Prolonged Rhythm Monitoring for the Detection of Occult Paroxysmal Atrial Fibrillation in Ischemic Stroke of Unknown Cause

Deep learning evaluation of echocardiograms to identify occult atrial fibrillation

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

Atrial fibrillation and survival on a medical intensive care unit

BACKGROUND

Atrial fibrillation (AF) is common among patients in the intensive care unit (ICU) and can be triggered by severe illness or preexisting conditions. It is debated if AF is an independent predictor of poor outcome.

METHODS

Data derives from a single center retrospective registry including all patients with a stay on the medical ICU for >24 h. The primary endpoint was ICU survival. Secondary endpoints included receiving mechanical support (renal, respiratory or circulatory), hemodynamic parameters during AF, rate and rhythm control strategies, anticoagulation, and documentation.

RESULTS

A total of 616 patients (male gender 62.3%, median age 75 years) were included in our analysis. New-onset AF was diagnosed in 87 patients (14.1%), 136 (22.1%) presented with preexisting AF, and 393 (63.8%) did not develop AF. Initial episodes of new-onset AF exhibited higher hemodynamic instability than episodes in preexisting cases, with elevated heart rates and increased catecholamine doses (both p < 0.001). ICU survival in new-onset AF was 80.5% (70/87) compared to 92.4% (363/393) in patients without AF (OR 0.340, CI 0.182-0.658, p < 0.001). Likewise, ICU survival in preexisting AF was 86.8% (118/136) was significantly lower compared to no AF (OR 0.542, CI 0.290-0.986, p = 0.050*). Independent predictors of ICU survival for patients were atrial fibrillation (p = 0.016), resuscitation before or during ICU stay (p < 0.001), and receiving acute dialysis on ICU (p = 0.002).

CONCLUSIONS

ICU survival is noticeably lower in patients with new-onset or preexisting atrial fibrillation compared to those without. Patients who develop new-onset AF during their ICU stay warrant special attention for both short-term and long-term care strategies.

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning

BACKGROUND

The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR.

METHODS

We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy.

RESULTS

ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68.

CONCLUSION

The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.

Hemorrhagic Transformation in Patients With Ischemic Stroke and Atrial Fibrillation: To Anticoagulate or Not, That Is the Question

The management of anticoagulation in patients with ischemic stroke and atrial fibrillation (AF) poses a critical dilemma due to the inherent risk of hemorrhagic transformation. This article presents the case of an 89-year-old male with AF and recurrent ischemic strokes, highlighting the complex challenge of deciding whether to initiate or withhold anticoagulation. After the initial ischemic stroke event, the patient started a direct oral anticoagulant. Subsequent imaging revealed hemorrhagic transformation, leading to the cessation of anticoagulation. Despite multiple hemorrhagic recurrences, balancing thrombotic and bleeding risks remained challenging. Mechanical thrombectomy was performed for a subsequent ischemic stroke due to an absolute contraindication for thrombolysis. The patient's intricate clinical course involved a multidisciplinary approach, resulting in a decision to cautiously resume low-dose anticoagulation combined with left atrial appendage closure. This decision was made after careful consideration of persistent thrombotic risk despite recurrent hemorrhages. The case underscores the complex management dilemma of anticoagulation in elderly patients with AF and recurrent strokes, emphasizing the need for a multidisciplinary approach and individualized decision-making in such challenging scenarios. Further research and guidelines are warranted to establish optimal strategies for (re)initiating anticoagulation in patients with recurrent hemorrhagic transformation.

Get Out the Door: Ambulatory EEG Trumps Routine EEG in the Detection of Interictal Epileptiform Abnormalities After a First Unprovoked Seizure

Diagnostic Accuracy of Ambulatory EEG vs Routine EEG in Patients With First Single Unprovoked Seizure Hernandez-Ronquillo L, Thorpe L, Feng C, Hunter G, Dash D, Hussein T, Dolinsky C, Waterhouse K, Roy P, Jette N. Neurol Clin Pract . 2023;13(3). doi:10.1212/CPJ.0000000000200160 Background and Objective: To evaluate the diagnostic accuracy of the ambulatory EEG (aEEG) at detecting interictal epileptiform discharges (IEDs)/seizures compared with routine EEG (rEEG) and repetitive/second rEEG in patients with a first single unprovoked seizure (FSUS). We also evaluated the association between IED/seizures on aEEG and seizure recurrence within 1 year of follow-up. Methods: We prospectively evaluated 100 consecutive patients with FSUS at the provincial Single Seizure Clinic. They underwent 3 sequential EEG modalities: first rEEG, second rEEG, and aEEG. Clinical epilepsy diagnosis was ascertained based on the 2014 International League Against Epilepsy definition by a neurologist/epileptologist at the clinic. An EEG-certified epileptologist/neurologist interpreted all 3 EEGs. All patients were followed up for 52 weeks until they had either second unprovoked seizure or maintained single seizure status. Accuracy measures (sensitivity, specificity, negative and positive predictive values, and likelihood ratios), receiver operating characteristic (ROC) analysis, and area under the curve (AUC) were used to evaluate the diagnostic accuracy of each EEG modality. Life tables and the Cox proportional hazard model were used to estimate the probability and association of seizure recurrence. Results: Ambulatory EEG captured IED/seizures with a sensitivity of 72%, compared with 11% for the first rEEG and 22% for the second rEEG. The diagnostic performance of the aEEG was statistically better (AUC: 0.85) compared with the first rEEG (AUC: 0.56) and second rEEG (AUC: 0.60). There were no statistically significant differences between the 3 EEG modalities regarding specificity and positive predictive value. Finally, IED/seizure on the aEEG was associated with more than 3 times the hazard of seizure recurrence. Discussion: The overall diagnostic accuracy of aEEG at capturing IED/seizures in people presenting with FSUS was higher than the first and second rEEGs. We also found that IED/seizures on the aEEG were associated with an increased risk of seizure recurrence. Classification of Evidence: This study provides Class I evidence supporting that, in adults with First Single Unprovoked Seizure (FSUS), 24-h ambulatory EEG has increased sensitivity when compared with routine and repeated EEG.

Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation

Importance

Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases.

Objective

To determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population.

Design, Setting, and Participants

This prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm.

Main Outcomes and Measures

A convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center.

Results

A total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater.

Conclusions and Relevance

Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

Research on atrial fibrillation mechanisms and prediction of therapeutic prospects: focus on the autonomic nervous system upstream pathways

Atrial fibrillation (AF) is the most common clinical arrhythmia disorder. It can easily lead to complications such as thromboembolism, palpitations, dizziness, angina, heart failure, and stroke. The disability and mortality rates associated with AF are extremely high, significantly affecting the quality of life and work of patients. With the deepening of research into the brain-heart connection, the link between AF and stroke has become increasingly evident. AF is now categorized as either Known Atrial Fibrillation (KAF) or Atrial Fibrillation Detected After Stroke (AFDAS), with stroke as the baseline. This article, through a literature review, briefly summarizes the current pathogenesis of KAF and AFDAS, as well as the status of their clinical pharmacological and non-pharmacological treatments. It has been found that the existing treatments for KAF and AFDAS have limited efficacy and are often associated with significant adverse reactions and a risk of recurrence. Moreover, most drugs and treatment methods tend to focus on a single mechanism pathway. For example, drugs targeting ion channels primarily modulate ion channels and have relatively limited impact on other pathways. This limitation underscores the need to break away from the "one disease, one target, one drug/measurement" dogma for the development of innovative treatments, promoting both drug and non-drug therapies and significantly improving the quality of clinical treatment. With the increasing refinement of the overall mechanisms of KAF and AFDAS, a deeper exploration of physiological pathology, and comprehensive research on the brain-heart relationship, it is imperative to shift from long-term symptom management to more precise and optimized treatment methods that are effective for almost all patients. We anticipate that drugs or non-drug therapies targeting the central nervous system and upstream pathways can guide the simultaneous treatment of multiple downstream pathways in AF, thereby becoming a new breakthrough in AF treatment research.

Atrial Fibrillation Recurrence in Patients With Transient New-Onset Atrial Fibrillation Detected During Hospitalization for Noncardiac Surgery or Medical Illness : A Matched Cohort Study

BACKGROUND

Atrial fibrillation (AF) is often detected for the first time in patients who are hospitalized for another reason. Long-term risks for AF recurrence in these patients are unclear.

OBJECTIVE

To estimate risk for AF recurrence in patients with new-onset AF during a hospitalization for noncardiac surgery or medical illness compared with a matched population without AF.

DESIGN

Matched cohort study. (ClinicalTrials.gov: NCT03221777).

SETTING

Three academic hospitals in Hamilton, Ontario, Canada.

PARTICIPANTS

The study enrolled patients hospitalized for noncardiac surgery or medical illness who had transient new-onset AF. For each participant, an age- and sex-matched control participant with no history of AF from the same hospital ward was recruited. All participants left the hospital in sinus rhythm.

MEASUREMENTS

14-day electrocardiographic (ECG) monitor at 1 and 6 months and telephone assessment at 1, 6, and 12 months. The primary outcome was AF lasting at least 30 seconds on the monitor or captured by ECG 12-lead during routine care at 12 months.

RESULTS

Among 139 participants with transient new-onset AF (70 patients with medical illness and 69 surgical patients) and 139 matched control participants, the mean age was 71 years (SD, 10), the mean CHA2DS2-VASc score was 3.0 (SD, 1.5), and 59% were male. The median duration of AF during the index hospitalization was 15.8 hours (IQR, 6.4 to 49.6 hours). After 1 year, recurrent AF was detected in 33.1% (95% CI, 25.3% to 40.9%) of participants in the transient new-onset AF group and 5.0% (CI, 1.4% to 8.7%) of matched control participants; after adjustment for the number of ECG monitors worn and for baseline clinical differences, the adjusted relative risk was 6.6 (CI, 3.2 to 13.7). After exclusion of participants who had electrical or pharmacologic cardioversion during the index hospitalization (n = 40) and their matched control participants and limiting to AF events detected by the patch ECG monitor, recurrent AF was detected in 32.3% (CI, 23.1% to 41.5%) of participants with transient new-onset AF and 3.0% (CI, 0% to 6.4%) of matched control participants.

LIMITATIONS

Generalizability is limited, and the study was underpowered to evaluate subgroups and clinical predictors.

CONCLUSION

Among patients who have transient new-onset AF during a hospitalization for noncardiac surgery or medical illness, approximately 1 in 3 will have recurrent AF within 1 year.

PRIMARY FUNDING SOURCE

Peer-reviewed grants.

ECG Smart Monitoring versus Implantable Loop Recorders for Atrial Fibrillation Detection after Cryptogenic Stroke-An Overview for Decision Making

Up to 20% of patients with ischemic stroke or transient ischemic attack have a prior history of known atrial fibrillation (AF). Additionally, unknown AF can be detected by different monitoring strategies in up to 23% of patients with cryptogenic or non-cardioembolic stroke. However, most studies had substantial gaps in monitoring time, especially early after the index event. Following this, AF rates would be higher if patients underwent continuous monitoring early after stroke, avoiding any gaps in monitoring. The few existing randomized studies focused on patients with cryptogenic stroke but did not focus otherwise specifically on prevention strategies in patients at high risk for AF (patients at higher age or with high CHA2DS2-VASC scores). Besides invasive implantable loop recorders (ILRs), external loop recorders (ELRs) and mobile cardiac outpatient telemetry (MCOT) are non-invasive tools that are commonly used for long-term ECG monitoring in cryptogenic-stroke patients in the ambulatory setting. The role of MCOT and hand-held devices within ECG smart monitoring in the detection of AF for the prevention of and after cryptogenic stroke is currently unclear. This intense review provides an overview of current evidence, techniques, and gaps in knowledge and aims to advise which patients benefit most from the current available devices.

Atrial Cardiomyopathy in Valvular Heart Disease: From Molecular Biology to Clinical Perspectives

This review discusses the evolving topic of atrial cardiomyopathy concerning valvular heart disease. The pathogenesis of atrial cardiomyopathy involves multiple factors, such as valvular disease leading to atrial structural and functional remodeling due to pressure and volume overload. Atrial enlargement and dysfunction can trigger atrial tachyarrhythmia. The complex interaction between valvular disease and atrial cardiomyopathy creates a vicious cycle of aggravating atrial enlargement, dysfunction, and valvular disease severity. Furthermore, atrial remodeling and arrhythmia can predispose to atrial thrombus formation and stroke. The underlying pathomechanism of atrial myopathy involves molecular, cellular, and subcellular alterations resulting in chronic inflammation, atrial fibrosis, and electrophysiological changes. Atrial dysfunction has emerged as an essential determinant of outcomes in valvular disease and heart failure. Despite its predictive value, the detection of atrial fibrosis and dysfunction is challenging and is not included in the clinical routine. Transthoracic echocardiography and cardiac magnetic resonance imaging are the main diagnostic tools for atrial cardiomyopathy. Recently published data have revealed that both left atrial volumes and functional parameters are independent predictors of cardiovascular events in valvular disease. The integration of atrial function assessment in clinical practice might help in early cardiovascular risk estimation, promoting early therapeutic intervention in valvular disease.

Clinical and Genetic Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke

BACKGROUND

Stroke is a leading cause of death and disability worldwide. Atrial fibrillation (AF) is a common cause of stroke but may not be detectable at the time of stroke. We hypothesized that an AF polygenic risk score (PRS) can discriminate between cardioembolic stroke and noncardioembolic strokes.

METHODS

We evaluated AF and stroke risk in 26 145 individuals of European descent from the Stroke Genetics Network case-control study. AF genetic risk was estimated using 3 recently developed PRS methods (LDpred-funct-inf, sBayesR, and PRS-CS) and 2 previously validated PRSs. We performed logistic regression of each AF PRS on AF status and separately cardioembolic stroke, adjusting for clinical risk score (CRS), imputation group, and principal components. We calculated model discrimination of AF and cardioembolic stroke using the concordance statistic (c-statistic) and compared c-statistics using 2000-iteration bootstrapping. We also assessed reclassification of cardioembolic stroke with the addition of PRS to either CRS or a modified CHA₂DS₂-VASc score alone.

RESULTS

Each AF PRS was significantly associated with AF and with cardioembolic stroke after adjustment for CRS. Addition of each AF PRS significantly improved discrimination as compared with CRS alone (P<0.01). When combined with the CRS, both PRS-CS and LDpred scores discriminated both AF and cardioembolic stroke (c-statistic 0.84 for AF; 0.74 for cardioembolic stroke) better than 3 other PRS scores (P<0.01). Using PRS-CS PRS and CRS in combination resulted in more appropriate reclassification of stroke events as compared with CRS alone (event reclassification [net reclassification indices]⁺=14% [95% CI, 10%-18%]; nonevent reclassification [net reclassification indices]^-=17% [95% CI, 15%-0.19%]) or the modified CHA₂DS₂-VASc score (net reclassification indices⁺=11% [95% CI, 7%-15%]; net reclassification indices^-=14% [95% CI, 12%-16%]) alone.

CONCLUSIONS

Addition of polygenic risk of AF to clinical risk factors modestly improves the discrimination of cardioembolic from noncardioembolic strokes, as well as reclassification of stroke subtype. Polygenic risk of AF may be a useful biomarker for identifying strokes caused by AF.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation

Background

Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored.

Objective

The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data.

Methods

We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0-2 days, ±3-7 days, and ±8-30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively.

Results

We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on ±0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the ±8-30 day window (sensitivity 0.688; 95% CI 0.685-0.690), with performance on the ±3-7 day window falling in between (sensitivity 0.708; 95% CI 0.704-0.710).

Conclusion

Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

Implantable loop recorders in patients with atrial fibrillation

INTRODUCTION

Implantable loop recorders (ILRs) provide practitioners with high-quality electrocardiographic data over an extended monitoring period. These data can guide the diagnosis and management of patients with atrial fibrillation (AF).

AREAS COVERED

This review summarizes the available evidence and consensus statements supporting the use of ILRs in the detection of AF, as well as monitoring of patients with known AF. Future directions for research are also discussed.

EXPERT OPINION

ILRs are the gold standard for detecting AF, providing superior diagnostic yield compared to other modes of ambulatory electrocardiography monitoring. Both experimental evidence and consensus statements support the use of ILRs in clinical settings where the diagnosis of AF may significantly change management, or where a high degree of sensitivity is needed. ILRs may also be used to monitor patients following AF ablation. More evidence is needed to better inform how ILR-detected AF should change management.

A meta-analysis of extended ECG monitoring in detection of atrial fibrillation in patients with cryptogenic stroke

Objective

The aim of this systematic review is to evaluate the various modalities available for extended ECG monitoring in the detection of atrial fibrillation (AF) following a cryptogenic stroke.

Methods

MEDLINE (Ovid), EMBASE (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL) were searched from January 2011 to November 2021. All randomised controlled trials and prospective cohort studies including the use of extended ECG monitoring >24 hours with a minimum duration of AF of 30 s in patients with either cryptogenic strokes or transient ischaemic attacks were included. A random-effects model was used to pool effect estimates of AF detection rates from different ECG modalities.

Results

3924 studies were identified, of which 47 were included reporting on a pooled population of 6448 patients with cryptogenic stroke. The pooled AF rate for implantable loop recorders (ILRs) increased from 4.9% (3.0%–7.9%) at 1 month to 38.4% (20.4%–60.2%) at 36 months. Mobile cardiac outpatient telemetry (MCOT) had a significantly higher pooled AF detection rate of 12.8% (8.9%–17.9%) versus 4.9% (3.0%–7.9%) for ILR at 1 month (p<0.0001). Predictors for AF detection include duration of monitoring (p<0.0001) and age (p<0.0001) for ILRs, but only age for MCOTs (p<0.020).

Conclusion

MCOT has a higher rate of detection at 1 month and is less invasive. Beyond 1 month, compliance becomes a significant limitation for MCOT. MCOT may be a reasonable alternative AF screening tool for patients with cryptogenic stroke if ILR is not available.

PROSPERO registration number

CRD42022297782.

An Artificial Intelligence Algorithm With 24-h Holter Monitoring for the Identification of Occult Atrial Fibrillation During Sinus Rhythm

Background

Subclinical atrial fibrillation (AF) is one of the pathogeneses of embolic stroke. Detection of occult AF and providing proper anticoagulant treatment is an important way to prevent stroke recurrence. The purpose of this study was to determine whether an artificial intelligence (AI) model can assess occult AF using 24-h Holter monitoring during normal sinus rhythm.

Methods

This study is a retrospective cohort study that included those who underwent Holter monitoring. The primary outcome was identifying patients with AF analyzed with an AI model using 24-h Holter monitoring without AF documentation. We trained the AI using a Holter monitor, including supraventricular ectopy (SVE) events (setting 1) and excluding SVE events (setting 2). Additionally, we performed comparisons using the SVE burden recorded in Holter annotation data.

Results

The area under the receiver operating characteristics curve (AUROC) of setting 1 was 0.85 (0.83–0.87) and that of setting 2 was 0.84 (0.82–0.86). The AUROC of the SVE burden with Holter annotation data was 0.73. According to the diurnal period, the AUROCs for daytime were 0.83 (0.78–0.88) for setting 1 and 0.83 (0.78–0.88) for setting 2, respectively, while those for nighttime were 0.85 (0.82–0.88) for setting 1 and 0.85 (0.80–0.90) for setting 2.

Conclusion

We have demonstrated that an AI can identify occult paroxysmal AF using 24-h continuous ambulatory Holter monitoring during sinus rhythm. The performance of our AI model outperformed the use of SVE burden in the Holter exam to identify paroxysmal AF. According to the diurnal period, nighttime recordings showed more favorable performance compared to daytime recordings.

Systemic immune inflammation index and system inflammation response index are potential biomarkers of atrial fibrillation among the patients presenting with ischemic stroke

Background

Chronic inflammatory disorders in atrial fibrillation (AF) contribute to the onset of ischemic stroke. Systemic immune inflammation index (SIII) and system inflammation response index (SIRI) are the two novel and convenient measurements that are positively associated with body inflammation. However, little is known regarding the association between SIII/SIRI with the presence of AF among the patients with ischemic stroke.

Methods

A total of 526 ischemic stroke patients (173 with AF and 353 without AF) were consecutively enrolled in our study from January 2017 to June 2019. SIII and SIRI were measured in both groups. Logistic regression analysis was used to analyse the potential association between SIII/SIRI and the presence of AF. Finally, the correlation between hospitalization expenses, changes in the National Institutes of Health Stroke Scale (NIHSS) scores and SIII/SIRI values were measured.

Results

In patients with ischemic stroke, SIII and SIRI values were significantly higher in AF patients than in non-AF patients (all p < 0.001). Moreover, with increasing quartiles of SIII and SIRI in all patients, the proportion of patients with AF was higher than that of non-AF patients gradually. Logistic regression analyses demonstrated that log-transformed SIII and log-transformed SIRI were independently associated with the presence of AF in patients with ischemic stroke (log-transformed SIII: odds ratio [OR]: 1.047, 95% confidence interval CI = 0.322–1.105, p = 0.047; log-transformed SIRI: OR: 6.197, 95% CI = 2.196–17.484, p = 0.001). Finally, a positive correlation between hospitalization expenses, changes in the NIHSS scores and SIII/SIRI were found, which were more significant in patients with AF (all p < 0.05).

Conclusions

Our study suggests SIII and SIRI are convenient and effective measurements for predicting the presence of AF in patients with ischemic stroke. Moreover, they were correlated with increased financial burden and poor short-term prognosis in AF patients presenting with ischemic stroke.

European Stroke Organisation (ESO) guideline on screening for subclinical atrial fibrillation after stroke or transient ischaemic attack of undetermined origin

We aimed to provide practical recommendations for the screening of subclinical atrial fibrillation (AF) in patients with ischaemic stroke or transient ischaemic attack (TIA) of undetermined origin. These guidelines are based on the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. Five relevant Population, Intervention, Comparator, Outcome questions were defined by a multidisciplinary module working group (MWG).

Longer duration of cardiac rhythm monitoring increases the detection of subclinical AF, but the optimal monitoring length is yet to be defined. We advise longer monitoring to increase the rate of anticoagulation, but whether longer monitoring improves clinical outcomes needs to be addressed. AF detection does not differ from in- or out-patient ECG-monitoring with similar monitoring duration, so we consider it reasonable to initiate in-hospital monitoring as soon as possible and continue with outpatient monitoring for more than 48h. Although insertable loop recorders (ILR) increase AF detection based on their longer monitoring duration, comparison with non-implantable ECG devices for similar monitoring time is lacking. We suggest the use of implantable devices, if feasible, for AF detection instead of non- implantable devices to increase the detection of subclinical AF. There is weak evidence of a useful role for blood, ECG, and brain imaging biomarkers for the identification of patients at high risk of AF. In patients with patent foramen ovale, we found insufficient evidence from RCT, but prolonged cardiac monitoring in patients >55 years is advisable for subclinical AF detection. To conclude, in adult patients with ischaemic stroke or TIA of undetermined origin, we recommend longer duration of cardiac rhythm monitoring of more than 48h and if feasible with IRL to increase the detection of subclinical AF.

Evaluating left atrial appendage function in a subtype of non-valvular atrial fibrillation using transesophageal echocardiography combined with two-dimensional speckle tracking

BACKGROUND

The use of transesophageal echocardiography (TEE) is a clinically feasible method for quantitative analysis of left atrial appendage (LAA) function. LAA dysfunction is closely associated with atrial fibrillation (AF)-related stroke. However, there are few studies on the changes in LAA function in patients with different types of AF. This study aimed to observe changes in LAA systolic motion and function in patients with different types of AF by using speckle-tracking echocardiography (STE).

METHODS

A retrospective study of 216 patients with non-valvular AF was conducted. The LAA was divided into three parts: the basal segment (B), middle segment (M), and top segment (A). Speck -racking technology was used to measure and record the forward strain values of the basal segment (B), middle segment (M), and top segment (A) of the LAA, and the peak positive strain dispersion of the LAA was calculated. The left atrial appendage mechanical dispersion (LAAMD) was defined as the standard deviation (SD) of the peak positive strain at each segment of the R-R interval.

RESULTS

Partial speckle-tracking parameters of the LAA showed statistical significance between the two groups. The peak strain on the top segment of the LAA was reduced in the persistent atrial fibrillation (per-AF) group compared to the paroxysmal atrial fibrillation (par-AF) group [11.87 (6.47-20.12) vs. 16.02 (9.76-24.50); 12.66 (6.66-21.22) vs. 20.16 (14.16-30.56); both P<0.01]. In the group with lower LAAMD, the proportion of patients with persistent AF (per-AF) was higher (66.3% vs. 33.7%; P<0.001), the left atrial dilatation was more significant (45.80±5.656 vs. 42.85±4.867; P<0.001), the LAA filling velocity and LAA empty velocity were lower (42.35±20.354 vs. 51.0±20.599; 38.71±24.39 vs. 51.62±21.282; both P<0.001), the LAA ejection fraction was significantly lower (52.16±25.538 vs. 70.85±20.741; P=0.000), and the peak positive strains of the M and A of the LAA were lower than those in the higher LAAMD group.

CONCLUSIONS

The deformability of the LAA is decreased diffusely in per-AF, especially in the A of the LAA. Compliance with LAA was worse in patients with per-AF than in those with par-AF.

Association between CHARGE-AF risk score and LA mechanics: LA reservoir strain can be a single parameter for predicting AF risk

AIMS

Atrial fibrillation (AF) is a prevalent arrhythmia and the leading preventable cause of cardioembolic stroke. Scoring systems for predicting AF risk do not use imaging modalities. We sought to determine whether LA longitudinal strain could be used as a single parameter for predicting the risk of AF.

METHODS AND RESULTS

Consecutive patients diagnosed with diastolic dysfunction between December 2019 and March 2020 were included. Two-dimensional, colour flow, continuous pulse-wave, and tissue Doppler transthoracic echocardiography (TTE) were performed using a Vivid E9 imaging system (GE Medical Systems, Chicago, USA). Measurements were obtained in the standard manner recommended by the American Society of Echocardiography. Moreover, LA longitudinal strain was measured using 2D speckle tracking echocardiography in the four-chamber view to evaluate left atrial function. The CHARGE-AF scoring system was used to predict AF risk.A total of 148 patients (mean age: 57.6 ± 11.9; male: 53%) with feasible views for LA strain measurement were divided into two groups based on a 10% CHARGE-AF cut-off score. The >10% group (48 patients; 32%) was defined as having a predicted 5-year AF risk >10%, and the ≤10% group (100 patients; 68%) was defined as having a predicted risk <10%. In the multivariate analysis, LA reservoir strain (LASr) was independently associated with CHARGE-AF score. Furthermore, using the Pearson correlation method, LASr was found to be highly correlated with CHARGE-AF score (r = -0.74, p < 0.0001).

CONCLUSIONS

LASr was highly correlated with CHARGE-AF risk score and may be used as a parameter to predict atrial myopathy and hence AF risk.

Impact of Arrhythmia in Hospital Mortality in Acute Ischemic Stroke Patients: A Retrospective Cohort Study in Northern Mexico

BACKGROUND

Atrial fibrillation has been associated with higher morbidity and mortality rates in acute ischemic stroke patients (AIS). However, there is scarce information regarding the clinical outcomes and strokes' characteristics among AIS patients with other type of arrhythmias.

OBJECTIVE

Our study aims to analyze the hospital mortality rate, stroke characteristics, and clinical and demographical data of patients with any post-stroke arrhythmia.

METHODS

Retrospective cohort study of AIS patients with 24h-Holter monitoring during hospital admission recruited between 2015-2020, outcomes were measured using the modified Rankin scale.

RESULTS

597 patients (61.13±13.61 years; 352 men) were included. Arrhythmias were diagnosed in 33 (5.5%), with atrial fibrillation as the most common finding (82%). Age was related to a higher rate of arrhythmia (P = 0.014). A larger prevalence of cardioembolic strokes (69.7% vs 16.6%, P < 0.05) and AIS in the middle cerebral artery's vascular territory (78.8% vs 58.7%, P < 0.05) were found amongst patients with an arrhythmia. No significant association was found between NIHSS at admission with neither incidence of arrhythmia nor mortality. Within the arrhythmia group, three in-hospital deaths were reported: one AF, one ventricular arrhythmia and one second-degree atrioventricular block. In a logistic regression analysis, patients with any kind of arrhythmia had a higher mortality rate (9.1% vs 1.2%, P = 0.011; OR 6.766, 95% CI 1.552 - 29.500).

CONCLUSION

Arrhythmia detection after an AIS was associated with increased in-hospital mortality. Risk factors related to arrhythmia detection were a higher mean age, cardioembolic strokes and AIS affecting the middle cerebral artery.

Application of artificial intelligence to the electrocardiogram

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Cross-sectional area of the vagus nerve on carotid duplex ultrasound and atrial fibrillation in acute stroke: A retrospective analysis

Introduction

The autonomic nervous system, including the vagus nerve, is associated with the development of atrial fibrillation (AF). However, the association between the cross-sectional area (CSA) of the vagus nerve on ultrasound and the presence of AF has not been fully clarified. This study investigated the association between vagus nerve CSA and the presence of AF in patients with acute stroke.

Methods

We retrospectively reviewed 150 consecutive patients with ischemic stroke or transient ischemic attack. Vagus nerve CSA was evaluated by carotid ultrasonography on the axial view at the thyroid gland level. Univariate and multivariable analyses were performed to examine the association between vagus nerve CSA and AF.

Results

Of 133 patients included in the analysis, 31 (23.3%) were diagnosed with AF before hospital discharge. The median right vagus nerve CSA was significantly smaller in patients with AF than in patients without AF (p = 0.03). However, there was no significant difference in median left vagus nerve CSA. Multivariable logistic regression analysis revealed that log transformed and quintiled brain natriuretic peptide level (odds ratio [OR], 5.03; 95% confidence interval [CI], 2.43-10.40) and right vagus nerve CSA (OR, 0.33; 95% CI, 0.12-0.91) were independent predictors of AF.

Discussion/conclusion

Smaller right vagus nerve CSA in carotid ultrasonography was an independent predictor of AF in patients with ischemic stroke or transient ischemic attack, suggesting that patients with small right vagus nerve CSA should be closely monitored for development of AF.

The efficacy and safety of insertable cardiac monitor on atrial fibrillation detection in patients with ischemic stroke: a systematic review and meta-analysis

Atrial fibrillation (AF) leads to a high risk of recurrent stroke, and the insertable cardiac monitor (ICM), as a new kind of electrocardiographic monitoring device, has been proven to enhance the recognition rate of AF. The aim of this systematic review was to evaluate the efficacy and safety of the ICM use in AF detection of patients with stroke. We pooled 1233 patients from three randomized controlled trials (RCTs). The detection rate of AF was superior in the ICM group to that in the control group at 6 months (risk ratio [RR], 4.63; P < 0.0001; 95% confidence interval [CI], 2.17-9.90) and 12 months (RR, 5.04; P < 0.00001; 95% CI, 2.93 to 8.68). Patients in the ICM group had a higher rate of oral anticoagulant usage (RR, 2.76; P < 0.00001; 95% CI, 1.89-4.02). However, there was no difference in the time to first detection of AF within 12 months (mean difference, - 8.28; P = 0.82; 95% CI, - 77.84-61.28) or the rate of recurrent ischemic stroke or transient ischemic attack (RR, 0.88; P = 0.51; 95% CI, 0.60-1.28) between the ICM and control groups. In addition, the ICM group experienced more adverse events than the control group within 12 months (RR, 4.42; P = 0.002; 95% CI, 1.69-11.55). To conclude, the sensitivity of ICM is superior to that of conventional external cardiac monitoring. Reducing adverse reactions will be a new development direction of ICM.

Considerations on Performance Evaluation of Atrial Fibrillation Detectors

OBJECTIVE

A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance.

METHODS

Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance.

RESULTS

The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance.

CONCLUSION

The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.

Left atrial strain predicts incident atrial fibrillation in the general population: the Copenhagen City Heart Study

BACKGROUND

Left atrial (LA) strain parameters have been demonstrated to be valuable predictors of atrial fibrillation (AF) in several patient cohorts. The purpose of this study was to investigate whether LA strain, assessed by two-dimensional speckle-tracking echocardiography, can be used to predict the development of AF in the general population.

METHODS AND RESULTS

This prospective longitudinal study included 4466 participants from the fifth Copenhagen City Heart Study. All participants underwent a health examination, including echocardiographic measurements of LA strain. Participants with prevalent AF at baseline were excluded. The primary endpoint was incident AF. During a median follow-up period of 5.3 years, 154 (4.3%) participants developed AF. In univariable analysis, peak atrial longitudinal strain (PALS), peak atrial contraction strain (PACS), and LA strain during the conduit phase were significantly associated with the development of AF. PALS [hazard ratio (HR) 1.05, 95% confidence interval (CI) (1.03-1.07), P < 0.001, per 1% decrease] and PACS (HR 1.08, 95% CI (1.05-1.12), P < 0.001, per 1% decrease] remained independent predictors of AF in multivariable analysis. In addition, PALS and PACS remained significantly associated with AF development even in participants with normal-sized atria and normal left ventricular (LV) systolic function.

CONCLUSION

In the general population, PALS and PACS independently predict incident AF. These findings remained consistent even in participants with normal-sized LA and normal LV systolic function.

A meta-analysis of the association of atrial septal abnormalities and atrial vulnerability

BACKGROUND

The mechanism of cryptogenic stroke (CS) in patients with atrial septal abnormalities remains unclear, and the increased incidence of atrial vulnerability may be one of the reasons. We performed this meta-analysis to clarify the association between atrial septal abnormalities and atrial vulnerability, and to provide evidence-based basis for the prevention and mechanism of CS.

METHODS

We systematically searched for studies on the association between atrial septal abnormalities and atrial vulnerability, and pooled available data on types of atrial septal abnormalities, types of atrial vulnerability, and methods of atrial vulnerability detection. The primary endpoints were the occurrence of atrial arrhythmias or P wave abnormalities. Random-effects models were used to calculate odds ratios (OR) and 95% confidence intervals (CI).

RESULTS

Twelve case-control studies were eligible. Compared with the control group, patients with atrial septal abnormalities had a higher risk of atrial vulnerability (OR: 1.93; 95% CI: 1.13-3.30, P = .02). Data based on stroke patients showed that the group with atrial septal abnormalities had a higher risk of atrial vulnerability than the control group (OR: 2.00; 95% CI: 1.13-3.53, P = .02). However, there was no significant difference in the incidence of atrial vulnerability between the 2 groups of nonstroke patients. Subgroup analysis showed that although atrial septal abnormality increased the risk of atrial vulnerability in the subgroup of atrial septal aneurysm (OR: 1.68; 95% CI: 0.47-5.95, P = .42), the subgroup of atrial fibrillation (AF)/atrial fluster (OR: 1.81; 95% CI: 0.94-3.46, P = .07) and the subgroup of subcutaneous recording system (OR: 1.33; 95% CI: 0.68-2.61, P = .41), the difference was not statistically significant.

CONCLUSIONS

Atrial septal abnormalities can increase the risk of atrial vulnerability, and atrial arrhythmia caused by atrial septal abnormalities may be one of the mechanisms of CS.

Atrial Fibrillation Burden Detected by Dual-Chamber Pacemakers as a Predictor for Cardiac Outcomes: A Retrospective Single-Center Cohort Study

Background: Atrial fibrillation (AF) might lead to adverse cardiac consequences. The association between AF burden and cardiac prognosis is unknown. Methods and Results: This retrospective cohort study enrolled 204 patients (117 males; age 74.5 ± 11.5 years) who underwent dual-chamber pacemaker implantation in our center from October 2003 to May 2017. During a median follow-up of 66.5 months, AF could be detected in 153 (75%) of the 204 pacemaker patients. Primary endpoint events (composite cardiac readmission, stroke or systemic embolism, and all-cause death) occurred in 83 cases (40.7%). In logistic regression analysis, AF detection was associated with increased risks of composite endpoints [odds ratio (OR) = 2.9, 95% confidence interval (CI): 1.3-6.2, p = 0.007], and the hazard was mainly driven by increased cardiac readmission (OR = 2.2, 95% CI: 1.1-4.7, p = 0.034). No significantly elevated risk for new-onset stroke, systemic embolism, or deaths were found in patients with AF detected than those without AF recorded. AF duration grade of more than 6 min suggested progressively increased composite endpoints (OR = 1.8, 95% CI: 1.2-2.7, p for trend = 0.005), cardiac readmission (OR = 1.8, 95% CI: 1.2-2.7, p for trend = 0.005), especially heart failure or acute coronary syndrome-associated readmission (OR = 1.8, 95% CI: 1.2-2.9, p for trend = 0.010), than those with shorter (<6 min) or no AF episodes. Kaplan-Meier analyses and Cox regression also suggested that episodes of AF more than 6 min predicted future cardiac events. Conclusions: AF detected by pacemakers were common. Higher AF burden predicted more adverse cardiac outcomes and might suggest the intervention of rhythm control in these population.

Diagnostic value of electrocardiogram during routine electroencephalogram

INTRODUCTION

A single-lead electrocardiogram (EKG) is routinely recorded with electroencephalogram (EEG). This study investigates the frequency and types of EKG abnormalities during routine EEG.

METHODS

All routine EEGs (20-60 min) over one year were retrospectively analyzed. A blinded cardiologist interpreted EKG recordings. An epileptologist evaluated EEGs. Demographic data, underlying comorbidities, and indications for the EEG were extracted.

RESULTS

A total of 433 recordings for 365 patients were included. Mean (±SD) age was 46.8 (±21.3) years and 50.4% were females. EKG abnormalities were detected in 28.5% of patients; sinus tachycardia (11%), premature ventricular contractions (7.9%), atrial fibrillation (Afib) (6.3%), sinus bradycardia (2.2%) and premature atrial contractions (1.1%). Afib was more common in females than males (p = 0.020), confirmed in six out of seven patients and discovered in 17 patients. Age (OR: 1.67, 95%CI: 1.05-2.66, p = 0.031), prior diagnosis of epilepsy (OR: 2.25, 95% CI: 1.22 - 4.14, p = 0.009), history of seizure (OR: 1.97,  95%CI: 1.09-3.54, p = 0.024), abnormal EEG (OR: 2.14, 95%CI: 1.25 - 3.66, p = 0.005) and EEGs evaluating seizures/epilepsy (OR: 4.18, 95% CI: 1.32 - 13.21, p = 0.015) or syncope (OR: 3.21, 95% CI: 1.16 - 8.84, p = 0.024) were independently associated with abnormal EKG.

CONCLUSION

The frequency of EKG abnormalities captured during routine EEGs was high, with Afib being the most significant. Older age, history of epilepsy or seizure, abnormal EEGs, and EEGs evaluating seizures/epilepsy or syncope were significant predictors. These findings suggest neurologists to become more vigilant to EKG recorded during routine EEG as such findings might have diagnostic and therapeutic implications.

Enhanced detection of cardiac arrhythmias utilizing 14-day continuous ECG patch monitoring

BACKGROUND

To evaluate the performance of a single‑lead, 14-day continuous electrocardiogram (ECG) patch for the detection of arrhythmias compared to conventional 24-h monitoring.

METHODS

This prospective clinical trial enrolled patients suspected of arrhythmias but not diagnosed by 12‑lead ECGs. Each patient underwent a 24-h Holter and 14-day ECG patch simultaneously. Seven types of arrhythmias were classified: supraventricular tachycardia (SVT, repetitive atrial beats >4 beats), irregular SVT without P wave (>4 beats), AF/AFL (irregular SVT without P wave ≥30 s), pause ≥3 s, atrioventricular block (AVB; Mobitz type II, third-degree, two to one or high degree AVB), ventricular tachycardia (VT), and polymorphic VT.

RESULTS

A total of 158 patients were recruited (mean wear time:12.3 ± 3.2 days). The overall arrhythmia detection rate was higher with 14-day ECG patches (59.5%) compared to 24-h Holter (19.0%, P < 0.001). Up to 87.2% of arrhythmias recorded with 14-day ECG patches were not associated with symptoms. The 14-day ECG patch was associated with higher detection rates compared to the 24-h Holter in patients with SVT (52.5% versus 15.8%, P < 0.001), irregular SVT without P wave (12.7% versus 4.4%, P = 0.002), AF/AFL (9.5% versus 3.8%, P = 0.042), and critical arrhythmias (pause ≥3 s, AVB, VT, polymorphic VT) (16.5% versus 2.5%, P < 0.001). The 14-day ECG patch detected more than 2 types of arrhythmias in 5.1% of patients. No serious adverse events in patients wearing the 14-day ECG patch were reported.

CONCLUSIONS

The 14-day ECG patch outperformed 24-h Holter to detect overall, asymptomatic, critical and multiple arrhythmias. It is safe and has the potential to identify individuals with hidden arrhythmias, especially those with critical arrhythmias.

Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is the major cause of stroke since approximately 25% of all strokes are of cardioembolic-origin. The detection and diagnosis of AF are often challenging due to the asymptomatic and intermittent nature of AF.

HYPOTHESIS

A wearable electrocardiogram (ECG)-device could increase the likelihood of AF detection. The aim of this study was to evaluate the feasibility and reliability of a novel, consumer-grade, single-lead ECG recording device (Necklace-ECG) for screening, identifying and diagnosing of AF both by a cardiologist and automated AF-detection algorithms.

METHODS

A thirty-second ECG was recorded with the Necklace-ECG device from two positions; between the palms (palm) and between the palm and the chest (chest). Simultaneously registered 3-lead ECGs (Holter) served as a golden standard for the final rhythm diagnosis. Two cardiologists interpreted independently in a blinded fashion the Necklace-ECG recordings from 145 patients (66 AF and 79 sinus rhythm, SR). In addition, the Necklace-ECG recordings were analyzed with an automatic AF detection algorithm.

RESULTS

Two cardiologists diagnosed the correct rhythm of the interpretable Necklace-ECG with a mean sensitivity of 97.2% and 99.1% (palm and chest, respectively) and specificity of 100% and 98.5%. The automatic arrhythmia algorithm detected the correct rhythm with a sensitivity of 94.7% and 98.3% (palm and chest) and specificity of 100% of the interpretable measurements.

CONCLUSIONS

The novel Necklace-ECG device is able to detect AF with high sensitivity and specificity as evaluated both by cardiologists and an automated AF-detection algorithm. Thus, the wearable Necklace-ECG is a new, promising method for AF screening.

CLINICAL TRIAL REGISTRATION

Study was registered in the ClinicalTrials.gov database (NCT03753139).

Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study

Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.

Blood biomarkers to detect new-onset atrial fibrillation and cardioembolism in ischemic stroke patients

BACKGROUND

Accumulating data suggest blood biomarkers could inform stroke etiology.

OBJECTIVE

The purpose of this study was to investigate the performance of multiple blood biomarkers in elucidating stroke etiology with a focus on new-onset atrial fibrillation (AF) and cardioembolism.

METHODS

Between January and December 2017, information on clinical and laboratory parameters and stroke characteristics was prospectively collected from ischemic stroke patients recruited from the National University Hospital, Singapore. Multiple blood biomarkers (N-terminal pro-brain natriuretic peptide [NT-proBNP], d-dimer, S100β, neuron-specific enolase, vitamin D, cortisol, interleukin-6, insulin, uric acid, and albumin) were measured in plasma. These variables were compared with stroke etiology and the risk of new-onset AF and cardioembolism using multivariable regression methods.

RESULTS

Of the 515 ischemic stroke patients (mean age 61 years; 71% men), 44 (8.5%) were diagnosed with new-onset AF, and 75 (14.5%) had cardioembolism. The combination of 2 laboratory parameters (total cholesterol ≤169 mg/dL; triglycerides ≤44.5 mg/dL) and 3 biomarkers (NT-proBNP ≥294 pg/mL; S100β ≥64 pg/mL; cortisol ≥471 nmol/l) identified patients with new-onset AF (negative predictive value [NPV] 90%; positive predictive value [PPV] 73%; area under curve [AUC] 85%). The combination of 2 laboratory parameters (total cholesterol ≤169 mg/dL; triglycerides ≤44.5 mg/dL) and 2 biomarkers (NT-proBNP ≥507 pg/mL; S100β ≥65 pg/mL) identified those with cardioembolism (NPV 86%; PPV 78%; AUC 87%). Adding clinical predictors did not improve the performance of these models.

CONCLUSION

Blood biomarkers could identify patients with increased likelihood of cardioembolism and direct the search for occult AF.

The Multifaceted Interplay between Atrial Fibrillation and Myocardial Infarction: A Review

This review was conducted to emphasize the complex interplay between atrial fibrillation (AF) and myocardial infraction (MI). In type 1 (T1) MI, AF is frequent and associated with excess mortality. Moreover, AF after hospital discharge for T1MI is not rare, suggesting the need to improve AF screening and to develop therapeutic strategies for AF recurrence. Additionally, AF is a common trigger for type 2 MI (T2MI), and recent data have shown that tachyarrhythmia or bradyarrhythmia could be a causal factor in, respectively, 13–47% or 2–7% of T2MI. In addition, AF is involved in T2MI pathogenesis as a result of severe anemia related to anticoagulants. AF is also an underestimated and frequent cause of coronary artery embolism (CE), as a situation at risk of myocardial infarction with non-obstructive coronary arteries. AF-causing CE is difficult to diagnose and requires specific management. Moreover, patients with both AF and chronic coronary syndromes represent a therapeutic challenge because the treatment of AF include anticoagulation, depending on the embolic risk, and ischemic heart disease management paradoxically includes antiplatelet therapy.

Predicting Atrial Fibrillation with High Risk of Embolization with Atrial Strain and NT-proBNP

The aim of the study was to determine markers of atrial dysfunction in patients with cryptogenic stroke to predict episodes of paroxysmal atrial fibrillation with high risk of embolization (HpAF). We classified patients included in the Crypto-AF study, Cryptogenic Stroke registry, to detect paroxysmal atrial fibrillation (pAF) with wearable Holter, according to the longest episode of pAF in three groups: without pAF detection, episodes of pAF shorter than 5 h, and episodes of pAF longer than 5 h (HpAF). Atrial dysfunction surrogates were evaluated: EKG pattern, Holter record and echocardiography parameters (left atria volume (LAVI), and peak atrial longitudinal and contraction strain (PALS and PACS). The level of N-terminal pro b-type natriuretic peptide (NT-proBNP) was determined. All patients were followed for 2 years to detect pAF and stroke recurrence. From 308 patients, 253 patients with high quality Holter analysis were selected. The distribution was No pAF 78.6% (n = 199), pAF < 5 h 7.9% (n = 20), and HpAF > 5 h 13.4% (n = 34). Age of the patients and combination of PALS and NT-proBNP independently predicted HpAF OR 1.07 (1.00; 1.15) and OR 3.05 (1.08; 8.60) respectively. The validity of PALS and NT-proBNP to detect patients at risk of HpAF was higher than the validity of age (AUC 0.82, sensitivity 78.95%, specificity 63%). Patients with PALS < 25% and NT-proBNP > 283 pg/ml had more detection of pAF during follow-up 35% vs. 5.1% OR 2.33 (1.05-5.13) (p < 0.001). Multimodal assessment of atrial dysfunction with PALS and NT-proBNP improved the prediction of pAF episodes with high embolic risk in patients with cryptogenic stroke.

Transient atrial mechanical dysfunction assessed in acute phase of embolic stroke of undetermined source

BACKGROUND AND PURPOSE

Paroxysmal atrial fibrillation (PAF) has been suggested as a major cause of embolic stroke of undetermined source (ESUS). Transient atrial mechanical dysfunction (stunning) frequently occurs after conversion of atrial fibrillation to sinus rhythm. The study aim was to determine if reversible atrial mechanical dysfunction in ESUS could help elucidate the mechanism of stroke.

METHODS

Eighty-five consecutive patients with acute ischemic stroke were enrolled according to the following inclusion criteria: [1] ≥55 years old; [2] normal sinus rhythm upon admission; [3] no apparent embolic source; and [4] transthoracic echocardiographic evaluation had been performed in both the early phase (<72 h) and late phase (>7 days) after stroke onset. There were 27 patients in the lacunar or atherothrombotic infarction group (controls), 22 in the PAF group, and 36 in the ESUS group. To determine atrial stunning, transmitral flow velocity profiles (Doppler peak E- [early diastolic] and A- [atrial systolic] waves) were obtained.

RESULTS

In the early phase, an E/A velocity ratio ≥ 1.0 was less common in the control group (1 patient, 3.7%) than in the PAF group (19 patients, 86.4%; p < 0.001) and ESUS group (10 patients, 27.8%; p < 0.05). In the late phase, the E/A ratio decreased to less than 1.0 in six patients (31.6%) who had PAF and in eight patients (80.0%) who had ESUS.

CONCLUSION

Transient atrial mechanical dysfunction could be a helpful finding for elucidating the stroke mechanism in patients with ESUS, and early echocardiographic assessment could improve its detection.

Atrial fibrillation post central retinal artery occlusion: Role of implantable loop recorders

OBJECTIVE

This study evaluated the risk of subclinical atrial fibrillation (AF) in patients with central retinal artery occlusion (CRAO) compared to those with cryptogenic stroke using implantable loop recorders (ILR).

METHODS

We conducted a retrospective analysis of 273 consecutive patients who had ILRs inserted at our institution for either cryptogenic stroke (n = 227) or CRAO (n = 46). Our primary endpoint was a time to event analysis for the new diagnosis of AF by ILR. Univariable and multivariable Cox proportional hazard models were used to determine the predictors of time-to-AF.

RESULTS

A total of 64 patients were found to have newly diagnosed AF by remote monitoring of the ILR. AF was detected in 57 of 227 (25%) cryptogenic stroke patients by the end of a maximum 5.1 years follow-up and in seven of 46 (15%) CRAO patients by the end of a maximum 3.6 years follow-up (P = .215, log-rank test). The Kaplan-Meier estimates for freedom from AF was 59.4% for CRAO and 66.6% for cryptogenic stroke (P = NS, log-rank test). Baseline variables predicting AF included older patients, higher CHADS₂ VASC score, longer PR interval on initial EKG evaluation, and mitral annular calcification on transthoracic echocardiogram.

CONCLUSIONS

Patients with CRAO are at risk for subclinical AF, similar to those with cryptogenic stroke. Long-term monitoring to detect AF may lead to changes in pharmacotherapy to reduce the risk for subsequent stroke.

CHA2DS2-VASc and readmission with new-onset atrial fibrillation, atrial flutter, or acute cerebrovascular accident

BACKGROUND

Although risk factors for atrial fibrillation (AF) and atrial flutter (AFL) are known, identifying patients who will develop AF/AFL within the near future remains challenging. We sought to evaluate if the CHA₂DS₂-VASc risk score (CVRS) can identify hospital readmissions with AF, AFL, or acute cerebrovascular accident (CVA) among hospitalized patients without prior history of AF/AFL.

METHODS

Using the Nationwide Readmission Database, a study cohort included patients without prior AF/AFL or new diagnosis of AF/AFL at the index hospitalization from 2012 to 2014. Patients were stratified based on the CVRS into three groups: Low (CVRS ≤1), Intermediate (CVRS 2-5), and High (CVRS ≥6).The primary outcome of interest was 180-day readmission rate with a primary or secondary diagnosis of AF/AFL. Secondary outcomes of interest were acute CVA and 6-month mortality rate.

RESULTS

A total of 17,820,640 patients were included in our study. Over a 6-month follow up duration from the index hospitalization, the overall re-admission rate for new onset atrial arrhythmias (AF/AFL) was 3.48% (n = 620,986), acute CVA 0.13% (n = 22,522), and all-cause mortality 0.31% (n = 55,632). When compared to other groups, patients with a higher CVRS were readmitted more frequently for AF/AFL [odds ratio (OR) 2.43; 95% confidence interval (CI) 2.41-2.45, P < .0001), acute CVA (OR 3.96; 95%CI 3.85-4.08, P < .0001), and all-cause mortality (OR 2.19; 95%CI 2.14-2.24, P < .0001).

CONCLUSION

In this large contemporary cohort, a CHADS2VA2SC score ≥ 6 identified patients without known prior atrial arrhythmias at an elevated risk of developing AF/AFL or acute CVA within 6 months of hospitalization.

Mean platelet volume and mean platelet volume/platelet count ratio in nonvalvular atrial fibrillation stroke and large artery atherosclerosis stroke

Ischemic stroke subtypes such as patients with large artery atherosclerosis, cardioembolism, and embolic stroke of undetermined source were investigated. This study was performed aimed to determine mean platelet volume (MPV) and mean platelet volume/platelet count (MPV/Plt) ratio in nonvalvular atrial fibrillation (AF) stroke and large artery atherosclerosis (LAA) stroke.We conducted a retrospective study of consecutive patients for treatment of acute ischemic stroke at Ruian People's Hospital from March 2017 to October 2018. The patients with ischemic stroke caused by AF and LAA were recruited to this study. Ischemic stroke was confirmed by magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), ischemic lesions on diffusion-weighted imaging were measured in terms of size, composition, and pattern. MPV and platelet count were examined and (MPV/Plt) ratio was calculated.Three hundred seventy one patients were enrolled composing of 177 (47.7%) nonvalvular AF and 194 (52.2%) with LAA. The MPV (11.3 ± 1.3 vs 10.8 ± 1.0, P < .001) and MPV/Plt ratio (0.066 ± 0.025 vs 0.055 ± 0.20, P < .001) were much higher in AF group than LAA group. Receiver-operating characteristic (ROC) analysis showed MPV (AUC: 0.624, confidence interval: 0.567-0.68, P < .001) and MPV/Plt (AUC: 0.657, confidence interval: 0.601-0.713, P < .001) predicted AF between the 2 groups. MPV/Plt ratio was negatively associated with lesion volume (r = -0.161, P = .033) in AF. The analyses of subtypes of composition of infarcts and infarct pattern showed that MPV/Plt ratio was almost higher in AF than LAA except for subcortical-only pattern. Multivariable regression analyses demonstrated National Institutes of Health Stroke Scale (NIHSS) score (r = 2.74; P < .001), LAD (r = -1.15; P = .025) and MPV/Plt ratio (r = -180.64; P = .021) were correlated with lesion volume.Our results indicated elevated MPV and MPV/Plt ratio for the identification of difference between AF and LAA in patients with ischemic stroke.

Left Atrial Volume Index Predicts New-Onset Atrial Fibrillation and Stroke Recurrence in Patients with Embolic Stroke of Undetermined Source

INTRODUCTION

It is unclear which surrogate of atrial cardiopathy best predicts the risk of developing a recurrent ischemic stroke in embolic stroke of undetermined source (ESUS). Left atrial diameter (LAD) and LAD index (LADi) are often used as markers of left atrial enlargement in current ESUS research, but left atrial volume index (LAVi) has been found to be a better predictor of cardiovascular outcomes in other patient populations.

OBJECTIVE

We aim to compare the performance of LAVi, LAD, and LADi in predicting the development of new-onset atrial fibrillation (AF) and stroke recurrence in ESUS.

METHODS

Between October 2014 and October 2017, consecutive patients diagnosed with ESUS were followed for new-onset AF, ischemic stroke recurrence, and a composite outcome of occult AF and stroke recurrence. LAVi and LADi were measured by transthoracic echocardiogram; "high" LAVi was defined as ≥35 mL/m2 in accordance with American Society of Echocardiography guidelines.

RESULTS

185 ischemic stroke patients with ESUS were recruited and followed for a median duration of 2.1 years. Increased LAVi was associated with new-onset AF detection (aOR 1.08; 95% CI 1.03-1.14; p = 0.003) and stroke recurrence (aOR 1.05; 95% CI 1.01-1.10; p = 0.026). Patients with "high" LAVi had a higher likelihood of developing a composite of AF detection and stroke recurrence (HR 3.45; 95% CI 1.55-7.67; p = 0.002). No significant association was observed between LADi and either occult AF or stroke recurrence.

CONCLUSIONS

LAVi is associated with new-onset AF and stroke recurrence in ESUS patients and may be a better surrogate of atrial cardiopathy.

Determinants of Use of Long-term Continuous Electrocardiographic Monitoring for Acute Ischemic Stroke Patients without Atrial Fibrillation at Baseline

BACKGROUND

Atrial fibrillation (AF) is the most common cardiac rhythm disorder associated with stroke. Increased risk of stroke is the same regardless of whether the AF is permanent or paroxysmal. However, detecting paroxysmal AF is challenging and resource intensive. We aimed to develop a predictive model for AF in patients with acute ischemic stroke, which could improve the detection rate of paroxysmal AF.

METHODS

We analyzed 10,034 adult patients with acute ischemic stroke. Differences in clinical characteristics between the patients with and without AF were analyzed in order to develop a predictive model of AF. The associated factors for AF were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses. We used another dataset, which enrolled 860 acute ischemic stroke patients without AF at baseline, to test whether the developed model could improve the detection rate of paroxysmal AF. Among the study population, 1,658 patients (16.5%) had AF.

RESULTS

Multivariate logistic regression revealed that sex, age, body weight, hypertension, diabetes mellitus, hyperlipidemia, pulse rate at admission, respiratory rate at admission, systolic blood pressure at admission, diastolic blood pressure at admission, National Institute of Health Stroke Scale (NIHSS) score at admission, total cholesterol level, triglyceride level, aspartate transaminase level, and sodium level were major factors associated with AF. CART analysis identified NIHSS score at admission, age, triglyceride level, and aspartate transaminase level as important factors for AF to classify the patients into subgroups.

CONCLUSION

When selecting the high-risk group of patients (with an NIHSS score >12 and age >64.5 years, or with an NIHSS score ≤12, age >71.5 years, and triglyceride level ≤61.5 mg/dL) according to the CART model, the detection rate of paroxysmal AF was approximately double in the acute ischemic stroke patients without AF at baseline.

Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke - a single-center study based on 998 patients

Aim of the Study: Cardioembolic stroke accounts for approximately 15-25% of ischemic strokes and is characterized by a poor prognosis. Atrial fibrillation (AF) is more commonly diagnosed in the elderly.The aim of the study was the assessment of the manifestations of AF in patients hospitalized due to cerebral stroke, with particular attention paid to newly diagnosed AF.Methods: A retrospective analysis was performed on 998 cerebral stroke patients. The data were analyzed for sex, age, cerebral stroke risk factors, drugs, NIHSS, RANKIN scores and ECG recordings on admission and at discharge.Results: The mean age of disease onset was 73 ± 16 years. Women accounted for 50.8% of patients. AF prior to hospital admission was diagnosed in 20.1% of patients, while de novo AF in 26.3% of patients during hospitalization. Hypercholesterolemia, hypertriglyceridemia and smoking were more commonly reported in ischemic stroke patients without AF compared to patients with ischemic stroke and AF. Ischemic heart disease, more frequent deaths, and a worse prognosis were more frequently observed in patients with ischemic stroke and AF compared to patients without AF. The first manifestation of AF in 25% of stroke patients was related to the period of the first 10 days of hospitalization.Discussion: The above data should prompt neurologists, cardiologists and family doctors to try to detect AF as a risk factor for ischemic stroke which worsens patient prognosis, prolongs hospital stay and contributes to increase in mortality, especially when more effective drug treatment is currently possible.

Cryptogenic stroke and atrial fibrillation in a real-world population: the role of insertable cardiac monitors

The incidence of atrial fibrillation (AF) in cryptogenic stroke (CS) patients has been studied in carefully controlled clinical trials, but real-world data are limited. We investigated the incidence of AF in clinical practice among CS patients with an insertable cardiac monitor (ICM) placed for AF detection. Patients with CS admitted to our Stroke Unit were included in the study; they received an ICM and were monitored for up to 3 years for AF detection. All detected AF episodes of at least 120 sec were considered. From March 2016 to March 2019, 58 patients (mean age 68.1 ± 9.3 years, 67% male) received an ICM to detect AF after a CS. No patients were lost to follow-up. AF was detected in 24 patients (41%, AF group mean age 70.8 ± 9.4 years, 62% male) after a mean time of 6 months from ICM (ranging from 2 days to 2 years) and 8 months after CS (ranging from 1 month to 2 years). In these AF patients, anticoagulant treatment was prescribed and nobody had a further stroke. In conclusion, AF episodes were detected via continuous monitoring with ICMs in 41% of implanted CS patients. AF in CS patients is asymptomatic and difficult to diagnose by strategies based on intermittent short-term recordings. Therefore, we suggest that ICMs should be part of daily practice in the evaluation of CS patients.

Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation

Aim: Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of stroke however many patients are diagnosed after onset. This study assessed the potential of machine-learning algorithms to detect NVAF. Materials & methods: A retrospective database study using a Japanese claims database. Patients with and without NVAF were selected. 41 variables were included in different classification algorithms. Results: Machine learning algorithms identified NVAF with an area under the curve of >0.86; corresponding sensitivity/specificity was also high. The stacking model which combined multiple algorithms outperformed single-model approaches (area under the curve ≥0.90, sensitivity/specificity ≥0.80/0.82), although differences were small. Conclusion: Machine-learning based algorithms can detect atrial fibrillation with accuracy. Although additional validation is needed, this methodology could encourage a new approach to detect NVAF.

Potential Utility of Neurosonology in Paroxysmal Atrial Fibrillation Detection in Patients with Cryptogenic Stroke

Background: Occult paroxysmal atrial fibrillation (PAF) is a common and potential treatable cause of cryptogenic stroke (CS). We sought to prospectively identify independent predictors of atrial fibrillation (AF) detection in patients with CS and sinus rhythm on baseline electrocardiogram (ECG), without prior AF history. We had hypothesized that cardiac arrhythmia detection during neurosonology examinations (Carotid Duplex (CDU) and Transcranial Doppler (TCD)) may be associated with higher likelihood of AF detection. Methods: Consecutive CS patients were prospectively evaluated over a six-year period. Demographics, clinical and imaging characteristics of cerebral ischemia were documented. The presence of arrhythmia during spectral waveform analysis of CDU/TCD was recorded. Left atrial enlargement was documented during echocardiography using standard definitions. The outcome event of interest included PAF detection on outpatient 24-h Holter ECG recordings. Statistical analyses were performed using univariate and multivariate logistic regression models. Results: A total of 373 patients with CS were evaluated (mean age 60 ± 11 years, 67% men, median NIHSS-score 4 points). The rate of PAF detection of any duration on Holter ECG recordings was 11% (95% CI 8%–14%). The following three variables were independently associated with the likelihood of AF detection on 24-h Holter-ECG recordings in both multivariate analyses adjusting for potential confounders: age (OR per 10-year increase: 1.68; 95% CI: 1.19–2.37; p = 0.003), moderate or severe left atrial enlargement (OR: 4.81; 95% CI: 1.77–13.03; p = 0.002) and arrhythmia detection during neurosonology evaluations (OR: 3.09; 95% CI: 1.47–6.48; p = 0.003). Conclusion: Our findings underline the potential utility of neurosonology in improving the detection rate of PAF in patients with CS.

Cardiac arrhythmia detection outcomes among patients monitored with the Zio patch system: a systematic literature review

Objective: Cardiac arrhythmias can be serious and life threatening, and can impose a significant burden on healthcare systems. Recent technological advances in ambulatory electrocardiogram recorders have led to the development of unobtrusive wearable biosensors which allow physicians to study patients' continuous cardiac rhythm data collected over multiple weeks. The objective of this systematic literature review was to summarize evidence on the clinical effectiveness of the Zio ¹ patch, a long-term, continuous, uninterrupted cardiac monitoring system. Methods: Findings from searches of MEDLINE, Embase and the Cochrane Central Register of Controlled Trials, as well as grey literature, were screened by two reviewers to identify studies reporting cardiac arrhythmia detection outcomes among patients monitored with Zio for an intended duration ≥7 days. Results: Twenty-three publications (22 unique studies) were identified. The unweighted mean wear time was 10.4 days (median ranging from 5 to 14 days). The rate of arrhythmia detection increased with monitoring durations >48 h and continued to increase beyond 7 days of monitoring. Across the 22 studies, unweighted mean detection rates for atrial fibrillation (AF; n = 15), supraventricular tachycardia or supraventricular ectopy (n = 15), and ventricular tachycardia (n = 15) were 12.2%, 45.5% and 17.3%, respectively. Unweighted mean detection rates for chronic/sustained AF (n = 5) and paroxysmal AF (n = 5) were 5.6% and 23.3%, respectively. Conclusion: Findings from the review suggest that long-term, continuous, uninterrupted monitoring with Zio results in longer patient wear times and higher cardiac arrhythmia detection rates compared with outcomes reported in previous reviews of short-duration (24-48 h) cardiac rhythm recording studies.