Atrial Fibrillation Monitoring Research Articles

Multiscale dilated convolutional neural network for Atrial Fibrillation detection.

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality. In light of the urgent need for effective automated AF monitoring, existing methods often fall short in balancing accuracy and computational efficiency. To address this issue, we introduce a framework based on Multi-Scale Dilated Convolution (AF-MSDC), aimed at achieving precise predictions with low cost and high efficiency. By integrating Multi-Scale Dilated Convolution (MSDC) modules, our model is capable of extracting features from electrocardiogram (ECG) datasets across various scales, thus achieving an optimal balance between precision and computational savings. We have developed three MSDC modules to construct the AF-MSDC framework and assessed its performance on renowned datasets, including the MIT-BIH Atrial Fibrillation Database and Physionet Challenge 2017. Empirical results unequivocally demonstrate that our technique surpasses existing state-of-the-art (SOTA) methods in the AF detection domain. Specifically, our model, with only a quarter of the parameters of a Residual Network (ResNet), achieved an impressive sensitivity of 99.45%, specificity of 99.64% (on the MIT-BIH AFDB dataset), and an [Formula: see text] score of 85.63% (on the Physionet Challenge 2017 AFDB dataset). This high efficiency makes our model particularly suitable for integration into wearable ECG devices powered by edge computing frameworks. Moreover, this innovative approach offers new possibilities for the early diagnosis of AF in clinical applications, potentially improving patient quality of life and reducing healthcare costs.

Exploring smartphone ownership, patient behaviour, and potential bias in wearables research for atrial fibrillation

Abstract Background Wearable sensors enable patient-led atrial fibrillation (AF) monitoring after catheter ablation (CA). Investigating smartphone ownership and patient behavior may provide insights into wearables research applicability. Understanding engagement and preferences could inform study design and care pathways. Objective To explore smartphone ownership, socio-economic factors, and behavior among individuals screened for the AFFU-AW study; a randomized controlled trial evaluating Apple Watch (AW)-based follow-up after AF CA. The impact of AW provision on patient behavior was also assessed. Methods Patients with AW-compatible iPhone-Operating System (iOS) devices were invited to participate (case arm), while non-enrolled patients served as controls. Demographics, race, and socio-economic status were surveyed at screening. Engagement and perceived impact were assessed in the case arm. Results Of 197 patients screened, 90 (46%) were ineligible due to smartphone incompatibility, 9 (5%) had no household smartphone. Controls were older (62.4±9.2 vs 57.4±9.8, p<0.01) but exhibited no significant gender or ethnicity differences. (table 1) Among AW users, 7 (17%) required additional assistance for device setup, and all subsequently recorded ECGs independently. 23 (57.5%) participants recorded daily ECGs. 30 (73.2%) reported reduced likelihood of seeking medical attention for AF. However, 2 (4%) reported increased likelihood. Participants in the control arm used blood pressure monitors for AF self-monitoring (33.3%). (fig 1). Conclusion Smartphone ownership is prevalent among AF CA patients. Cross-platform compatibility for wearable interventions is important to address potential biases. A majority of patients felt AW monitoring reduced medical attendances, highlighting the potential for wearables to enhance patient-led care. However, a small proportion reported increased health anxiety, indicating a need for tailored support and education.Figure 1:AF monitoring strategyTable 1:Baseline Characteristics

Continuing evaluation of atrial fibrillation detection after cryptogenic stroke: one-year findings from a multi-center study with confirm Rx ICM

Abstract Background The detection of atrial fibrillation (AF) after a cryptogenic stroke (CS) carries crucial therapeutic implications. While clinical trials have meticulously explored the incidence of AF in CS patients, real-world data on this topic remain relatively sparse. In this study, we aimed to assess the incidence of AF among patients with cryptogenic stroke, who underwent the implantation of an insertable cardiac monitor (ICM). The abstract reports the interim results from the CS sub-study of the Smart Registry. Methods A prospective, single-arm, multi-center registry was conducted to identify AF in patients with cryptogenic stroke using the Confirm R ICM. Inclusion criteria comprised participants aged 40 years or older who had experienced cryptogenic strokes within a 90-day window. Prior to enrollment, comprehensive diagnostic assessments were performed to meticulously rule out all potential sources of embolism, in accordance with the minimal standards of the American and European Guidelines. At each follow-up visit, expert electrophysiologists reviewed and adjudicated ICM detected AF episodes. The primary endpoint was the cumulative incidence of true device-detected AF (lasting more than 30 seconds) at 6 months. Kaplan-Meier curves were employed to assess the cumulative incidence of true device-detected AF through 12 months. Results In our study, 117 patients were enrolled across 19 global sites from September 2021 to August 31, 2023. Kaplan-Meier analysis revealed a 21.3% probability (confidence interval (CI): 14.5%, 30.7%) of true AF episodes lasting more than 30 seconds at 6 months, increasing to 31.0% (CI: 22.0%, 42.5%) at 12 months. Similar results were observed in episodes lasting over 120 seconds at both 6 months (20.7%) and 12 months (30.5%). In average, 14.4 true AF episodes per patient were detected per year. The median time from implantation to AF detection (> 30 seconds) was 31.5 days (interquartile range, 7 to 191). Conclusion Our ongoing study illustrates the effectiveness of the Confirm Rx ICM in detecting atrial fibrillation after cryptogenic stroke. These results confirm the diagnostics value of long-term continuous AF monitoring in cryptogenic stroke patients.

Development and validation of the Atri-Risk Conduction Index risk score to predict risk of atrial fibrillation after typical atrial flutter ablation

BackgroundIdentification of patients at risk for atrial fibrillation (AF) after typical atrial flutter (tAFL) ablation is important to guide monitoring and treatment. ObjectiveThe purpose of this study was to create and validate a risk score to predict AF after tAFL ablation MethodsWe identified patients who underwent tAFL ablation with no AF history between 2017 and 2022 and randomly allocated to derivation and validation cohorts. We collected clinical variables and measured conduction parameters in sinus rhythm on an electrophysiology recording system (CardioLab, GE Healthcare). Univariate and multivariate logistic regressions (LogR) were used to evaluate association with AF development. ResultsA total of 242 consecutive patients (81% male; mean age 66 ± 11 years) were divided into derivation (n =142) and validation (n = 100) cohorts. Forty-two percent developed AF over median follow-up of 330 days. In multivariate LogR (derivation cohort), proximal to distal coronary sinus time (pCS-dCS) ≥70 ms (odds ratio [OR] 16.7; 95% confidence interval [CI] 5.6–49), pCS time ≥36 ms (OR 4.5; 95% CI 1.5–13), and CHADS2-VASc score ≥3 (OR 4.3; 95% CI 1.6–11.8) were independently associated with new AF during follow-up. The Atri-Risk Conduction Index (ARCI) score was created with 0 as minimal and 4 as high-risk using pCS-dCS ≥70 ms = 2 points; pCS ≥36 ms = 1 point; and CHADS2-VASc score ≥3 = 1 point. In the validation cohort, 0% of patients with ARCI score = 0 developed AF, whereas 89% of patients with ARCI score = 4 developed AF. ConclusionWe developed and validated a risk score using atrial conduction parameters and clinical risk factors to predict AF after tAFL ablation. It stratifies low-, moderate-, and high-risk patients and may be helpful in individualizing approaches to AF monitoring and anticoagulation.

Rate of atrial fibrillation by Holter-Stroke Risk Analysis in undetermined TIA/rapidly improving stroke symptoms patients.

Holter-SRA (Stroke Risk Analysis) is an automated analysis of ECG monitoring for Atrial Fibrillation (AF) detection. The aim of this study was to evaluate the rate of AF in undetermined TIA/Rapidly improving stroke symptoms (RISS) patients. Prospective study of undetermined TIA/RISS patients who presented to the emergency department. Early vascular studies (angio CT, transthoracic echocardiography and ECG) were performed in emergency department. The Holter-SRA device was placed for 2 h and the patients were classified into: confirmed AF, high risk of AF or low risk of AF. Prolonged ambulatory monitoring (7 days) was carried out every month for patients with a high-risk pattern. The results were evaluated until definitive detection of AF or low-risk pattern. The endpoints were rate of AF and vascular recurrence at 90 days. Over a period of 24 months, 83 undetermined TIA/RISS patients were enrolled. The mean age was 70 ± 10 years and 61% were men. The median ABCD2 score was 4 points (1-7). After 2 h of monitoring in the emergency department, AF was detected in one patient (1.2%), 51 patients with a low-risk pattern and 31 patients (37.3%) showed a high-risk pattern of AF. During the ambulatory monitoring, of the 31 patients high risk pattern patients, AF was diagnosed to 17 cases and of the 51 patients with a low-risk pattern, one case experienced a recurrent vascular due to undetected AF (1.9% false negative). Three patients (3.6%) suffered a vascular recurrence within the first 90 days, before AF diagnosis. In our study, AF was detected in 22.9% of the 83 patients with indeterminate TIA/RISS. Holter-SRA has allowed us to increase the detection of AF, especially those patients with a high-risk pattern in the first 3 months.

Assessing ascertainment bias in atrial fibrillation across US minority groups.

The aim of this study is to define atrial fibrillation (AF) prevalence and incidence rates across minority groups in the United States (US), to aid in diversity enrollment target setting for randomized controlled trials. In AF, US minority groups have lower clinically detected prevalence compared to the non-Hispanic or Latino White (NHW) population. We assess the impact of ascertainment bias on AF prevalence estimates. We analyzed data from adults in Optum's de-identified Clinformatics® Data Mart Database from 2017-2020 in a cohort study. Presence of AF at baseline was identified from inpatient and/or outpatient encounters claims using validated ICD-10-CM diagnosis algorithms. AF incidence and prevalence rates were determined both in the overall population, as well as in a population with a recent stroke event, where monitoring for AF is assumed. Differences in prevalence across cohorts were assessed to determine if ascertainment bias contributes to the variation in AF prevalence across US minority groups. The period prevalence was respectively 4.9%, 3.2%, 2.1% and 5.9% in the Black or African American, Asian, Hispanic or Latino, and NHW population. In patients with recent ischemic stroke, the proportion with AF was 32.2%, 24.3%, 25%, and 24.5%, respectively. The prevalence of AF among the stroke population was approximately 7 to 10 times higher than the prevalence among the overall population for the Asian and Hispanic or Latino population, compared to approximately 5 times higher for NHW patients. The relative AF prevalence difference of the Asian and Hispanic or Latino population with the NHW population narrowed from respectively, -46% and -65%, to -22% and -24%. The study findings align with previous observational studies, revealing lower incidence and prevalence rates of AF in US minority groups. Prevalence estimates of the adult population, when routine clinical practice is assumed, exhibit higher prevalence differences compared to settings in which monitoring for AF is assumed, particularly among Asian and Hispanic or Latino subgroups.

Health behavior outcomes in stroke survivors prescribed wearables for atrial fibrillation detection stratified by age.

Smartwatches have become readily accessible tools for detecting atrial fibrillation (AF). There remains limited data on how they affect psychosocial outcomes and engagement in older adults. We examine the health behavior outcomes of stroke survivors prescribed smartwatches for AF detection stratified by age. We analyzed data from the Pulsewatch study, a randomized controlled trial that enrolled patients (≥ 50 years) with a history of stroke or transient ischemic attack and CHA2DS2-VASc ≥ 2. Intervention participants were equipped with a cardiac patch monitor and a smartwatch-app dyad, while control participants wore the cardiac patch monitor for up to 44 days. We evaluated health behavior parameters using standardized tools, including the Consumer Health Activation Index, the Generalized Anxiety Disorder questionnaire, the 12-Item Short Form Health Survey, and wear time of participants categorized into three age groups: Group 1 (ages 50-60), Group 2 (ages 61-69), and Group 3 (ages 70-87). We performed statistical analysis using a mixed-effects repeated measures linear regression model to examine differences amongst age groups. Comparative analysis between Groups 1, 2 and 3 revealed no significant differences in anxiety, patient activation, perception of physical health and wear time. The use of smartwatch technology was associated with a decrease in perception of mental health for Group 2 compared to Group 1 (β = -3.29, P = 0.046). Stroke survivors demonstrated a willingness to use smartwatches for AF monitoring. Importantly, among these study participants, the majority did not experience negative health behavior outcomes or decreased engagement as age increased.

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing

BackgroundContinuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF. ObjectivesThis study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal. MethodsWe collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set. ResultsThe results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed). ConclusionsDNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Directional statistics-inspired end-to-end atrial fibrillation detection model based on ECG rhythm

Numerous algorithms designed for detecting atrial fibrillation (AF) often exhibit limitations in extracting essential rhythm features, leading to challenges in accurately discerning ectopic beats and consequently yielding suboptimal detection performance. In this study, we explored the distribution patterns of R-R intervals (RRIs) in both AF and specific rhythms, such as atrial premature beats (APBs) and normal sinus rhythm (NSR). For the first time, we employed the Sobolev test statistics, a method used in directional statistics to assess spherical uniformity, to quantify the irregularity and variability characteristics of RRIs. We developed an end-to-end learnable model for detecting AF by leveraging this approach. A cross-dataset validation method is employed to train and test the proposed model. This involved the use of a simulated dataset and eight distinct real-world databases. Notably, when trained on the Computing in Cardiology Challenge 2017 (C2017) and tested on the MIT-BIH Atrial Fibrillation Database (AFDB), our model, designed to take only a sequence of 32 RRIs as input, achieved a sensitivity of 96.2%, specificity of 98.2%, and accuracy of 97.3%. The results illustrate its competitive standing against the existing methods for AF detection and its enhanced resilience to ectopic beats. Unlike existing deep learning-based AF detection models, our model prioritizes interpretability, boasts lower computational complexity (with fewer than 2000 learnable parameters), and demonstrates superior generalization capabilities. This will help improve the quality of long-term real-time monitoring and management of AF, reduce the burden on clinicians, and ultimately improve patient outcomes.

Wearable heart rate variability and atrial fibrillation monitoring to improve clinically relevant endpoints in cardiac surgery-a systematic review.

This systematic review aims to highlight the untapped potential of heart rate variability (HRV) and atrial fibrillation (AF) monitoring by wearable health monitoring devices as a critical diagnostic tool in cardiac surgery (CS) patients. We reviewed established predictive capabilities of HRV and AF monitoring in specific cardiosurgical scenarios and provide a perspective on additional predictive properties of wearable health monitoring devices that need to be investigated. After screening most relevant databases, we included 33 publications in this review. Perusing these publications on HRV's prognostic value, we could identify HRV as a predictor for sudden cardiac death, mortality after acute myocardial infarction (AMI), and post operative atrial fibrillation (POAF). With regards to standard AF assessment, which typically includes extensive periods of unrecorded cardiac activity, we demonstrated that continuous monitoring via wearables recorded significant cardiac events that would otherwise have been missed. Photoplethysmography and single-lead electrocardiogram (ECG) were identified as the most useful and convenient technical assessment modalities, and their advantages and disadvantages were described in detail. As a call to further action, we observed that the scientific community has relatively extensively explored wearable AF screening, whereas HRV assessment to improve relevant clinical outcomes in CS is rarely studied; it still has great potential to be leveraged. Therefore, risk assessment in CS would benefit greatly from earlier preoperative and postoperative AF detection, comprehensive and accurate assessment of cardiac health through HRV metrics, and continuous long-term monitoring. These should be achievable via commercially available wearables.

Risk of atrial fibrillation in patients with systemic sclerosis: a nationwide population-based study.

Atrial fibrillation (AF) is the most common arrhythmia in the general population causing substantial economic burden, morbidity, and mortality. The incidence rate and risk of AF in patients with systemic sclerosis (SSc) are unclear. We aimed to assess the incidence rate of AF in patients with SSc, and the risk of incident AF in patients with SSc compared with the general population. The Korean National Health Insurance Service database was used as the data source. Patients with claims data for SSc between 2010 and 2017 were extracted from the database along with 1:5 age- and sex-matched controls. The index date was the earliest date with claims data for SSc between 2010 and 2017. The follow-up duration was from the index date to 2019. Multivariable Cox proportional hazard models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for AF in patients with SSc. Overall, 2,519 patients with SSc and 12,595 age- and sex-matched controls were included. Over a mean follow-up duration of 5.2±2.6 years, the incidence rates of AF were 3.52 and 1.68 per 1,000 person-years for patients with SSc and controls, respectively. Compared with controls, patients with SSc had a significantly higher risk of incident AF (adjusted HR = 2.095, 95% CI = 1.466-2.994). Patients with SSc had a two-fold higher risk of incident AF than controls. Given the significant economic burden, morbidity, and mortality that AF poses, close monitoring for incident AF in patients with SSc is warranted.

Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial.

Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394. RR2-10.1016/j.cvdhj.2021.07.002.

Abstract 19154: Impact of Personal ECG Monitoring on Health Care Resource Utilization

Introduction: Atrial fibrillation (AF) monitoring is essential to optimize AF control, reducing the risk of adverse outcomes such as stroke. Traditional noninvasive monitoring options are limited to 30 days. KardiaMobile (KM) are medical-grade, personal ECG devices that record and detect common heart rhythm abnormalities over months to years. This study examined the use of KM to monitor AF and determine its impact on health care resource utilization (HCRU). Methods: This retrospective, observational study used anonymized administrative claims from 2017-2022 and linked to KM users with AF to compare their HCRU to AF patients that did not use KM. The index date for the KM group was the first device use after the first AF claim. The index date of the non-KM group was randomly matched. All-cause HCRU was identified within 1 year after index date. Results: A total of 243,450 AF patients were included with 121,725 in each group. The KM user group was similar to the comparison group in age (mean age 68.1 [SD=10.7] vs 69.1 [10.4]) and gender (62% males in both groups). The percentage of patients that had at least 1 outpatient visit was higher for the KM group (80% vs 64%, p<0.0001), but lower for ER/urgent care visits (28% vs 30%, p<0.0001) and lower for hospitalizations (16% vs 20%, p<0.0001). Among patients with at least 1 hospitalization, KM users had nearly half as many hospitalizations as the comparison group (Figure, p<0.0001). For those that had an ER/urgent care visit, KM users also had significantly less visits (Figure, p<0.0001). Conclusions: AF patients that use a KM personal ECG are more likely to have outpatient visits but have fewer ER/urgent care visits and fewer hospitalizations than those who do not use KM. These results suggest that ER/urgent care and hospitalizations are reduced in AF patients who use KM. Figure. Comparison of utilization in the year following the index date for those with at least one visit in the category

Abstract 15223: Clinical and Economic Impact of Patient Loss-to-Follow-Up After Short-Term Cardiac Monitor Usage in Cryptogenic Stroke

Introduction: Short-term cardiac monitors (STMs) are increasingly used in the work-up of cryptogenic stroke patients before proceeding to long-term continuous monitoring for atrial fibrillation (AF) with insertable cardiac monitors (ICM). However, the rate of ICM placement after STM is only 4-5% based on large real-world studies. The impact of patient fall-out after STM on clinical/economic outcomes is unknown. Research Question: The objective of this analysis was to project the risk of additional ischemic strokes and stroke-related costs with varying rates of patient loss to follow-up (LTFU) after 48-hour, 14-day or 30-day monitors, compared to an approach of continuous ICM monitoring post-stroke. Methods: A previously published Markov model based on the CRYSTAL-AF trial was utilized to project lifetime ischemic strokes after various monitoring strategies, from a US payer perspective. Patient characteristics and AF detection rates were based on CRYSTAL-AF: diagnostic yield with the initial STMs were 0.8% for 48-hr, 3.1% for 14-day and 6.8% for 30-day monitors. AF detection resulted in a change from aspirin to DOAC, with subsequent risks of ischemic strokes and associated costs (including acute + post-acute care) modeled based on published literature. Results: Increasing rates of patient LTFU after STM were associated with higher projected additional secondary strokes compared to an immediate ICM approach, across all 3 STM types (figure). In the scenario based on real-world data (95% LTFU), a range of 62-68 additional strokes are projected per 1,000 patients, with associated stroke costs of $4,928-$5,449/patient when averaged across the population. Conclusions: Loss of patient follow-up after STM in real-world CS patients is projected to lead to substantial secondary stroke burden due to undetected AF, compared to continuous monitoring with ICM. Future work could focus on optimizing stroke pathways to ensure timely and continued access to monitoring.

Can 24 h of ambulatory ECG be used to triage patients to extended monitoring?

Access to long-term ambulatory recording to detect atrial fibrillation (AF) is limited for economical and practical reasons. We aimed to determine whether 24 h ECG (24hECG) data can predict AF detection on extended cardiac monitoring. We included all US patients from 2020, aged 17-100 years, who were monitored for 2-30 days using the PocketECG device (MEDICALgorithmics), without AF ≥30 s on the first day (n = 18,220, mean age 64.4 years, 42.4% male). The population was randomly split into equal training and testing datasets. A Lasso model was used to predict AF episodes ≥30 s occurring on days 2-30. The final model included maximum heart rate, number of premature atrial complexes (PACs), fastest rate during PAC couplets and triplets, fastest rate during premature ventricular couplets and number of ventricular tachycardia runs ≥4 beats, and had good discrimination (ROC statistic 0.7497, 95% CI 0.7336-0.7659) in the testing dataset. Inclusion of age and sex did not improve discrimination. A model based only on age and sex had substantially poorer discrimination, ROC statistic 0.6542 (95% CI 0.6364-0.6720). The prevalence of observed AF in the testing dataset increased by quintile of predicted risk: 0.4% in Q1, 2.7% in Q2, 6.2% in Q3, 11.4% in Q4, and 15.9% in Q5. In Q1, the negative predictive value for AF was 99.6%. By using 24hECG data, long-term monitoring for AF can safely be avoided in 20% of an unselected patient population whereas an overall risk of 9% in the remaining 80% of the population warrants repeated or extended monitoring.

Evaluation of Capacitive ECG for Unobtrusive Atrial Fibrillation Monitoring

Evaluation of Capacitive ECG for Unobtrusive Atrial Fibrillation Monitoring

Low Utility of Short-Term Rhythm Assessment Before Long-Term Rhythm Monitoring in Patients With Cryptogenic Stroke

Implantable cardiac monitors are routinely placed for long-term monitoring (LTM) after a period of negative short-term monitoring (STM) to increase atrial fibrillation (AF) detection after a cryptogenic stroke or transient ischemic attack (TIA). Optimizing AF monitoring after a cryptogenic stroke is critical to improve outcomes and reduce costs. We sought to compare the diagnostic yield of STM versus LTM, assess the impact of routine STM on hospitalization length of stay, and perform a financial analysis comparing the current model to a theoretical model wherein patients can proceed directly to LTM. Our retrospective observational cohort study analyzed patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or TIA who underwent Holter device monitoring. Of 396 subjects, STM detected AF in 10 (2.5%) compared with a diagnostic yield of 14.6% for LTM (median time to diagnosis of 76days). Of the 386 patients with negative STM, 130 (33.7%) received an implantable cardiac monitor while an inpatient, and 256 (66.3%) did not. We calculated a point estimate of 1.67days delay of discharge attributable to the requirement for STM to precede LTM. Our model showed that the expected cost per patient in the STM-first paradigm is $28,615.33 versus $27,111.24 in the LTM-or-STM paradigm. Considering the relatively lower diagnostic yield of STM and its association with a longer length of stay and higher costs, it may be reasonable to proceed directly to LTM to optimize AF detection after a cryptogenic stroke or TIA.

Wearable Cardiac Rhythm Monitoring Device for Detection of Postoperative Atrial Fibrillation

PurposeThis study evaluated the use of a wearable, patch-based cardiac rhythm monitoring device in detecting postoperative atrial fibrillation (POAF) among cardiac surgical patients within 30 days after hospital discharge. DescriptionFrom the SEARCH-AF (The Post-Surgical Enhanced Monitoring for Cardiac Arrhythmias and Atrial Fibrillation) CardioLink-1 trial, this study examined rates of POAF according to surgery type and the incremental value of continuous cardiac rhythm monitoring among patients who underwent valve surgery. The primary outcome was cumulative atrial fibrillation or atrial flutter lasting for ≥6 minutes detected by continuous monitoring or atrial fibrillation or atrial flutter documented by a 12-lead electrocardiogram within 30 days of randomization. EvaluationThe primary outcome occurred in 8.2%, 13.5%, and 21.2% of patients who underwent isolated coronary artery bypass grafting (CABG), isolated valve surgery, and combined CABG and valve surgery. Relative to patients who underwent isolated CABG, those patients who had valve surgery were more likely to experience POAF. A higher diagnostic yield was obtained when the patch-based cardiac rhythm monitor was applied in patients who underwent valve surgery. ConclusionsUse of a wearable, patch-based cardiac monitoring device was an effective detection strategy among patients undergoing valve surgery, given their higher risk of developing POAF.

Value of morphological and laboratory markers in predicting the effectiveness of cryoballoon pulmonary vein isolation in atrial fibrillation

Aim. To determine the clinical, morphological, and biochemical pre­dictors of substrate-specific, non-vein-dependent atrial fibrillation (AF) by morphological and immunological examinations in patients with recurrent AF after pulmonary vein (PV) isolation.Material and methods. The study included 100 people (58 men and 42 women) with paroxysmal (n=89) and persistent (n=11) AF with a mean age of 58,2±9,4 years. After the primary operation, 15 people withdrew from the study due to refusal of reintervention, and 1 person withdrew due to suppuration of loop recorder bed and its explantation. A total of 100 patients underwent primary cryoballoon PV isolation with myocardial biopsy from the interventricular and interatrial septa, while 60 patients had subsequent hematoxylin-eosin and Van Gieson stains. Also, 100 patients were simultaneously implanted with ECG loop recorders for AF monitoring and blood was taken (before the intervention) for subsequent analysis of inflammation markers and antimyocardial antibodies. Patients underwent clinical observation during the year (visits after 3, 6, 12 months). Patients with relapses were referred for repeat electrophysiological examination and segmental ablation if PV ruptures were detected. After repeated intervention, the follow-up period was extended for a year with the same schedule of visits. According Two groups of patients were formed: without relapses (vein-dependent AF, group I) and with relapses (non-vein-dependent AF, group II), despite the achieved PV isolation as a result of 1 or 2 operations.Results. An analysis of morphological changes showed that an increase in AF recurrence risk by an average of 6,14 times (95% confidence interval: 1,14-47,6 (p=0,0462)) is associated with stage 2 interventricular septal fibrosis. Lymphocyte infiltration of the interventricular septum was associated with higher AF recurrence by an average of 9 times (95% confidence interval: 0,89-95,37) (p=0,0519).Among the laboratory parameters, a significantly higher concentration of the N-terminal pro-brain natriuretic peptide and a significantly lower concentration of antimyocardial antibodies were found in the group of patients with recurrent AF after LV isolation (p=0,0101).Conclusion. Inflammatory myocardium changes are an important link in the pathogenesis of AF and are associated with its recurrence.

Feasibility and safety of a single patch-based device for 30 days of continuous, uninterrupted ambulatory ecg monitoring

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): iRhythm Technologies, Inc. Background 14-day continuous ambulatory ECG monitoring (AEM) has been shown to have superior diagnostic yield to traditional 48-hour Holter monitoring for paroxysmal or undiagnosed atrial fibrillation and a wide range of clinical arrhythmias. 30-day recordings are typically from event recorders (non-continuous) and require frequent adhesive/electrode and battery replacement, all of which can impair patient compliance, wear time, and diagnostic yield. A new, uninterrupted patch-based AEM with next-generation breathable adhesive, storage, and battery life has received FDA clearance for 14-day wear. We evaluated the safety and feasibility of using this device for 30-days of continuous AEM. Methods We enrolled 30 participants with indications for AEM and applied a single Zio Monitor device for 30-days and analyzed wear time, analyzable time, and diagnostic yield, compared to with Zio Monitor 14-day use and with our standard, previous-generation product. Results In the cohort (76% female, 35% non-white, age 37±9 years, BMI 30.6±6.8), the median wear time was 29 continuous days (IQR 28-30) with no device changeouts and no clinically significant skin irritation. Although median analyzable times were high and similar across the previous (XT) and next-generation (Monitor) devices, the mean was significantly higher with Monitor, indicating fewer outliers that experienced premature termination of wear time. Arrhythmia yield (AF ≥30 sec, SVT ≥90 bpm & ≥4 bt, VT ≥100 bpm & ≥4 bt, inc PVT/TdP/VF, Pause ≥3 s, and/or AVB (any 2nd Deg or CHB) was significantly higher with the new Zio Monitor Device compared to Zio XT (80.9% vs 77.1%; p=0.017). Conclusion The application of a single, continuous, patch-based AEM for 30 days without adhesive (electrode) or battery replacement is feasible and safe. Analyzable time was high for 14-day and 30-day wear. The uninterrupted wear time and high compliance could lead to higher diagnostic yield than with traditional 30-day event recorders.