Atrial fibrillation (AF) detection and treatment are key elements to reduce recurrence risk in cryptogenic stroke (CS) with underlying arrhythmia. The purpose of the present study was to assess the predictors of AF in CS and the utility of existing AF-predicting scores in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study.

METHOD

The NOR-FIB study was an international prospective observational multicenter study designed to detect and quantify AF in CS and cryptogenic transient ischaemic attack (TIA) patients monitored by the insertable cardiac monitor (ICM), and to identify AF-predicting biomarkers. The utility of the following AF-predicting scores was tested: AS5F, Brown ESUS-AF, CHA₂DS₂-VASc, CHASE-LESS, HATCH, HAVOC, STAF and SURF.

RESULTS

In univariate analyses increasing age, hypertension, left ventricle hypertrophy, dyslipidaemia, antiarrhythmic drugs usage, valvular heart disease, and neuroimaging findings of stroke due to intracranial vessel occlusions and previous ischemic lesions were associated with a higher likelihood of detected AF. In multivariate analysis, age was the only independent predictor of AF. All the AF-predicting scores showed significantly higher score levels for AF than non-AF patients. The STAF and the SURF scores provided the highest sensitivity and negative predictive values, while the AS5F and SURF reached an area under the receiver operating curve (AUC) > 0.7.

CONCLUSION

Clinical risk scores may guide a personalized evaluation approach in CS patients. Increasing awareness of the usage of available AF-predicting scores may optimize the arrhythmia detection pathway in stroke units.

Electrocardiographic markers of atrial cardiomyopathy and risk of heart failure in the multi-ethnic study of atherosclerosis (MESA) cohort

Background

The association of electrocardiographic (ECG) markers of atrial cardiomyopathy with heart failure (HF) and its subtypes is unclear.

Methods

This analysis included 6,754 participants free of clinical cardiovascular disease (CVD), including atrial fibrillation (AF), from the Multi-Ethnic Study of Atherosclerosis. Five ECG markers of atrial cardiomyopathy (P-wave terminal force in V1 [PTFV1], deep-terminal negativity in V1 [DTNV1], P-wave duration [PWD], P-wave axis [PWA], advanced intra-atrial block [aIAB]) were derived from digitally recorded electrocardiograms. Incident HF events through 2018 were centrally adjudicated. An ejection fraction (EF) of 50% at the time of HF was used to classify HF as HF with reduced EF (HFrEF), HF with preserved EF (HFpEF), or unclassified HF. Cox proportional hazard models were used to examine the associations of markers of atrial cardiomyopathy with HF. The Lunn-McNeil method was used to compare the associations in HFrEF vs. HFpEF.

Results

413 HF events occurred over a median follow-up of 16 years. In adjusted models, abnormal PTFV1 (HR (95%CI): 1.56(1.15-2.13), abnormal PWA (HR (95%CI):1.60(1.16-2.22), aIAB (HR (95%CI):2.62(1.47-4.69), DTNPV1 (HR (95%CI): 2.99(1.63-7.33), and abnormal PWD (HR (95%CI): 1.33(1.02-1.73), were associated with increased HF risk. These associations persisted after further adjustments for intercurrent AF events. No significant differences in the strength of association of each ECG predictor with HFrEF and HFpEF were noted.

Conclusions

Atrial cardiomyopathy defined by ECG markers is associated with HF, with no differences in the strength of association between HFrEF and HFpEF. Markers of atrial Cardiomyopathy may help identify individuals at risk of developing HF.

Association of non-sustained atrial tachycardia and its duration in 24-h Holter monitoring with embolic stroke of unknown source

BACKGROUND AND PURPOSE

Although supraventricular ectopic beats (SVE), including premature atrial contractions (PACs) and non-sustained atrial tachycardia (NSAT), are frequent in the general population, some study results indicate that they are pathologic. SVE may predict undiagnosed atrial fibrillation or be associated with the embolic pattern of ischemic stroke. The aim of this study was to identify the indicators most associated with embolic stroke among the parameters that suggest the burden of SVE.

METHODS

A total of 1920 consecutive acute ischemic stroke (AIS) patients were enrolled from two university hospitals. We defined embolic stroke of unknown source (ESUS) and small vessel occlusion (SVO) etiologies using stricter criteria than the existing conventional criteria.

RESULTS

We enrolled 426 (SVO: 310 vs. ESUS: 116) patients who met the inclusion criteria. In the 24-h Holter monitoring parameters, total number of PACs and PAC-to-total beat ratio were not significantly different between the two groups. However, NSATs were more frequent, and the duration of the longest NSAT was longer in the ESUS group. Multivariate logistic regression revealed that high brain natriuretic peptide levels, presence of NSAT, history of previous stroke, and the longest NSAT duration significantly correlated with the ESUS etiology.

CONCLUSION

The presence of NSAT and its duration are more important indicators of embolic stroke than the frequency of PACs is. Therefore, considering secondary prevention in AIS patients with ESUS, 24-h Holter monitoring parameters, such as the presence of NSAT and its duration, could be considered as potential sources of cardio-embolism.

Molecular biomarkers predicting newly detected atrial fibrillation after ischaemic stroke or TIA: A systematic review

Background:

Several molecular biomarkers are available that predict newly detected atrial fibrillation (NDAF). We aimed to identify such biomarkers that predict NDAF after an Ischaemic stroke (IS)/Transient Ischaemic Attack (TIA) and evaluate their performance.

Methods:

A systematic review was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies of patients with IS, TIA, or both, who underwent ECG monitoring for ⩾24 h, which reported molecular biomarkers and frequency of NDAF after electronic searches of multiple databases were included.

Results:

Twenty-one studies (76% IS, 24% IS and TIA) involving 4640 patients were included. Twelve biomarkers were identified, with cardiac biomarkers evaluated in the majority (75%) of patients. Performance measures were inconsistently reported. Among cohorts selecting high-risk individuals (12 studies), the most studied biomarkers were N-Terminal-Pro Brain Natriuretic Peptide (NT-ProBNP, five studies; C-statistics reported by three studies, 0.69–0.88) and Brain Natriuretic Peptide (BNP, two studies; C-statistics reported in two studies, 0.68–0.77). Among unselected cohorts (nine studies), the most studied biomarker was BNP (six studies; C-statistics reported in five studies, 0.75–0.88). Only BNP was externally validated (two studies) but using different thresholds to categorise risk of NDAF.

Conclusion:

Cardiac biomarkers appear to have modest to good discrimination for predicting NDAF, although most analyses were limited by small, heterogeneous study populations. Their clinical utility should be explored further, and this review supports the need to assess the role of molecular biomarkers in large prospective studies with standardised selection criteria, definition of clinically significant NDAF and laboratory assays.