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.

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.

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.

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.

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.

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.

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.

The Synergistic Relationship Between Atrial Fibrillation and Diabetes Mellitus: Implications for Cardiovascular and Metabolic Health.

Type 2 diabetes mellitus (T2DM) and atrial fibrillation (AF) are widespread chronic conditions that profoundly impact public health. While the intricate mechanisms linking these two diseases remain incompletely understood, this review sets out to comprehensively analyze the current evidence about their pathophysiology, epidemiology, diagnosis, prognosis, and treatment. We reveal that T2DM can influence the electrical and structural properties of the atria through multiple pathways, including oxidative stress, inflammation, fibrosis, connexin remodeling, glycemic variability, and autonomic dysfunction. Moreover, it significantly influences AF's clinical course, elevating the risk of heart failure, stroke, and cardiovascular mortality. Our review also explores treatment options for individuals with T2DM and AF, encompassing antidiabetic and antiarrhythmic drugs and non-pharmacological interventions, such as cardioversion catheter ablation and direct current cardioversion. This review depicts an insight into the clinical interplay between T2DM and AF. It deepens our comprehension of the fundamental mechanisms, potential therapeutic interventions, and their implications for patient care. This comprehensive resource benefits researchers seeking to deepen their knowledge in this domain. Ultimately, our findings pave the way for more effective strategies in managing AF within the context of T2DM.

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.

A Retrospective Review of 30-Day Hospital Readmission Risk After Open Heart Surgery in Patients With Atrial Fibrillation.

Introduction Readmission rates after open heart surgery (OHS) remain an important clinical issue. The causes are varied, with identifying risk factors potentially providing valuable information to reduce healthcare costs and the rate of post-operative complications. This study aimed to characterize the reasons for 30-day hospital readmission rates of patients after open heart surgery. Methods All patients over 18 years of age undergoing OHS at a community hospital from January 2020 through December 2020 were identified. Demographic data, medical history, operative reports, post-operative complications, and telehealth interventions were obtained through chart review. Descriptive statistics and readmission rates were calculated, along with a logistic regression model, to understand the effects of medical history on readmission. Results A total of 357 OHS patients met the inclusion criteria for the study. Within the population, 8.68% of patients experienced readmission, 10.08% had an emergency department (ED) visit, and 95.80% had an outpatient office visit. A history of atrial fibrillation (AFib) significantly predicted 30-day hospital readmissions but not ED or outpatient office visits. Telehealth education was delivered to 66.11% of patients. Conclusion The study investigated factors associated with 30-day readmission following OHS. AFib patients were more likely to be readmitted than patients without atrial fibrillation. No other predictors of readmission, ED visits, or outpatient office visits were found. Patients reporting symptoms of tachycardia, pain, dyspnea, or "other" could be at increased risk for readmission.

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.

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.

Incidence and Clinical Outcomes of New-Onset Atrial Fibrillation in Critically lll Patients with COVID-19: A Multicenter Cohort Study - New-Onset Atrial Fibrillation and COVID-19.

Atrial fibrillation (Afib) can contribute to a significant increase in mortality and morbidity in critically ill patients. Thus, our study aims to investigate the incidence and clinical outcomes associated with the new-onset Afib in critically ill patients with COVID-19. A multicenter, retrospective cohort study includes critically ill adult patients with COVID-19 admitted to the intensive care units (ICUs) from March, 2020 to July, 2021. Patients were categorized into two groups (new-onset Afib vs control). The primary outcome was the in-hospital mortality. Other outcomes were secondary, such as mechanical ventilation (MV) duration, 30-day mortality, ICU length of stay (LOS), hospital LOS, and complications during stay. After propensity score matching (3:1 ratio), 400 patients were included in the final analysis. Patients who developed new-onset Afib had higher odds of in-hospital mortality (OR 2.76; 95% CI: 1.49-5.11, P = .001). However, there was no significant differences in the 30-day mortality. The MV duration, ICU LOS, and hospital LOS were longer in patients who developed new-onset Afib (beta coefficient 0.52; 95% CI: 0.28-0.77; P < .0001,beta coefficient 0.29; 95% CI: 0.12-0.46; P < .001, and beta coefficient 0.35; 95% CI: 0.18-0.52; P < .0001; respectively). Moreover, the control group had significantly lower odds of major bleeding, liver injury, and respiratory failure that required MV. New-onset Afib is a common complication among critically ill patients with COVID-19 that might be associated with poor clinical outcomes; further studies are needed to confirm these findings.

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.

Coronavirus Disease 2019 Associated Acute Myocardial Infarction and Atrial Fibrillation: A Case Report.

In the last two years since the inception of the Coronavirus pandemic, there have been a myriad of reports and studies related to Coronavirus disease 2019 (COVID-19). We present a unique case of COVID-19 associated with both acute myocardial infarction and new-onset atrial fibrillation (AFIB) in an elderly lady which is the first reported case to the best of our knowledge. The patient was symptomatic with acute COVID-19 and developed a type 2 myocardial infarction with new-onset AFIB. The patient also developed sepsis which may have contributed to the development of AFIB.

A Case of Left-Sided Infective Endocarditis of Multiple Native Valves Complicated by Valve Leaflet Perforation, Multiple Septic Emboli, Thromboembolic Events, and Cardiac Arrhythmias.

Coagulase-negative staphylococci (CoNS) can uncommonly cause native valve endocarditis. We present a case of left-sided infective endocarditis of native valves presenting with splenic, lung, and brain infarcts along with aortic and significant mitral valve involvement with mitral valve perforation. The patient was also found to be in atrial flutter and atrial fibrillation. Left-sided endocarditis is reported to cause brain and spleen infarcts but pulmonary embolisms are usually a complication of right-sided endocarditis. Atrial fibrillation is also known to increase mortality in patients with infective endocarditis.

Automated Atrial Fibrillation Detection with ECG.

An electrocardiography system records electrical activities of the heart, and it is used to assist doctors in the diagnosis of cardiac arrhythmia such as atrial fibrillation. This study presents a fast, automated deep-learning algorithm that predicts atrial fibrillation with excellent performance (F-1 score 88.2% and accuracy 97.3%). Our approach involves the pre-processing of ECG signals, followed by an alternative representation of the signals using a spectrogram, which is then fed to a fine-tuned EfficientNet B0, a pre-trained convolution neural network model, for the classification task. Using the transfer learning approach and with fine-tuning of the EfficientNet, we optimize the model to achieve highly efficient and effective classification of the atrial fibrillation.