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A novel Neuro-Bio-Electric-Stimulation device (Neubie, Neufit, Austin, Texas, USA) using Direct Current (DC) has been used to treat various neurological conditions (ALS, MS, peripheral neuropathy, chronic pain) and functional limitations such as limited range of motion. One method, called the Master Reset Protocol, is thought to stimulate the vagus nerve system, impacting heart rate, digestion and other vital systems. We used the Master Reset Protocol on a subject experiencing paroxysmal Atrial Fibrillation (AFib) to assess whether this treatment might be effective in reversing a cardiac arrhythmia. A single subject is reported in this Case Report. The subject is a 62-year-old healthy, athletic male, 6'2″ tall, 165 lbs. with a good diet and is not obese nor has other exacerbating underlying conditions related to heart disease. The subject experiences arrhythmia approximately 1-2 times per month lasting generally 3 or more days per the subject. The Master Reset Method was initiated within 12 hours of arrhythmia onset, and arrhythmia before and after treatment was confirmed through subject observation and confirmed with pulse readings. A total of ten treatments were conducted over 7 months. Reversal of arrhythmia was confirmed during or within 24 hours of treatment with DC application for all 10 treatments (100%). Two of the more severe cases of AFib required two treatments on the same day with confirmed reversal of AFib. Treatment with Direct Current suggests a good correlation with reversal of arrhythmia. Further studies are planned to determine if similar, regular, treatments can be effective in preventing arrhythmia.

Since the clinical use of digitalis as the first pharmacological therapy for atrial fibrillation (AF) 235 years ago in 1785, antiarrhythmic drug therapy has advanced considerably and become a cornerstone of AF clinical management. Yet, a preventive or curative panacea for sustained AF does not exist despite the rise of AF global prevalence to epidemiological proportions. While multiple elevated risk factors for AF have been established, the natural history and etiology of AF remain incompletely understood. In the present article, the first section selectively highlights some disappointing shortcomings and current efforts in antiarrhythmic drug therapy to uncover reasons why AF is such a clinical challenge. The second section discusses some modern takes on the natural history of AF as a relentless, progressive fibro-inflammatory \"atriomyopathy.\" The final section emphasizes the need to redefine therapeutic strategies on par with new insights of AF pathophysiology.

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. 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. 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 (CHA DS -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 CHA DS ‑VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke. The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA DS -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. 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 (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.

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.

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.

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.

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, = .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; < .0001,beta coefficient 0.29; 95% CI: 0.12-0.46; < .001, and beta coefficient 0.35; 95% CI: 0.18-0.52; < .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.

Background The advantage of catheter ablation compared to medical therapy for atrial fibrillation (AF) in patients diagnosed with heart failure with preserved ejection fraction (HFpEF) remains indeterminate. A meta‐analysis was conducted to assess outcomes within this population. Methods We searched MEDLINE, Embase, Cochrane CENTRAL, and ClinicalTrials.gov through May 2025. Twelve studies satisfied the inclusion criteria, encompassing a total sample size of 43 584 individuals. Outcomes included primary composite endpoints, HF hospitalizations, all‐cause mortality, AF recurrence, cardiovascular (CV) mortality, all‐cause hospitalization, and stroke. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using random‐effects models; heterogeneity was assessed using I2 statistics. Publication bias and sensitivity analyses were also performed. Results Catheter ablation notably reduced the primary composite endpoint (HR, 0.53; 95% CI, 0.41–0.68; p < 0.01) and hospitalizations due to heart failure (HR, 0.62; 95% CI, 0.48–0.81; p < 0.01). The overall mortality rate was reduced with ablation (HR, 0.64; 95% CI, 0.45–0.91; p < 0.01). A trend toward lower atrial fibrillation recurrence was observed (HR 0.64; 95% CI 0.38–1.10). The intervention significantly decreased the risk of stroke (HR, 0.66; 95% CI, 0.60–0.72; p < 0.01). Other outcomes indicated a favorable effect of ablation; however, they did not attain statistical significance. Conclusion In patients with atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF), catheter ablation is correlated with substantially enhanced outcomes compared to medical therapy, particularly in decreasing composite events, mortality, and the incidence of stroke. Trial Registration PROSPERO (CRD420251069889) Outlines the study's main features and displays the related results.

PurposeApproximately 10–40% of strokes are cryptogenic (CS). Long-term electrocardiographic (ECG) monitoring has been recommended in these patients to search for atrial fibrillation (AF). An unresolved issue is whether ambulatory ECG (AECG) monitoring should be performed first, followed by an implantable loop recorder (ILR) if AECG monitoring is non-diagnostic, or whether long-term ECG monitoring should be initiated using ILRs from the onset. The purpose of this study was to assess, using an ILR, AF incidence in the first month after CS.MethodsWe enrolled consecutive CS patients referred for an ILR. All patients were monitored via in-hospital continuous telemetry from admission until the ILR (Medtronic [Minneapolis, MN] LINQ™) was implanted. The duration and overall burden of all AF episodes ≥ 2 min was determined.ResultsThe cohort included 343 patients (68 ± 11 years, CHA2DS2-VASc 3.5 ± 1.7). The time between stroke and ILR was 3.7 ± 1.5 days. During the first 30 days, only 18 (5%) patients had AF. All episodes were paroxysmal, lasting from 2 min to 67 h and 24 min. The median AF burden was 0.85% (IQR 0.52, 10.75). During 1 year of follow-up, 67 (21%) patients had AF.ConclusionThe likelihood of AF detection by an ILR in the first month post-CS is low. Thus, the diagnostic yield of 30 days of AECG monitoring is likely to be limited. These data suggest a rationale for proceeding directly to ILR implantation prior to hospital discharge in CS patients, as many have AF detected during longer follow-up.