Detection Of Silent Atrial Fibrillation Research Articles

Abstract 17046: Accuracy of Apple Watch for Detection of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Background: Apple Watch (AW) for the detection of silent atrial fibrillation (AF) has shown encouraging results due to its photoplethysmogram (PPG) sensors. However, clinical accuracy studies show varying results. To the best of our knowledge, this is the first meta-analysis conducted to ascertain the pooled sensitivity, and specificity in AF detection using the AW. Methods: PubMed, Google Scholar, and Scopus were queried till May 2023 for studies examining the accuracy of AW in AF detection. The outcomes of interest were the sensitivity and specificity of AW for the detection of AF. Sensitivities and specificities were calculated using true positives, true negatives, false positives, and false negatives wherever applicable. A random-effects meta-analysis was conducted using the generic inverse variance method to pool sensitivities and specificities. Further, sensitivity analysis was performed excluding studies using KardiaBand. Results: A total of 12 studies with a patient population of 421,156 were included with a mean age of 63 years. 6/12 studies used AW series 4 or higher while 3/12 studies evaluated the use of KardiaBand on top of the AW. AF was detected in 3,184 patients from a total of 420,312 patients. Following pooled analysis, an overall point estimate of sensitivity was observed to be [0.87 (0.76, 0.98); Figure 1A]. Ten studies evaluated specificity, with the pooled estimate being [0.91 (0.87, 0.95); Figure 1B]. After excluding studies using KardiaBand, the sensitivity was reported as 0.84 [0.68, 1.00] and specificity was observed to be 0.94 [0.91, 0.98]. Conclusion: AW proved to be effective in detecting AF in our analysis owing to high overall sensitivity and specificity with and without the addition of KardiaBand. However, cardiologists should still exercise caution before undertaking action based on the electrocardiographic diagnosis generated by this wrist-worn monitor.

Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

Epidemiology of falls in St. Petersburg (based on research by the Department of Family Medicine)

Introduction: prevention of falls and fractures in older adults is a strategic task of the geriatric service.Aim: to estimate the prevalence of falls and its risk factors, as well as their association with geriatric syndromes in St. Petersburg.Materials and methods: analysis of the results of four studies conducted in St. Petersburg between 2009 and 2019. The total number of participants was 1398 persons aged 60 years and older. Main parameters: comprehensive geriatric assessment, measurement of cardio-ankle vascular index (CAVI) using Vasera VS-1500 volumetric sphygmography computer complex, detection of silent atrial fibrillation using «MyDiagnostick 1001R» device.Results: every third person falls at the age of 65 to 74 years old, and every second person falls at the age of 85 and older. In 50% of cases, falls are accompanied by injuries. The main factors associated with a higher risk of falls were arterial hypertension, atrial fibrillation, stroke/transient ischemic attack, cognitive impairment, sensory deficits, urinary incontinence, decreased physical functioning and CAVI above predicted value. Vision correction reduced the risk of falls by 46.7%, improved/good nutrition status according to self-assessment data by 55.3%, correction of urinary incontinence by 55.8%, improved cognitive functioning by 73.3%, and eating two or more fruits and vegetables a day by 66.0%. Individualized analysis of fall risk factors and development of fall prevention programs can reduce the risk of falls by a factor of nine within the first year.Conclusion: Screening to assess the risk of falls and compiling individual fall prevention programs can significantly reduce the risk of falls in older and senile age. The effectiveness of individual fall risk reduction programs decreases after the first year of implementation. Consequently, it is necessary to conduct annual screening of older patients to assess the risk of falls and subsequent adjustment of personalized fall prevention plan.

Incidence of atrial fibrillation in an opportunistic screening campaign in a general practitioner setting: a prospective population study

Abstract Introduction The detection of atrial fibrillation (AF) largely depends on the method used, thus the reported true rate of incident AF is controversial. The detection of symptomatic AF is straight forward in most cases, while it is unclear which strategies provide the best results for the detection of silent AF. Traditional methods like pulse palpation have a low accuracy and pulse guided ECG confirmation is not deemed cost-effective. Purpose The aim of the study was to detect the incidence symptomatic AF, using ECG, and opportunistic screening of silent AF, using an AF-detection capable automatic sphygmomanometer, in patients ≥65 years old in the general practitioner (GP) setting. Methods This was a population-based prospective cohort study of unselected general population referred for routine visits in 93 randomly selected GPs. Patients of both sexes of ≥65 years without previous diagnosis of AF were considered for the study. Each patient if symptomatic was directly referred to perform an ECG, otherwise if asymptomatic, underwent blood pressure monitoring with an AF-detection capable automatic sphygmomanometer followed by an ECG in case the device signaled AF. Results The final population comprised of 14987 individuals of ≥65 years old. The follow up extended to 16838 patient-years. The overall incidence of AF was 2.25% patient-years (95% CI 2.03–2.48). AF incidence was significantly higher in male (male/female ratio 1.29), overweight (BMI: 28.2 kg/m2 vs 27.0 kg/m2), older (79.1 vs 75.4 years old) individuals, with 44.9% belonging to the ≥80-year-old group. Other risk factors for incident AF were higher systolic BP measurements, history of stroke/TIA, congestive heart failure and chronic kidney disease. On multivariate analysis, age (annual increment), mitral valve disease, previous stroke and the number of annual visits (>10), were independent predictors of incidental AF. About 25% of cases were silent AF (0.56% patient-years, 95% CI 0.46–0.69) and were identified using the AF-detection capable automatic sphygmomanometer. Independent predictors of silent AF were age (per year increment), overweight (BMI >28 kg/m2) and the number of visits (>10) in the GPs' office. Conclusions We found a higher than previously reported incidence of AF possibly due to capturing silent AF. This simple protocol, using an AF-capable automatic sphygmomanometer, might be feasible and easily implemented on the routine GP care where high rates of coverage can be achieved. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Veneto Region Italy

Silent atrial fibrillation: clinical management and perspectives.

Silent atrial fibrillation (AF) is an asymptomatic atrial arrhythmia that can be diagnosed by chance during a systematic electrocardiogram, an external Holter, or from implanted cardiac devices. There is a significant body of the literature around silent AF, yet it remains largely underdiagnosed in everyday clinical practice. Meanwhile, new diagnostic tools have significantly improved the detection of silent AF, creating a potential for mass screening via new technologies and the promise of a major step forward in e-health progress. However, it is not yet known whether silent AF is associated with the same thromboembolic risk as symptomatic AF, and whether these asymptomatic and often short-lasting episodes therefore require anticoagulation therapy and rhythm management.

EFFICACY OF ATRIAL FIBRILLATION DETECTION IN CRYPTOGENIC STROKE ACCORDING TO DATA OF IMPLANTABLE LOOP RECORDERS: PILOT STUDY

Aim. To estimate the efficacy of invasive and non-invasive long-term ECG monitoring in comparison with conventional follow-up for the detection of silent atrial fibrillation (AF)/atrial flutter (AFL)/atrial tachycardia (AT) in patients with a cryptogenic stroke.Methods. The randomized, prospective, two-center study enrolled 36 patients who suffered cryptogenic stroke (CS) or transient ischemic attack (TIA) without past history of AF/AFL/AT. Patients were randomized in a 1:1:1 ratio to one of the three ECG monitoring strategies: standard arrhythmia monitoring (group I, n=12), ECG-monitoring with implantable loop recorder (group II, n=12), and ambulatory noninvasive ECG monitoring (group III, n=12). The primary endpoint was the time to the first detection of AF/AFL/AT. Patients, assigned to the group I, underwent an assessment at scheduled and unscheduled visits and received ambulatory ECG monitoring 28 days and 1 year after randomization. Patients in the group II underwent implantation of Reveal XT (Medtronic, USA) with the daily remote data transmission to CareLink Network. In the group III, for long-term external monitoring, Spyder system (WEB Biotechnology, Singapore) was used for up to 28 days.Results. During the first 28 days of observation, there were no significant differences in AF detection rates between groups I, II, and III: 0 (0%), 1 (8%), and 2 (17%) cases, respectively, р=0.537. During the year of observation, AF/AFL/AT episodes were detected in 1 case (8%) in the group I, 6 cases (50%) in the group II, and 2 cases (17%) in the group III, p=0.0486. The mean time from enrollment into the study to detection of the first AF/AFL/AT episode was 67 days (15; 97) in all groups. In the groups II and III, the first arrhythmia episodes were detected by monitoring devices on days 24 and 6, respectively. In most cases, arrhythmia episodes detected by long-term monitoring were asymptomatic. Recurrent stroke or TIA events occurred in group I and III (1 case in each group), but not in the group with implantable cardiac monitors. Subgroup analysis showed that significantly higher AF/AFL/AT detection rate was associated with stroke, CHA2DS2VASc score ≥2, and the presence of hypertension. For the 12 months of follow-up, the mean AF burden in the group II was 0.4 (0.2; 0.5) hours per day (1.6%). In the patients with recurrent stroke, AF burden was 3.2% compared to 0.9% in the rest of patients.Conclusion. Detection of silent AF with implantable cardiac monitors is superior to standard and long-term external monitoring in cryptogenic stroke patients.

Insertable cardiac monitor detection of silent atrial fibrillation in candidates for percutaneous patent foramen ovale closure.

An underlying atrial vulnerability or a preexisting misdiagnosed atrial fibrillation in some patients who are candidates for patent foramen ovale (PFO) closure may lead to an unnecessary percutaneous intervention. The aim of this work was to define paroxysmal atrial fibrillation rate, through a 6-month insertable loop-recorder monitoring, in patients over 55 years old with cryptogenic stroke and PFO. PFO closure criteria: significant right-to-left shunt and at least one high-risk feature (permanent right-to-left shunt, atrial septal aneurysm, prominent Eustachian valve, recurrent brain ischemia, previous deep vein thrombosis, thrombophilia). Insertable cardiac monitoring criteria: previous cryptogenic stroke, more than 55 years and at least one atrial fibrillation risk factor (heart failure, hypertension, age older than 65 years, diabetes, atrial runs, left atrium dilatation, left ventricle hypertrophy, pulmonary disease, thyroid disease, obesity). Atrial fibrillation detection threshold: arrhythmia duration longer than 5 min. From January 2008 to March 2017, 195 patients underwent to loop-recorder monitoring. A total of 70 (36%) patients were candidates for PFO closure. The 6-month silent atrial fibrillation rate was 11.4%. In the arrhythmia-free cohort, 28 patients (45.2%) underwent percutaneous foramen ovale closure (group A) and 34 (54.8%) were medically treated (group B). Atrial fibrillation detection rate was 14.3% in group A and 0% in group B. The 36-month atrial fibrillation-free survival was 76%. An occult preexisting atrial fibrillation may lead to unnecessary percutaneous foramen ovale closure in a significant proportion of patients. A 6-month loop-recorder monitoring may improve the patient oriented decision-making.

Silent Atrial Fibrillation: Unknown Truths

In recent years, silent atrial fibrillation (AF) has acquired broad interest in the neurologic and cardiovascular communities. Silent AF has been associated with similar morbidity and mortality as symptomatic AF and with similar rates of silent embolic events. In current clinical practice, AF remains mostly underdiagnosed, and 25% of patients with AF-associated cardioembolic stroke have not been previously diagnosed with AF. Silent AF detection methods include pulse palpation, ambulatory external electrocardiographic recordings, insertable cardiac monitors, and previously implanted cardiac devices with atrial lead. The increased interest is being directed toward detection of silent AF. Whether this will imply better outcomes for patients remains to be demonstrated.

Detection of silent atrial fibrillation by pacemaker in patients with stroke or transient ischemic attack

Detection of silent atrial fibrillation by pacemaker in patients with stroke or transient ischemic attack

Improved Detection of Silent Atrial Fibrillation Using 72-Hour Holter ECG in Patients With Ischemic Stroke

Adequate diagnosis of atrial fibrillation (AF), including paroxysmal AF, is an important part of stroke workup. Prolonged ECG monitoring may improve the detection of paroxysmal, previously undiagnosed AF (unknown AF). Therefore, we evaluated systematic 72-hour Holter ECG monitoring to detect unknown AF for the workup of patients with stroke. Unselected survivors of a stroke or transient ischemic attack (TIA) without known AF were enrolled in a prospective, multicenter cohort study of 72-hour Holter ECG monitoring in 9 German secondary and tertiary stroke centers between May 2010 and January 2011. In addition to standardized workup of stroke pathogenesis according to the German Stroke Unit protocol, all patients underwent 72-hour Holter ECG monitoring directly after admission. All ECGs were centrally analyzed by 2 independent observers. We determined the proportion of unknown AF and compared the detection rates of 72- and 24-hour monitoring. A total of 1135 patients were enrolled (mean age, 67 years [SD, 13.1 years], 45% women, 29% TIA). Unknown AF was detected in 49 out of 1135 patients (4.3%, [95% confidence interval, 3.4-5.2%]) by 72-hour ECG monitoring. Unknown AF was diagnosed in 29 patients (2.6%) within the first 24 hours of ECG monitoring, and in 20 more patients only by 72 hours of ECG monitoring. The number needed to screen by 72-hour ECG was 55 patients (95% confidence interval [35-123]) for each additional AF diagnosis. Patients with unknown AF were significantly older and had more often a history of previous stroke. Patients with unknown AF were equally distributed within categories of pathogenesis according to Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification. In unselected survivors of stroke or TIA, 72-hour ECG monitoring is feasible and improves the detection rate of silent paroxysmal AF.