Effectiveness and Safety of Direct Oral Anticoagulants Versus Warfarin in Patients with Atrial Fibrillation and Cancer: A Target Trial Emulation from SEER-Medicare Database.

BACKGROUND: Direct oral anticoagulants (DOACs) are preferred over warfarin in patients with atrial fibrillation (AFib). However, their safety and effectiveness in patients with AFib and cancer are inconclusive. METHODS: We conducted a retrospective cohort study by emulating a target trial. Patients with a record of cancer (breast, prostate, or lung), newly diagnosed with AFib initiated DOACs or warfarin within 3 months after AFib diagnosis from the 2012-2019 Surveillance, Epidemiology, and End Results (SEER)-Medicare database were included. We compared the risk of ischemic stroke, major bleeding, and secondary outcomes (venous thromboembolism, intracranial bleeding, gastrointestinal bleeding, and non-critical site bleeding) between patients who initiated DOACs and warfarin. Inverse probability treatment weights and inverse probability censoring weights were used to adjust imbalanced patient and disease characteristics and loss to follow-up between the two groups. Weighted pooled logistic regression were used to estimate treatment effect with hazard ratios (HRs) with 95% confidence interval (95% CIs). RESULTS: The incidence rates of stroke and major bleeding between DOAC and warfarin initiators were 9.97 vs. 9.91 and 7.74 vs. 9.24 cases per 1000 person-years, respectively. In adjusted intention-to-treat analysis, patients initiated DOACs had no statistically significant difference in risk of ischemic stroke (HR = 0.87, 95% CI 0.52-1.44) and major bleeding (HR = 1.14, 95% CI 0.77-1.68) compared to those initiated warfarin. In adjusted per-protocol analysis, there was no statistical difference in risk of ischemic stroke (HR = 1.81, 95% CI 0.75-4.36) and lower risk for major bleeding, but the 95% CI was wide (HR = 0.35, 95% CI 0.12-0.99) among DOAC initiators compared to warfarin initiators. The benefits in secondary outcomes were in favor of DOACs. The findings remained consistent across subgroups and sensitivity analyses. CONCLUSION: DOACs are safe and effective alternatives to warfarin in the management of patients with AFib and cancer.

Benefit and risk of oral anticoagulant initiation strategies in patients with atrial fibrillation and cancer: a target trial emulation using the SEER-Medicare database.

Oral anticoagulants (OACs) are recommended for patients with atrial fibrillation (AFib) having CHA2DS2-VASc score ≥ 2. However, the benefits of OAC initiation in patients with AFib and cancer at different levels of CHA2DS2-VASc is unknown. We included patients with new AFib diagnosis and a record of cancer (breast, prostate, or lung) from the 2012-2019 Surveillance, Epidemiology, and End Results (SEER)-Medicare database (n = 39,915). Risks of stroke and bleeding were compared between 5 treatment strategies: (1) initiated OAC when CHA2DS2-VASc ≥ 1 (n = 6008), (2) CHA2DS2-VASc ≥ 2 (n = 8694), (3) CHA2DS2-VASc ≥ 4 (n = 20,286), (4) CHA2DS2-VASc ≥ 6 (n = 30,944), and (5) never initiated OAC (reference group, n = 33,907). Confounders were adjusted using inverse probability weighting through cloning-censoring-weighting approach. Weighted pooled logistic regressions were used to estimate treatment effect [hazard ratios (HRs) and 95% confidence interval (95% CIs)]. We found that only patients who initiated OACs at CHA2DS2-VASc ≥ 6 had lower risk of stroke compared without OAC initiation (HR 0.64, 95% CI 0.54-0.75). All 4 active treatment strategies had reduced risk of bleeding compared to non-initiators, with OAC initiation at CHA2DS2-VASc ≥ 6 being the most beneficial strategy (HR = 0.49, 95% CI 0.44-0.55). In patients with lung cancer or regional/metastatic cancer, OAC initiation at any CHA2DS2-VASc level increased risk of stroke and did not reduce risk of bleeding (except for Regimen 4). In conclusion, among cancer patients with new AFib diagnosis, OAC initiation at higher risk of stroke (CHA2DS2-VASc score ≥ 6) is more beneficial in preventing ischemic stroke and bleeding. Patients with advanced cancer or low life-expectancy may initiate OACs when CHA2DS2-VASc score ≥ 6.

Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning.

There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. In this study, we evaluate the performance of time domain (TD) and frequency domain (FD) convolutional neural network (CNN) classification models in an inter-institutional scenario leveraging three different publicly available datasets. The larger PTB-XL ECG dataset was used to initially train TD and FD CNN models for atrial fibrillation (AFIB) classification. The models were then tested on two different data sets, Lobachevsky University Electrocardiography Database (LUDB) and Korea University Medical Center database (KURIAS). The FD model was able to retain most of its performance (>0.81 F1-score), whereas TD was highly affected (<0.53 F1-score) by the dataset variations, even with TL applied. The FD CNN showed superior robustness to cross-institutional variability and has potential for widespread application with no compromise to ECG classification performance.

Fourier space approach for convolutional neural network (CNN) electrocardiogram (ECG) classification: A proof-of-concept study.

There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching > 85% accuracy for various cardiac pathologies. Although the accuracy within institutions might be high, models trained at one institution might not be generalizable enough for accurate detection when deployed in other institutions due to differences in type of signal acquisition, sampling frequency, time of acquisition, device noise characteristics and number of leads. In this proof-of-concept study, we leverage the publicly available PTB-XL dataset to investigate the use of time-domain (TD) and frequency-domain (FD) convolutional neural networks (CNN) to detect myocardial infarction (MI), ST/T-wave changes (STTC), atrial fibrillation (AFIB) and sinus arrhythmia (SARRH). To simulate interinstitutional deployment, the TD and FD implementations were also compared on adapted test sets using different sampling frequencies 50 Hz, 100 Hz and 250 Hz, and acquisition times of 5 s and 10s at 100 Hz sampling frequency from the training dataset. When tested on the original sampling frequency and duration, the FD approach showed comparable results to TD for MI (0.92 FD - 0.93 TD AUROC) and STTC (0.94 FD - 0.95 TD AUROC), and better performance for AFIB (0.99 FD - 0.86 TD AUROC) and SARRH (0.91 FD - 0.65 TD AUROC). Although both methods were robust to changes in sampling frequency, changes in acquisition time were detrimental to the TD MI and STTC AUROCs, at 0.72 and 0.58 respectively. Alternatively, the FD approach was able to maintain the same level of performance, and, therefore, showed better potential for interinstitutional deployment.

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.

Association between electrocardiographic features and mortality in COVID-19 patients.

BACKGROUND: Cardiovascular events have been reported in the setting of coronavirus disease-19 (COVID-19). It has been hypothesized that systemic inflammation may aggravate arrhythmias or trigger new-onset conduction abnormalities. However, the specific type and distribution of electrocardiographic disturbances in COVID-19 as well as their influence on mortality remain to be fully characterized. METHODS: Electrocardiograms (ECGs) were obtained from 186 COVID-19-positive patients at a large tertiary care hospital in Northern Nevada. The following arrhythmias were identified by cardiologists: sinus bradycardia, sinus tachycardia, atrial fibrillation (A-Fib), atrial flutter, multifocal atrial tachycardia (MAT), premature atrial contraction (PAC), premature ventricular contraction (PVC), atrioventricular block (AVB), and right bundle branch block (RBBB). The mean PR interval, QRS duration, and corrected QT interval were documented. Fisher's exact test was used to compare the ECG features of patients who died during the hospitalization with those who survived. The influence of ECG features on mortality was assessed with multivariable logistic regression analysis. RESULTS: A-Fib, atrial flutter, and ST-segment depression were predictive of mortality. In addition, the mean ventricular rate was higher among patients who died as compared to those who survived. The use of therapeutic anticoagulation was associated with reduced odds of death; however, this association did not reach statistical significance. CONCLUSION: The underlying pathogenesis of COVID-19-associated arrhythmias remains to be established, but we postulate that systemic inflammation and/or hypoxia may induce potentially lethal conduction abnormalities in affected individuals. Longitudinal studies are warranted to evaluate the risk factors, pathogenesis, and management of COVID-19-associated cardiac arrhythmias.

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study.

BACKGROUND: Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. OBJECTIVE: The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars. METHODS: We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA2DS2-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA2DS2­VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke. RESULTS: The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion. CONCLUSIONS: Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Atrial fibrillation in ß-thalassemia Major Patients: Diagnosis, Management and Therapeutic Options.

The prevalence of atrial fibrillation (AFib) in ß-thalassemia major (ß-TM) patients has increased in the last few years, reaching up to 33.0%. Several factors may drive this value to even more in the next few years. We summarized the main challenges in the management and therapy of AFib in this very specific group of patients.

Comparison of Weight-Based Dose vs. Standard Dose Diltiazem in Patients with Atrial Fibrillation Presenting to the Emergency Department.

BACKGROUND: Despite evidence-based recommended weight-based (WB) dosing of diltiazem for the initial treatment of atrial fibrillation (AF) with rapid ventricular response (RVR), many providers utilize lower initial doses of diltiazem. OBJECTIVE: We sought to determine whether a low, standard dose of diltiazem is noninferior to WB diltiazem as an initial bolus dose in the treatment of AF with RVR. METHODS: This retrospective review included patients who presented to the emergency department (ED) of an urban, academic tertiary medical center experiencing AF with RVR from November 2010 to August 2014. Adult patients were categorized by the dose of diltiazem received; 10 mg standard dose or 0.2-0.3 mg/kg WB dose. The primary outcome of successful treatment was defined as a composite of the following parameters 15 min after the initial bolus dose: heart rate (HR) < 100 beats/min, reduction of HR ≥ 20%, or a conversion to normal sinus rhythm. RESULTS: Four hundred and fifty-six patients who received diltiazem were included for study evaluation (standard dose: n = 255 patients, WB: n = 201 patients). Baseline characteristics, medical history, and medication use before ED presentation were similar between the groups. Significant differences at baseline between the groups included weight and HR at presentation. The primary outcome of successful treatment was attained in 60.8% of the standard dose patients and 68.7% of the WB patients (p = 0.082). CONCLUSIONS: In patients presenting to the ED, we found that standard dose diltiazem was noninferior to WB dosing in the initial treatment of AF with RVR.

Your Father and Grandfather's Atrial Fibrillation: A Review of the Genetics of the Most Common Pathologic Cardiac Dysrhythmia.

Atrial fibrillation (AF) remains the most common pathologic dysrhythmia in humans with a prevalence of 1-2% of the total population and as high as 10% of the elderly. AF is an independent risk marker for cardiovascular mortality and morbidity, and given the increasing age of the population, represents an increasing burden of disease. Although age and hypertension are known risk factors for development of AF, the study of families with early onset AF revealed mutations in genes coding for ion channels and other proteins involved in electrotonic coupling as likely culprits for the pathology in select cases. Recent investigations using Genome-Wide Association Studies have revealed several single nucleotide polymorphisms (SNPs) that appear to be associated with AF and have highlighted new genes in the proximity of the SNPs that may potentially contribute to the development of the dysrhythmia. Here we review the genetics of AF and discuss how application of GWAS and next generation sequencing have advanced our knowledge of AF and further investigations may yield novel therapeutic targets for the disease.

Should the Left Atrial Appendage Closure (LAAC) Technique Be the Main Form of Stroke Prevention in Patients With Long-Standing Persistent or Permanent Atrial Fibrillation?

Currently, oral anticoagulants are considered the gold standard for stroke prevention in patients with atrial fibrillation. Despite the efficacy of oral anticoagulants in reducing stroke incidence, patients are at risk of developing adverse reactions such as excessive bleeding and bruising, and can also have drug-drug interactions. In the early 2000s, a minimally invasive technique called the left atrial appendage closure emerged as an alternative for stroke prevention in atrial fibrillation patients who could not tolerate oral anticoagulants. Despite the success of the left atrial appendage closure, practitioners still opt for medication therapy and are reluctant to advocate for this procedure. Given the adverse effects of oral anticoagulants, physicians should question if this is the appropriate method of stroke prevention in long-standing persistent or permanent atrial fibrillation patients. This case report investigates an 82-year-old Middle Eastern male in the United States with long-standing persistent atrial fibrillation who underwent a left atrial appendage closure due to recurrent bleeding on oral anticoagulants. In addition, there will be further discussion on the appropriate method of stroke prevention in similar patients.

Development and Validation of Machine Learning Algorithms to Predict 1-Year Ischemic Stroke and Bleeding Events in Patients with Atrial Fibrillation and Cancer.

In this study, we leveraged machine learning (ML) approach to develop and validate new assessment tools for predicting stroke and bleeding among patients with atrial fibrillation (AFib) and cancer. We conducted a retrospective cohort study including patients who were newly diagnosed with AFib with a record of cancer from the 2012-2018 Surveillance, Epidemiology, and End Results (SEER)-Medicare database. The ML algorithms were developed and validated separately for each outcome by fitting elastic net, random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and neural network models with tenfold cross-validation (train:test = 7:3). We obtained area under the curve (AUC), sensitivity, specificity, and F2 score as performance metrics. Model calibration was assessed using Brier score. In sensitivity analysis, we resampled data using Synthetic Minority Oversampling Technique (SMOTE). Among 18,388 patients with AFib and cancer, 523 (2.84%) had ischemic stroke and 221 (1.20%) had major bleeding within one year after AFib diagnosis. In prediction of ischemic stroke, RF significantly outperformed other ML models [AUC (0.916, 95% CI 0.887-0.945), sensitivity 0.868, specificity 0.801, F2 score 0.375, Brier score = 0.035]. However, the performance of ML algorithms in prediction of major bleeding was low with highest AUC achieved by RF (0.623, 95% CI 0.554-0.692). RF models performed better than CHA2DS2-VASc and HAS-BLED scores. SMOTE did not improve the performance of the ML algorithms. Our study demonstrated a promising application of ML in stroke prediction among patients with AFib and cancer. This tool may be leveraged in assisting clinicians to identify patients at high risk of stroke and optimize treatment decisions.

Impact of Sinus Rhythm Restoration on Mitral Regurgitation in Patients With Atrial Fibrillation: A Comparative Echocardiographic Study.

INTRODUCTION: Mitral regurgitation (MR) is a common valvular abnormality that can be exacerbated by atrial fibrillation (AF). Mitral regurgitation is classified based on mitral leaflet motion and can be either primary (organic) or secondary (functional). This study investigates the relationship between AF and functional MR, specifically assessing whether cardioversion to sinus rhythm influences MR severity and echocardiographic indices. METHODS: This retrospective cohort study included 417 patients with AF and significant MR (Grade III or IV) who were hospitalized for cardioversion. Patients underwent echocardiography during AF and again within six months after successful cardioversion. The primary outcome was the change in MR severity post cardioversion. Secondary outcomes included changes in left atrial size, left ventricular ejection fraction, and mitral valve anatomy. Statistical analysis involved chi-square tests for categorical variables, Student's t-tests for continuous variables, and multivariate logistic regression to assess the impact of sinus rhythm restoration on MR severity. RESULTS: Of the 417 patients, 61% (n = 254) returned to sinus rhythm after cardioversion. Among these, 28.4% (n = 72) showed a two-grade or greater improvement in MR severity. Patients who returned to sinus rhythm also exhibited significant reductions in left ventricular and left atrial dimensions and improved transvalvular gradients. The multivariate analysis indicated that conversion to sinus rhythm was independently associated with MR improvement. CONCLUSIONS: Restoration of sinus rhythm is associated with significant improvement in functional MR, potentially reducing the need for surgical intervention. These findings suggest that rhythm control should be considered in managing patients with AF and significant MR, as it may lead to improved cardiac function and better clinical outcomes. Further large-scale prospective studies are needed to determine the most effective management strategy for functional MR in the context of AF.

Bradycardia, Renal Failure, Atrioventricular Nodal Block, Shock, and Hyperkalemia (BRASH) Syndrome-Induced Atrial Fibrillation: A Case Report.

BRASH syndrome is a syndrome that comprises bradycardia, renal failure, atrioventricular nodal block, shock, and hyperkalemia. This syndrome is usually associated with a junctional rhythm. Early recognition of this clinical entity is crucial for appropriate management. In this case report, we describe a 70-year-old female who presented with BRASH syndrome-induced atrial fibrillation with a slow ventricular response.

Neurological Consequences of Cardiac Arrhythmias: Relationship Between Stroke, Cognitive Decline, and Heart Rhythm Disorders.

Cardiac arrhythmias are one of the most common disorders with high morbidity and mortality. The effect of cardiac arrhythmias on the brain is very pronounced due to the high sensitivity of the brain to oxygen and blood supply. This mortality is preventable by early diagnosis and treatment which improves the patient's quality of life. Intervening at the right time, post arrhythmia is significant in preventing deaths and improving patient outcomes. Multiple pathophysiological mechanisms are studied for the brain-axis implications, that have the potential to be targeted by novel therapies. In this review, we describe the pathophysiological mechanisms and recent advances in detail to understand the functional aspects of the brain-heart axis and neurological implications post-stroke, caused by cardiac disorders.  This paper aims to discuss the current literature on the neurological consequences of cardiac arrhythmias and delve into a deeper understanding of the brain-heart axis, imbalances, and decline, with the aim of summarizing everything and all about the neurological consequences of cardiac arrhythmias.

Outcomes of Patients with Monoclonal Gammopathy of Undetermined Significance with and without Atrial Fibrillation: A Retrospective Cohort Analysis of the Nationwide Inpatient Sample Database.

BACKGROUND: Monoclonal gammopathy of undetermined significance (MGUS) is a non-malignant precursor of multiple myeloma (MM). MGUS has been suggested to be associated with a higher risk of cardiovascular diseases, including AFIB, but it is still unclear whether this association is real. Studies are lacking on the impact of atrial fibrillation on health outcomes in this population. The association of AFIB in this population is lagging and merits further investigation. METHODS: The study conducted a retrospective analysis of the Nationwide Inpatient Sample (NIS) for 2018, including adult patients with primary diagnoses of MGUS and AFIB. Patients were divided into two groups based on AFIB presence. Outcomes assessed included complications, length of stay, mortality, hospital charges, and discharge disposition. RESULTS: The study included 9007 patients with MGUS of whom 2404 had AFIB. Patients with both MGUS and AFIB had higher rates of acute kidney injury [AKI] (31.5% vs. 27.5%; p = 0.002) and pericarditis (2% vs. 1.2%; p = 0.029). They also had longer hospital stays (5 vs. 4 days; p < 0.001) and higher hospitalization costs ($43,729 vs. $41,169; p < 0.001). CONCLUSIONS: The study showed that the prevalence of AFIB in MGUS patients is high. Patients with AFIB had increased rates of complications (AKI and pericarditis) and higher mortality compared to patients without AFIB. Further studies screening for AFIB in this patient population are warranted.

Solutions for Atrial Fibrillation Edvocacy (SAFE): Improving Awareness And Access To Afib Screening For Detection And Referral For Treatment.

Background: Atrial Fibrillation (Afib) can lead to stroke and heart failure, and early detection of Afib is an effective method of preventing these life-threatening conditions. An estimated 2.7 million Americans are living with Afib1, a number that is expected to rise dramatically in the coming years. Methods: The aim of this demonstration project was to create an additional access point in the community at local pharmacies for Afib screening, detection, and referral to physicians for follow-up and initiation of evidence-based therapy when appropriate. This prospective research study was conducted with 14 community pharmacies across the US, in which a total of 650 patients were screened for Afib. Pharmacists conducted SAFEty Risk Assessments that consisted of completion of a Stroke Risk Scorecard and EKG determination utilizing AliveCor's KardiaMobile® 6L device. Results: In 552 (82.5%) of 669 total EKG readings, a "normal" rhythm was detected, and in 117 (17.5%) EKG readings an abnormal detection occurred. A total of 12 out of 650 patients (1.8%) received EKG readings of Afib, which is greater than double the expected prevalence of Afib in the US (0.81%), a statistically significant finding (p < 0.0001). Other notable findings included 42 (6.3%) EKG readings of Wide QRS, and 26 (3.9%) EKG readings of tachycardia. A total of 44 patients were referred to physicians for follow-up on their risk for Afib. Conclusions: Community pharmacies offer a unique, valuable access point for patients to receive Afib screenings. Pharmacists are well positioned to make a significant contribution in the cardiovascular health of their patients and increase the value of team-based health care.

Paroxysmal Atrial Fibrillation (PFA) Detection With Cardiac Monitoring Devices Implanted by Neurologists in Patients With Embolic Strokes of Undetermined Source (ESUS).

Introduction Ischemic stroke is among the leading causes of death and disability. Approximately 50% of cryptogenic strokes are embolic strokes of undetermined source (ESUS). The most common cause of ESUS is atrial fibrillation. Therefore, the detection of atrial fibrillation with long-term implantable devices is needed. Neurologists are directly involved with acute and post-acute stroke care and have direct access to the management of stroke patients. Therefore, there is a need for neurologists to recommend, implant, and monitor cardiac implantable devices in patients with ESUS. Methods From November 2022 to October 2023, our group implanted 32 ESUS patients with Confirm Rx™ insertable cardiac monitors (Abbott, USA). Atrial fibrillation detection was supervised and monitored daily. Results In 24 months, atrial fibrillation was detected in 12.5% of patients (four patients), sinus bradycardia in 6.25% of patients (two patients), paroxysmal supraventricular tachycardia in 9.4% of patients (three patients), and asystole in one patient. Conclusion Our study shows that neurologists involved in the treatment of stroke care can safely implant, monitor, and detect atrial fibrillation accurately. Our rate of detection of atrial fibrillation in patients with ESUS was 12.8%, which is consistent with prior studies.

Efficacy and Safety of Novel Oral Anticoagulants in Atrial Fibrillation: A Systematic Review.

In recent times, novel oral anticoagulants (NOACs)/direct oral anticoagulants (DOACs) have emerged as an alternative to the traditionally used Vitamin K oral antagonists (VKA) like warfarin for the treatment of atrial fibrillation (AF). This systematic review and meta-analysis aims to evaluate the efficacy and safety of NOACs in patients with AF and, thus, the related thromboembolic risks and sequelae. Of the 131 published articles we examined, 11 were included in an in-depth systematic review. The articles we reviewed were from the past ten years, from 2013 onward. The analysis derived the efficacy and safety of NOACs in patients with AF and also included different patients' baseline characteristics and subgroups. This systematic review reiterates previous research findings of superior efficacy and safety of the use of NOACs in the AF population and also illuminates certain head-to-head comparisons of individual NOACs with warfarin. It digressed into subgroups of patients with different baseline characteristics to provide evidence and support the existing guidelines for the use of NOACs in the treatment of AF. Overall, there is marked efficacy and safety of NOACs in patients with AF, be they elderly or Asian, with decreased renal function, or with other comorbidities. Adherence to NOACs was also satisfactory. Despite such a review, there needs to be more research on vast subgroups and also on reversal antidotes like andexanet alfa and idarucizumab, as well as more head-to-head analysis between NOACs over a long duration of study, which would provide more answers and pinpoint reasons as to the differences that exist between demographics and subgroups in the usage of NOACs.

Managing Stroke Prevention in Hypertrophic Obstructive Cardiomyopathy (HOCM) Patients Without Confirmed Persistent Atrial Fibrillation: A Dilemma of Anticoagulation.

Hypertrophic obstructive cardiomyopathy (HOCM) is a genetic cardiovascular disorder characterized by the thickening of the heart muscle, particularly the left ventricle. It is a leading cause of sudden cardiac death in young individuals. HOCM is associated with various complications, including arrhythmias and an increased risk of stroke. Patients with HOCM are at an increased risk of stroke due to the development of atrial fibrillation (AFib), a common arrhythmia observed in HOCM. AFib can result in the formation of blood clots in the atria, which may subsequently embolize the brain, causing a stroke. However, not all HOCM patients develop persistent AFib, leading to uncertainty regarding the appropriate management of stroke prevention in these cases. This case study aims to explore the management of recurrent cerebrovascular events (CVA) in a patient with HOCM who does not have confirmed persistent AFib. The argument revolves around whether anticoagulation should be offered for secondary stroke prevention in HOCM patients without a confirmed diagnosis of persistent AFib.