Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events

From Toxin to Treatment: A Narrative Review on the Use of Botulinum Toxin for Autonomic Dysfunction

Since its regulatory approval over a half-century ago, botulinum toxin has evolved from one of the most potent neurotoxins known to becoming routinely adopted in clinical practice. Botulinum toxin, a highly potent neurotoxin produced by Clostridium botulinum, can cause botulism illness, characterized by widespread muscle weakness due to inhibition of acetylcholine transmission at neuromuscular junctions. The observation of botulinum toxin's anticholinergic properties led to the investigation of its potential benefits for conditions with an underlying etiology of cholinergic transmission, including autonomic nervous system dysfunction. These conditions range from disorders of the integument to gastrointestinal and urinary systems. Several formulations of botulinum toxin have been developed and tested over time, significantly increasing the availability of this treatment for appropriate clinical use. Despite the accelerated and expanded use of botulinum toxin, there lacks an updated comprehensive review on its therapeutic use, particularly to treat autonomic dysfunction. This narrative review provides an overview of the effect of botulinum toxin in the treatment of autonomic dysfunction and summarizes the different formulations and dosages most widely studied, while highlighting reported outcomes and the occurrence of any adverse events.

[Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management]

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Early diagnosis and effective management are important to reduce atrial fibrillation-related adverse events. Photoplethysmography (PPG) is often used to assist wearables for continuous electrocardiograph monitoring, which shows its unique value. The development of PPG has provided an innovative solution to AF management. Serial studies of mobile health technology for improving screening and optimized integrated care in atrial fibrillation have explored the application of PPG in screening, diagnosing, early warning, and integrated management in patients with AF. This review summarizes the latest progress of PPG analysis based on artificial intelligence technology and mobile health in AF field in recent years, as well as the limitations of current research and the focus of future research.

A Real-World Evaluation of Primary Medication Nonadherence in Patients with Nonvalvular Atrial Fibrillation Prescribed Oral Anticoagulants in the United States

BACKGROUND

Nonadherence to oral anticoagulants (OACs) is a challenge to stroke risk reduction in patients with nonvalvular atrial fibrillation (NVAF). Data on primary medication nonadherence (PMN) in NVAF are lacking.

OBJECTIVES

Our aim was to assess the rates and predictors of PMN among NVAF patients who were newly prescribed an OAC.

METHODS

This was a retrospective database analysis of linked healthcare claims and electronic health record data. Adult NVAF patients with a prescription order for an OAC (apixaban, rivaroxaban, dabigatran, or warfarin) between January 2016 and June 2019 were identified (date of first prescription order = index date). Patients had a 1-year baseline and a 6-month post-index period to assess the rates of PMN, defined as having a prescription order but no paid claim for any OAC on or within 30 days after the index date. Sensitivity analyses explored 60-, 90- and 180-day PMN thresholds. Logistic regression models were used to examine the predictors of PMN.

RESULTS

Among 20,393 patients, the overall 30-day PMN rate was 28.4%; PMN rates decreased to 17% with a 180-day threshold. PMN was numerically lowest for warfarin among OACs and numerically lowest for apixaban among direct OACs. A CHA2DS2-VASc score of ≥ 3, commercial insurance, and African American race were associated with higher odds of PMN.

CONCLUSIONS

More than one-quarter of patients experienced PMN within 30 days of their initial prescription order. This rate decreased over a longer period, suggesting a delay in fills. Understanding the factors associated with PMN is warranted to develop effective interventions for improving OAC treatment rates in NVAF.

A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection

Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in "irregularly irregular" heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths.

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F_1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.

Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study

Background

Some attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions.

Objective

This study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device.

Methods

A total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch.

Results

One of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve.

Conclusions

We were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis

Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system's limitations, both in terms of statistical performance as well as recognizing situations for which the system's predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. We focus on electrocardiogram (ECG) analysis as an example domain in which AI has great potential and tackle two challenging tasks: the detection of a heterogeneous mix of known and unknown arrhythmias from ECG and the identification of underlying cardio-pathology from segments annotated as normal sinus rhythm recorded in patients with an intermittent arrhythmia. We validate our methods by simulating a screening for arrhythmias in a large-scale population while adhering to statistical significance requirements. Specifically, our system 1) visualizes the relative importance of each part of an ECG segment for the final model decision; 2) upholds specified statistical constraints on its out-of-sample performance and provides uncertainty estimation for its predictions; 3) handles inputs containing unknown rhythm types; and 4) handles data from unseen patients while also flagging cases in which the model's outputs are not usable for a specific patient. This work represents a significant step toward overcoming the limitations currently impeding the integration of AI into clinical practice in cardiology and medicine in general.