Comparison of Arrhythmia Detection by 24-Hour Holter and 14-Day Continuous Electrocardiography Patch Monitoring

Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters

Background and Objectives: Chronic heart failure (CHF) caused by ischemic heart disease (IHD) is the leading cause of death worldwide and presents significant health challenges. Effective management of IHD requires prevention, early detection, and treatment to improve patient outcomes. This study aims to expand the diagnostic utility of various 24 h Holter ECG parameters, such as T-wave alternans (TWA), late ventricular potentials (LVPs), and heart rate variability (HRV) in patients with CHF caused by IHD. Additionally, we seek to explore the association between these parameters and other comorbid conditions affecting the prognosis of CHF patients. Materials and Methods: We conducted a prospective case-control study with 150 patients divided into two subgroups: 100 patients with CHF caused by IHD, and 50 patients in the control group. Data included medical history, physical examination, laboratory tests, echocardiography, and 24 h Holter monitoring. Results: Our comparative analysis demonstrated that both TWA and LVPs were significantly higher in patients with CHF compared to the control group (p < 0.01), indicating increased myocardial electrical vulnerability in CHF patients. Both time and frequency-domain HRV parameters were significantly lower in the CHF group. However, the ratio of NN50 to the total count of NN intervals (PNN50) showed a borderline significance (p = 0.06). While the low-frequency (LF) domain was significantly lower in CHF patients, the high-frequency (HF) domain did not differ significantly between groups. Acceleration and deceleration capacities were also significantly altered in CHF patients. Categorizing CHF patients by left ventricular ejection fraction (LVEF) revealed that the mean of the 5-min normal-to-normal intervals over the complete recording (SDNN Index) was significantly higher in patients with LVEF ≥ 50% compared to those with CHF with reduced EF and CHF with mildly reduced EF (p < 0.001), whereas the other HRV parameters showed no significant differences among the groups. Conclusions: Holter ECG parameters can become a reliable tool in the assessment of patients with CHF. The integration of multiple Holter ECG parameters, such as TWA, LVPs, and HRV, can significantly enhance the diagnostic assessment of CHF caused by IHD. This comprehensive approach allows for a more nuanced understanding of the patient's condition and potential outcomes.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

A Review on Atrial Fibrillation Detection From Ambulatory ECG

Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.

Holter ECG for Syncope Evaluation in the Internal Medicine Department-Choosing the Right Patients

Physicians use Holter electrocardiography (ECG) monitoring to evaluate some patients with syncope in the internal medicine department. We questioned whether Holter ECG should be used in the presented setting. Included were all consecutive patients admitted with syncope to one of our nine internal medicine departments who had completed a 24 h Holter ECG between 2018 and 2021. A diagnostic Holter was defined as one which altered the patient’s treatment and met ESC/ACC/AHA diagnostic criteria. A total of 478 Holter tests were performed for syncope evaluation during admission to an internal medicine department in the study period. Of them, 25 patients (5.2%) had a diagnostic Holter finding. Sinus node dysfunction was the most frequent diagnostic recording (13 patients, 52%). In multivariant analysis, predictors for diagnostic Holter were older age (OR 1.35, 95% CI 1.08−1.68), heart failure with preserved ejection fraction (OR 4.1, 95% CI 1.43−11.72), and shorter duration to Holter initiation (OR 0.73, 95% CI 0.56−0.96). There was a positive correlation between time from admission to Holter and hospital stay, r(479) = 0.342, p < 0.001. Our results suggest that completing a 24 h Holter monitoring during admission to the internal medicine department should be restricted to patients with a high pre-test probability to avoid overuse and possible harm.

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF.

Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335).

Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home.

Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data’s quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

BACKGROUND

Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention.

OBJECTIVE

This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology.

METHODS

We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as "wearables," "machine learning," and "cardiovascular disease." Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready).

RESULTS

After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes.

CONCLUSIONS

Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.

Arrhythmia Detection is Improved by 14-Day Continuous Electrocardiography Patch Monitoring and CHA2DS2-VASc Score

BACKGROUND

Arrhythmias are not always easy to capture because they are often paroxysmal or asymptomatic.

METHODS

Using the CHA2DS2-VASc score for arrhythmia risk assessment, a 14-day electrocardiography monitor patch was used to evaluate patients with no documented history of arrhythmia.

RESULTS

Ninety-three patients (mean age 59.8 ± 12.0 years, 46.2% female) received 14-day electrocardiography telemonitoring, and 14 patients (15%) were diagnosed with arrhythmias during a follow-up of 1004.4 person-days (mean recorded days 10.8 ± 4.1). The patients who were detected to have arrhythmias were older and had a higher prevalence of heart failure and chronic kidney disease. The result showed that arrhythmias were more likely to develop during a 14-day monitoring period in the patients with a CHA2DS2-VASc score of ≥ 3 or ≥ 4. Atrioventricular block was more likely to be detected in the patients with a CHA2DS2-VASc score of ≥ 3 or ≥ 4 during 7-day or 14-day monitoring periods. Ventricular tachycardia was also more likely to be detected in the patients with a CHA2DS2-VASc score of ≥ 4 or ≥ 5 during a 14-day monitoring period. When evaluating the risk of arrhythmia, a CHA2DS2-VASc score of ≥ 3 or ≥ 4 was associated with a higher risk of any arrhythmias during a 14-day monitoring period, while a CHA2DS2-VASc score of ≥ 4 was associated with a higher risk of any arrhythmias during a 7-day monitoring period.

CONCLUSIONS

The results may suggest that a 14-day monitoring period is more favorable to detect arrhythmias. Atrioventricular block and ventricular tachycardia were more likely to develop in the patients with a higher CHA2DS2-VASc score.