Echocardiography In the Prediction of Atrial Fibrillation Recurrence: A Review

Deep learning evaluation of echocardiograms to identify occult atrial fibrillation

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

Anticoagulating New-Onset Atrial Fibrillation After COVID-19: A Single-Center Experience

Anticoagulation (AC) strategy in new-onset atrial fibrillation (NOAF) secondary to other illnesses has not been broadly studied, and society-level guidance does not provide a strong recommendation regarding outpatient continuation upon discharge. Our study focused specifically on patients experiencing NOAF secondary to COVID-19. It sought to understand whether our facility's rounding prescribers were continuing patients on AC at discharge, the presence of arrhythmia at one-year follow-up, and to observe the risk of adverse outcomes in light of this unique precipitant. A retrospective cohort analysis and chart review were conducted of 231 consecutive inpatients during the initial 19 months of the COVID-19 pandemic. Eighteen patients experiencing NOAF with an average calculated CHA2DS2-VASc score of four were included in the cohort. Four patients (22%) died during hospitalization and 14 patients were discharged. Twelve of fourteen patients (86%) were discharged on AC, and eight remained adherent at follow-up. Two discharged patients died of unknown causes prior to follow-up. At follow-up, which occurred at a median of 1.2 years, 25% of the surviving cohort remained in atrial fibrillation (AF). No major bleeding events were recorded during the studied period. This retrospective analysis of a small sample of patients admitted to a single medical center for COVID-19 and experiencing NOAF demonstrates that local prescribers are continuing AC at discharge, that the rate of recurrence of AF is similar to onset in non-COVID illness at one year, and that risk of death approximated that of COVID-19 itself rather than NOAF.

The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation

Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Nevertheless, in some cases, these arrhythmias are not treated properly, resulting in their recurrences. If left untreated, they may result in complications such as strokes, heart failure, or death. Until recently, the primary techniques for diagnosing recurrent arrhythmias following CA were the findings predisposing to the changes caused by the arrhythmias on cardiac imaging and electrocardiograms during follow-up visits, or if patients reported having palpitations or chest discomfort after the ablation. However, these follow-ups may be time-consuming and costly, and they may not always determine the root cause of the recurrences. With the introduction of artificial intelligence (AI), these follow-up visits can be effectively shortened, and improved methods for predicting the likelihood of recurring arrhythmias after their ablation procedures can be developed. AI can be divided into two categories: machine learning (ML) and deep learning (DL), the latter of which is a subset of ML. ML and DL models have been used in several studies to demonstrate their ability to predict and identify cardiac arrhythmias using clinical variables, electrophysiological characteristics, and trends extracted from imaging data. AI has proven to be a valuable aid for cardiologists due to its ability to compute massive amounts of data and detect subtle changes in electric signals and cardiac images, which may potentially increase the risk of recurrent arrhythmias after CA. Despite the fact that these studies involving AI have generated promising outcomes comparable to or superior to human intervention, they have primarily focused on atrial fibrillation while atrial flutter (AFL) and atrial tachycardia (AT) were the subjects of relatively few AI studies. Therefore, the aim of this review is to investigate the interaction of AI algorithms, electrophysiological characteristics, imaging data, risk score calculators, and clinical variables in predicting cardiac arrhythmias following an ablation procedure. This review will also discuss the implementation of these algorithms to enable the detection and prediction of AFL and AT recurrences following CA.

Early Echocardiographic Predictors for Atrial Fibrillation Propensity: The Left Atrium Oracle

Atrial fibrillation (AF) results from structural and electrical remodeling of the atria, primarily of the left atrium (LA); therefore, LA changes, both anatomical and functional are recognized as proarrhythmic markers with a powerful prognostic value. Being widely available and noninvasive, echocardiography is used to monitor LA form and function in clinical practice. Early functional (electrical) remodeling of the LA precedes anatomical alterations. Impaired LA functions and reduced atrial compliance due to atrial fibrosis may be evaluated using novel echocardiographic techniques, such as tissue Doppler analysis and speckle tracking. Functional evaluation of the LA conveys prognostic information about the risk of AF, as the severity of the impairment is an independent predictor of new-onset AF and AF recurrence. However, specific parameters are still investigated for incorporation into algorithms to predict future AF occurrence. The aim of the review is to summarize echocardiographic parameters, their predicting value and applicability in practice.

Prediction of Atrial Fibrillation Using Machine Learning: A Review

There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.