The relationship between Holter electrocardiography (ECG) and atrial fibrillation (AF) diagnosis in the real world has not been widely evaluated in Japan.This is a claims-based retrospective study using a health insurance claims database provided by DeSC Healthcare Corporation. We identified patients with at least one Holter for any purpose during the data period from April 2015 to November 2020 and without diagnosis of AF before the tests (n = 19,739). We obtained a whole picture of Holter and AF diagnosis after correcting for population distribution bias in the dataset. Based on this picture and the assumption that the patient had AF at the 1^st Holter whose AF was detected for the first time at the second or subsequent Holter, we estimated the number of diagnosis with AF and overlooked AF by initial Holter. We conducted sensitivity analyses changing the definition of AF, the potential detection period, and the washout period (a period required to avoid including patients who have already been diagnosed with AF or who have already undergone several Holters) to confirm the validity of the base scenario.Among patients for analysis, 88.4% had only one Holter. The percentage of AF diagnosis by initial Holter was 7.6%. The percentage of AF overlooked by initial Holter was estimated to be 31.4% and this value did not change much by sensitivity analyses.It was estimated that approximately 30% of AF patients were overlooked by initial Holter, and reducing the overlooked rate will be a clinical challenge.

Supraventricular ectopic beats (SVEB) or ventricular ectopic beats (VEB) are common arrhythmia with uncertain occurrence and morphological diversity, so realizing their automatic localization is of great significance in clinical diagnosis.

We propose a modified U-net network: USV-net, it can simultaneously realize the automatic positioning of VEB and SVEB. The improvement consists of three parts: Firstly, we reconstruct part of the convolutional layer in U-net using group convolution to reduce the expression of redundant features. Secondly, a plug-and-play multi-scale 2D deformable convolution (MSDC) module is designed to extract cross-channel features of different scales. Thirdly, in addition to conventional output of U-net, we also compress and output the bottom feature map of U-net, the dual-output is trained through Dice-loss to take into account the learning of high/low resolution features of the model. We used the MIT-BIH arrhythmia database for patient-specific training, and used Sensitivity, Positive prediction rate and F1-scores to evaluate the effectiveness of our method.

The F1-scores of SVEB and VEB achieve the best results compared with other studies in different testing records. It is worth noting that the F1-scores of SVEB and VEB reached 81.3 and 95.4 in the 24 testing records. Moreover, our method is also at the forefront in Sensitivity and Positive prediction rate.

The method proposed in this paper has great potential in the detection of SVEB and VEB. We anticipate efficiency and accuracy of clinical detection of ectopic beats would be improved.

Palpitations are a common symptom managed by general practitioners and cardiologists; atrial fibrillation (AF) is the most common arrhythmia in adults. The recent commercial availability of smartphone-based devices and wearable technologies with arrhythmia detection capabilities has revolutionized the diagnosis and management of these common medical issues, as it has placed the power of arrhythmia detection into the hands of the patient. Numerous mobile health (mHealth) devices that can detect, record, and automatically interpret irregularities in heart rhythm and abrupt changes in heart rate using photoplethysmography (PPG)- and electrocardiogram-based technologies are now commercially available. As opposed to prescription-based external rhythm monitoring approaches, these devices are more inexpensive and allow for longer-term monitoring, thus increasing sensitivity for arrhythmia detection, particularly for patients with infrequent symptoms possibly due to cardiac arrhythmias. These devices can be used to correlate symptoms with cardiac arrhythmias, assess efficacy and toxicities of arrhythmia therapies, and screen the population for serious rhythm disturbances such as AF. Although several devices have received clearance for AF detection from the United States Food & Drug Administration, limitations include the need for ECG confirmation for arrhythmias detected by PPG alone, false positives, false negatives, charging requirements for the battery, and financial cost. In summary, the growth of commercially available devices for remote, patient-facing rhythm monitoring represents an exciting new opportunity in the care of patients with palpitations and known or suspected dysrhythmias. Physicians should be familiar with the evidence that underlies their added value to patient care and, importantly, their current limitations.

Palpitations are one of the most common reasons for medical consultation. They tend to worry patients and can affect their quality of life. They are often a symptom associated with cardiac rhythm disorders, although there are other etiologies. For diagnosis, it is essential to be able to reliably correlate the symptoms with an electrocardiographic record allowing the identification or ruling out of a possible rhythm disorder. However, reaching a diagnosis is not always simple, given that they tend to be transitory symptoms and the patient is frequently asymptomatic at the time of assessment. In recent years, electrocardiographic monitoring systems have incorporated many technical improvements that solve several of the 24-h Holter monitor limitations. The objective of this review is to provide an update on the different monitoring methods currently available, remarking their indications and limitations, to help healthcare professionals to appropriately select and use them in the work-up of patients with palpitations.

The incidence of ventricular tachycardia (VT) in preoperative evaluation for noncardiac surgery in general hospitals has not been established. The aim of this study was to determine the incidence of VT, characteristics of patients with VT, characteristics of VT, and significance of VT in patients undergoing 24-h Holter monitoring as preoperative evaluation for noncardiac surgery. In 601 patients, VT was detected in 46 patients (7.7%). In patients with VT, left ventricular ejection fraction (LVEF) was lower (62.6 ± 9.3% vs. 66.6 ± 8.9%, p = 0.003), and B-type natriuretic peptide (BNP) was higher compared with patients without VT (median, 52.5 pg/mL vs. 32.8 pg/mL, p = 0.02). The maximum number of consecutive beats of VT was more frequent in the patients with LVEF < 50% than in the patients with LVEF ≥ 50% (median, 11.5 beats vs. 3.0 beats, p = 0.01). Forty patients (87%) underwent scheduled surgery without major complications.

Clinical safety findings remain one of the reasons for attrition of drug candidates during clinical development. Cardiovascular liabilities are not consistently detected in early-stage clinical trials and often become apparent when drugs are administered chronically for extended periods of time. Vital sign data collection outside of the clinic offers an opportunity for deeper physiological characterization of drug candidates and earlier safety signal detection. A working group representing expertise from biopharmaceutical and technology sectors, US Food and Drug Administration (FDA) public-private partnerships, academia, and regulators discussed and presented a remote cardiac monitoring case study at the FNIH Biomarkers Consortium Remote Digital Monitoring for Medical Product Development workshop to examine applicability of the biomarker qualification evidentiary framework by the FDA. This use case examined the components of the framework, including the statement of need, the context of use, the state of the evidence, and the benefit/risk profile. Examination of results from 2 clinical trials deploying 510(k)-cleared devices for remote cardiac data collection demonstrated the need for analytical and clinical validity irrespectively of the regulatory status of a device of interest, emphasizing the importance of data collection method assessment in the context of intended use. Additionally, collection of large amounts of ambulatory data also highlighted the need for new statistical methods and contextual information to enable data interpretation. A wider adoption of this approach for drug development purposes will require collaborations across industry, academia, and regulatory agencies to establish methodologies and supportive data sets to enable data interpretation and decision-making.

Atrial fibrillation (Afib) is the most common and underestimated cardiac arrhythmia with a lifetime risk of >35% after the age of 55 years and the risk continues to rise exponentially. Afib leads to stasis of blood within the atria allowing clot formation and increasing the risk for systemic embolization leading to strokes. Outcomes due to Afib can improve significantly with appropriate treatment. Thus, the need for convenient, well-tolerated, cost-effective cardiac monitoring for Afib is needed. The study aims to evaluate the various newer devices and compare them with traditional Holter monitoring, keeping diagnostic yield, cost-effectiveness, and patients' convenience in mind. Though Holter monitoring is simple and non-expensive, it has major limitations including limited recording capacity, inability for real-time recordings, and inconvenience to patients. Zio Patch (iRhythm Technologies, Inc; San Francisco, CA) and other loop recording devices are patient-friendly, inexpensive, and can offer real-time data for longer days. More prospective studies are needed to evaluate the sensitivity, specificity, and the actual number of patients getting benefits from newer devices by diagnosing Afib sooner and start early prevention therapy.

The diagnostic yield of 24-hour ECG Holter monitoring (24H) is currently overcome by alternative ECG monitoring techniquesand it needs to be optimized. The recognition of inter-atrial block (IAB) has emerged as a reliable indicator of patients at risk of atrial fibrillation relapses, and its role enhancing the yield of 24H is yet to be determined. We hypothesized that a presumably low yield of 24H may be ameliorated by means of incorporating the assessment for IAB.

We retrospectively analyzed 1017 consecutive 24H registers performed in a Multidisciplinary Integrated Health Care Institution, in which a restrictive definition of diagnostic 24H findings was used. A univariate and multivariate regression analysis served to determine the variables associated with a higher 24H's yield, including the requesting medical specialty, type of indication and a number of clinical, echocardiographic and ECG variables, including IAB.

The mean age of our population was 62 ± 17 years (55% males). The majority of 24H were indicated from the Cardiology department (48%). The overall yield was 12.8%, higher for the assessment of the integrity of the electrical conduction system (26.1%) and poorer for the assessment of syncope (3.2%) and cryptogenic stroke (4.6%). The variables associated with higher diagnostic performance were indication from Cardiology (p < 0.001), IAB (p = 0.004), structural heart disease (p = 0.008) and chronic renal failure (p = 0.009). Patients ≤ 50 years old only retrieved a 7% yield. In the multivariate analysis, indication from Cardiology and IAB remained significant predictors of higher 24H's yield. In a secondary analysis including echocardiographic data, only identification of IAB remained statistically significant.

The recognition of IAB and the type of indication are major determinants of a higher 24H's diagnostic yield and may help to optimize the selection of candidates.