Noseworthy, Peter A

Background and Purpose: Cerebral microbleeds (CMBs) are represented by small areas of hemosiderin deposition, detected on brain magnetic resonance imaging (MRI), and found in ≈23% of the cognitively normal population over age of 60 years. CMBs predict risk of hemorrhagic and ischemic stroke. They correlate with increased cardiovascular mortality. In this article, we sought to determine in a population-based study whether antithrombotic medications correlate with CMBs and, if present, whether the association was direct or mediated by another variable. Methods: The study consisted of 1253 participants from the population-based Mayo Clinic Study of Aging who underwent T2* gradient-recalled echo magnetic resonance imaging. We tested the relationship between antithrombotic medications and CMB presence and location, using multivariable logistic-regression models. Ordinal logistic models tested the relationship between antithrombotics and CMB frequency. Using structural equation models, we assessed the effect of antithrombotic medications on presence/absence of CMBs and count of CMBs in the CMB-positive group, after considering the effects of age, sex, vascular risk factors, amyloid load by positron emission tomography, and apoE. Results: Two hundred ninety-five participants (26.3%) had CMBs. Among 678 participants taking only antiplatelet medications, 185 (27.3%) had CMBs. Among 95 participants taking only an anticoagulant, 43 (45.3%) had CMBs. Among 44 participants taking an anticoagulant and antiplatelet therapy, 21 (48.8%) had CMBs. Anticoagulants correlated with the presence and frequency of CMBs, whereas antiplatelet agents were not. Structural equation models showed that predictors for presence/absence of CMBs included older age at magnetic resonance imaging, male sex, and anticoagulant use. Predictors of CMB count in the CMB-positive group were male sex and amyloid load. Conclusions: Anticoagulant use correlated with presence of CMBs in the general population. Amyloid positron emission tomography correlated with the count of CMBs in the CMB-positive group.

Background: In patients with heart failure and atrial fibrillation (AF), several clinical trials have reported improved outcomes, including freedom from AF recurrence, quality of life, and survival, with catheter ablation. This article describes the treatment-related outcomes of the AF patients with heart failure enrolled in the CABANA trial (Catheter Ablation Versus Antiarrhythmic Drug Therapy for Atrial Fibrillation). Methods: The CABANA trial randomized 2204 patients with AF who were ≥65 years old or <65 years old with ≥1 risk factor for stroke at 126 sites to ablation with pulmonary vein isolation or drug therapy including rate or rhythm control drugs. Of these, 778 (35%) had New York Heart Association class >II at baseline and form the subject of this article. The CABANA trial’s primary end point was a composite of death, disabling stroke, serious bleeding, or cardiac arrest. Results: Of the 778 patients with heart failure enrolled in CABANA, 378 were assigned to ablation and 400 to drug therapy. Ejection fraction at baseline was available for 571 patients (73.0%), and 9.3% of these had an ejection fraction <40%, whereas 11.7% had ejection fractions between 40% and 50%. In the intention-to-treat analysis, the ablation arm had a 36% relative reduction in the primary composite end point (hazard ratio, 0.64 [95% CI, 0.41–0.99]) and a 43% relative reduction in all-cause mortality (hazard ratio, 0.57 [95% CI, 0.33–0.96]) compared with drug therapy alone over a median follow-up of 48.5 months. AF recurrence was decreased with ablation (hazard ratio, 0.56 [95% CI, 0.42–0.74]). The adjusted mean difference for the AFEQT (Atrial Fibrillation Effect on Quality of Life) summary score averaged over the entire 60-month follow-up was 5.0 points, favoring the ablation arm (95% CI, 2.5–7.4 points), and the MAFSI (Mayo Atrial Fibrillation-Specific Symptom Inventory) frequency score difference was –2.0 points, favoring ablation (95% CI, –2.9 to –1.2). Conclusions: In patients with AF enrolled in the CABANA trial who had clinically diagnosed stable heart failure at trial entry, catheter ablation produced clinically important improvements in survival, freedom from AF recurrence, and quality of life relative to drug therapy. These results, obtained in a cohort most of whom had preserved left ventricular function, require independent trial verification. Registration: URL: https://www.clinicaltrials.gov/ct2/show/NCT00911508 ; Unique identifier: NCT0091150.

Background An artificial intelligence algorithm that detects age using the 12‐lead ECG has been suggested to signal “physiologic age.” This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index of vascular aging, with accelerated physiologic aging gauged by ECG‐derived artificial intelligence–estimated age. Methods and Results This study included 531 patients who underwent ECG and a noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry. Abnormal PMEF was defined as reactive hyperemia peripheral arterial tonometry index ≤2.0. Accelerated or delayed physiologic aging was calculated by the Δ age (ECG‐derived artificial intelligence–estimated age minus chronological age), and the association between Δ age and PMEF as well as its impact on composite major adverse cardiovascular events were investigated. Δ age was higher in patients with abnormal PMEF than in patients with normal PMEF (2.3±7.8 versus 0.5±7.7 years; P =0.01). Reactive hyperemia peripheral arterial tonometry index was negatively associated with Δ age after adjustment for cardiovascular risk factors (standardized β coefficient, –0.08; P =0.048). The highest quartile of Δ age was associated with an increased risk of major adverse cardiovascular events compared with the first quartile of Δ age in patients with abnormal PMEF, even after adjustment for cardiovascular risk factors (hazard ratio, 4.72; 95% CI, 1.24–17.91; P =0.02). Conclusions Vascular aging detected by endothelial function is associated with accelerated physiologic aging, as assessed by the artificial intelligence–ECG Δ age. Patients with endothelial dysfunction and the highest quartile of accelerated physiologic aging have a marked increase in risk for cardiovascular events.

Abstract Aims An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). Methods and results We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02–1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93–0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. Conclusion The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.

AbstractBackgroundThe improved life expectancy observed in patients living with human immunodeficiency virus (HIV) infection has made age‐related cardiovascular complications, including arrhythmias, a growing health concern.HypothesisWe describe the temporal trends in frequency of various arrhythmias and assess impact of arrhythmias on hospitalized HIV patients using the Nationwide Inpatient Sample (NIS).MethodsData on HIV‐related hospitalizations from 2005 to 2014 were obtained from the NIS database using International Classification of Diseases, 9th Revision (ICD‐9) codes. Data was further subclassified into hospitalizations with associated arrhythmias and those without. Baseline demographics and comorbidities were determined. Outcomes including in‐hospital mortality, cost of care, and length of stay were extracted. SAS 9.4 (SAS Institute Inc., Cary, NC) was utilized for analysis. A multivariable analysis was performed to identify predictors of arrhythmias among hospitalized HIV patients.ResultsAmong 2 370 751 HIV‐related hospitalizations identified, the overall frequency of any arrhythmia was 3.01%. Atrial fibrillation (AF) was the most frequent arrhythmia (2110 per 100 000). The overall frequency of arrhythmias increased over time by 108%, primarily due to a 132% increase in AF. Arrhythmias are more frequent among older males, lowest income quartile, and nonelective admissions. Patients with arrhythmias had a higher in‐hospital mortality rate (9.6%). In‐hospital mortality among patients with arrhythmias decreased over time by 43.8%. The cost of care and length of stay associated with arrhythmia‐related hospitalizations were mostly unchanged.ConclusionsArrhythmias are associated with significant morbidity and mortality in hospitalized HIV patients. AF is the most frequent arrhythmia in hospitalized HIV patients.

In the past year, there have been numerous advances in our understanding of arrhythmia mechanisms, diagnosis, and new therapies. We have seen advances in basic cardiac electrophysiology with data suggesting that secretoneurin may be a biomarker for patients at risk of ventricular arrhythmias, and we have learned of the potential role of an NPR-C (natriuretic peptide receptor-C) in atrial fibrosis and the role of an atrial specific 2-pore potassium channel TASK-1 as a therapeutic target for atrial fibrillation. We have seen studies demonstrating the role of sensory neurons in sleep apnea–related atrial fibrillation and the association between bariatric surgery and atrial fibrillation ablation outcomes. Artificial intelligence applied to electrocardiography has yielded estimates of age, sex, and overall health. We have seen new tools for collection of patient-centered outcomes following catheter ablation. There have been significant advances in the ability to identify ventricular tachycardia termination sites through high-density mapping of deceleration zones. We have learned that right ventricular dysfunction may be a predictor of survival benefit after implantable cardioverter-defibrillator implantation in patients with nonischemic cardiomyopathy. We have seen further insights into the role of His bundle pacing on improving outcomes. As our understanding of cardiac laminopathies advances, we may have new tools to predict arrhythmic event rates in gene carriers. Finally, we have seen numerous advances in the treatment of arrhythmias in patients with congenital heart disease.

Background: The optimal timing of catheter ablation for atrial fibrillation (AF) in reference to the time of diagnosis is unknown. We sought to assess the impact of the duration between first diagnosis of AF and ablation, or diagnosis-to-ablation time (DAT), on AF recurrence following catheter ablation. Methods: We conducted a systematic electronic search for observational studies reporting the outcomes associated with catheter ablation for atrial fibrillation stratified by diagnosis-to-ablation time. The primary meta-analysis using a random effects model assessed AF recurrence stratified by DAT ≤1 year versus >1 year. A secondary analysis assessed outcomes stratified by DAT ≤3 years versus >3 years. Results: Of the 632 screened studies, 6 studies met inclusion criteria for a total of 4950 participants undergoing AF ablation for symptomatic AF. A shorter DAT ≤1 year was associated with a lower relative risk of AF recurrence compared with DAT >1 year (relative risk, 0.73 [95% CI, 0.65–0.82]; P <0.001). Heterogeneity was moderate (I 2 =51%). When excluding the one study consisting of only patients with persistent AF, the heterogeneity improved substantially (I 2 =0%, Cochran’s Q P =0.55) with a similar estimate of effect (relative risk, 0.78 [95% CI, 0.71–0.85]; P <0.001). Conclusions: Shorter duration between time of first AF diagnosis and AF ablation is associated with an increased likelihood of ablation procedural success. Additional study is required to confirm these results and to explore implementation of earlier catheter AF ablation and patient outcomes within the current AF care pathway. Visual Overview A visual overview is available for this article.

AbstractBackgroundThe accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually‐applied 12‐lead electrocardiogram (ECG) interpretation methods is difficult.PurposeWe sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software.MethodsIn a two‐part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs.ResultsThe derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903‐0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862‐0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively.ConclusionThe VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.