Safety of outpatient commencement of sotalol

Background

Inpatient monitoring is recommended for sotalol initiation.

Objective

The purpose of this study was to assess the safety of outpatient sotalol commencement.

Methods

This is a multicenter, retrospective, observational study of patients initiated on sotalol in an outpatient setting. Serial electrocardiogram monitoring at day 3, day 7, 1 month, and subsequently as clinically indicated was performed. Corrected QT (QTc) interval and clinical events were evaluated.

Results

Between 2008 and 2023, 880 consecutive patients who were commenced on sotalol were evaluated. Indications were atrial fibrillation/flutter in 87.3% (n = 768), ventricular arrhythmias in 9.9% (n = 87), and other arrhythmias in 2.8% (n = 25). The daily dosage at initiation was 131.0 ± 53.2 mg/d. The QTc interval increased from baseline (431 ± 32 ms) to 444 ± 37 ms (day 3) and 440 ± 33 ms (day 7) after sotalol initiation (P < .001). Within the first week, QTc prolongation led to the discontinuation of sotalol in 4 and dose reduction in 1. No ventricular arrhythmia, syncope, or death was observed during the first week. Dose reduction due to asymptomatic bradycardia occurred in 3 and discontinuation due to dyspnea in 3 within the first week. Overall, 1.1% developed QTc prolongation (>500 ms/>25% from baseline); 4 within 3 days, 1 within 1 week, 4 within 60 days, and 1 after >3 years. Discontinuation of sotalol due to other adverse effects occurred in 41 patients within the first month of therapy.

Conclusion

Sotalol initiation in an outpatient setting with protocolized follow-up is safe, with no recorded sotalol-related mortality, ventricular arrhythmias, or syncope. There was a low incidence of significant QTc prolongation necessitating discontinuation within the first month of treatment. Importantly, we observed a small incidence of late QT prolongation, highlighting the need for vigilant outpatient surveillance of individuals on sotalol.

Detecting QT prolongation from a single-lead ECG with deep learning

For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I-the lead that is often available in ambulatory ECG monitors-and use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. QTNet-a deep neural network that infers QT intervals from Lead-I ECG-was trained using over 3 million ECGs from 653 thousand patients at the Massachusetts General Hospital and tested on an internal-test set consisting of 633 thousand ECGs from 135 thousand patients. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another healthcare institution. On both evaluations, the model achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). Finally, QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. These results show that drug-induced QT prolongation risk can be tracked from ECG Lead-I using deep learning.

A Scoping Review of Varying Mobile Electrocardiographic Devices

The electrocardiogram (ECG) can now be measured using mobile devices. Mobile ECG devices, which are defined as devices capable of recording and transmitting non-standard ECGs, offer numerous advantages such as cost-effectiveness and being user-friendly. Mobile ECG can also extend recording lengths (e.g., 2 days, 14 days), which is necessary to capture important intermittent events (e.g., cardiac arrhythmias) and evaluate prognostic risk markers (e.g., prolonged corrected QT (QTc) interval). Some mobile ECG devices can even connect to broadband networks allowing patients to remotely transmit their ECG to a clinician. This article systematically examines different mobile ECG devices used in prior studies and provides a detailed assessment of five diverse yet commonly used mobile ECG devices: AliveCor KardiaMobile; AliveCor KardiaMobile 6L; iRhythm ZioPatch; Apple Smartwatch ECG; and CardioSecur System. These mobile ECG devices are diverse in the number of leads measured and the duration of monitoring. Similar to their diversity, there has been a wide range of clinical applications of mobile ECG devices. Despite significant progress, questions regarding data quality, and clinican and patient acceptance and compliance persist.

Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.

S, Poddubskaya E, Novikova A, Bykova A, Kopylov P. A single-lead ECG based cardiotoxicity detection in patients on polychemotherapy

Background

Anti-cancer treatment can be fraught with cardiovascular complications, which is the most common cause of death among oncological survivors. Without appropriate cardiomonitoring during anti-cancer treatment, it becomes challenging to detect early signs of cardiovascular complications. In order to achieve higher survival rates, it is necessary to monitor oncological patients outpatiently after anti-cancer treatment administration. In this regard, we aim to evaluate the efficacy of single-lead ECG remote monitoring to detect cardiotoxicity in cancer patients with minimal cardiovascular diseases after the first cycle of polychemotherapy.

Materials and methods

The study included patients 162 patients over 18 years old with first diagnosed different types of solid tumors, planed for adjuvant (within 8 weeks after surgery) or neoadjuvant polychemotherapy. All patients were monitored, outpatiently, during 14-21 days (depending on the regimen of polychemotherapy) after polychemotherapy administration using single-lead ECG.

Results

QTc > 500 mc prolongation was detected in 8 patients (6.6 %), first-diagnosed arial fibrillation was detected in 11 patients (9 %) in period after chemotherapy administration. Moreover, left ventricular diastolic dysfunction using single-lead ECG after polychemotherapy was detected in 49 (40.1 %) patients with sensitivity 80 %, specificity 95 %, AUC 0.88 (95 % CI, 0.82-0.93).

Conclusions

The side effects of cancer treatment may cause life-threatening risks. Early identification of cardiotoxicity plays a vital role in the solution of this problem. Using portable devices to detect early cardiotoxicity is a simple, convenient and affordable screening method, that can be used for promptly observation of patients.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

Corrected QT Interval (QTc) Diagnostic App for the Oncological Routine: Development Study

BACKGROUND

Numerous antineoplastic drugs such as chemotherapeutics have cardiotoxic side effects and can lead to long QT syndrome (LQTS). When diagnosed and treated in time, the potentially fatal outcomes of LQTS can be prevented. Therefore, regular electrocardiogram (ECG) assessments are critical to ensure patient safety. However, these assessments are associated with patient discomfort and require timely support of the attending oncologist by a cardiologist.

OBJECTIVE

This study aimed to examine whether this approach can be made more efficient and comfortable by a smartphone app (QTc Tracker), supporting single-lead ECG records on site and transferring to a tele-cardiologist for an immediate diagnosis.

METHODS

To evaluate the QTc Tracker, it was implemented in 54 cancer centers in Germany. In total, 266 corrected QT interval (QTc) diagnoses of 122 patients were recorded. Moreover, a questionnaire on routine ECG workflow, turnaround time, and satisfaction (1=best, 6=worst) was answered by the centers before and after the implementation of the QTc Tracker.

RESULTS

Compared to the routine ECG workflow, the QTc Tracker enabled a substantial turnaround time reduction of 98% (mean 2.67, 95% CI 1.72-2.67 h) and even further time efficiency in combination with a cardiologic on-call service (mean 12.10, 95% CI 5.67-18.67 min). Additionally, nurses and patients reported higher satisfaction when using the QTc Tracker. In particular, patients' satisfaction sharply improved from 2.59 (95% CI 2.41-2.88) for the routine ECG workflow to 1.25 (95% CI 0.99-1.51) for the QTc Tracker workflow.

CONCLUSIONS

These results reveal a significant improvement regarding reduced turnaround time and increased user satisfaction. Best patient care might be guaranteed as the exposure of patients with an uncontrolled risk of QTc prolongations can be avoided by using the fast and easy QTc Tracker. In particular, as regular side-effect monitoring, the QTc Tracker app promises more convenience for patients and their physicians. Finally, future studies are needed to empirically test the usability and validity of such mobile ECG assessment methods.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04055493; https://classic.clinicaltrials.gov/ct2/show/NCT04055493.

Feasibility of using KardiaMobile-L6 for QT interval monitoring during the early phase of the COVID-19 pandemic in critical care patients

The electrocardiogram (ECG) represents an essential tool to determine cardiac electrical abnormalities in COVID-19 patients, the effects of anti-SARS-CoV-2 drugs, and potential drug interactions. Smartphone-based heart monitors have increased the spectrum of ECG monitoring however, we are not aware of its reliability in critically ill COVID-19 patients. We aim to evaluate the feasibility and reliability of nurse-performed smartphone electrocardiography for QT interval monitoring in critically ill COVID-19 patients using KardiaMobile-6L compared with the standard 12-lead ECG. An observational comparative study was conducted comparing consecutive KardiaMobile-6L and 12-lead ECG recordings obtained from 20 patients admitted to the intensive care unit with SARS-CoV-2 infection and on invasive mechanical ventilation. The heart rate-corrected QT (QTc) intervals measured by KardiaMobile-6L and 12-lead ECG were compared. In 60 percent of the recordings, QTc intervals measured by KardiaMobile-6L matched those by 12-lead ECG. The QTc intervals measured by KardiaMobile-6 and 12-lead ECG were 428 ± 45 ms and 425 ± 35 ms (p = 0.82), respectively. The former demonstrated good agreement (bias = 2.9 ms; standard deviation of bias = 29.6 ms) with the latter, using the Bland-Altman method of measurement agreement. In all but one recording, KardiaMobile-6L demonstrated QTc prolongation. QTc interval monitoring with KardiaMobile-6L in critically ill COVID-19 patients was feasible and demonstrated reliability comparable to the standard 12-lead ECG.

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances in wearable ECG technology, however, raise the possibility of automated out-of-hospital QT tracking. Applications of Deep Learning (DL) - a subfield within Machine Learning - in ECG analysis holds the promise of automation for a variety of classification and regression tasks. In this work, we propose a residual neural network, QTNet, for the regression of QT intervals from a single lead (Lead-I) ECG. QTNet is trained in a supervised manner on a large ECG dataset from a U.S. hospital. We demonstrate the robustness and generalizability of QTNet on four test-sets; one from the same hospital, one from another U.S. hospital, and two public datasets. Over all four datasets, the mean absolute error (MAE) in the estimated QT interval ranges between 9ms and 15.8ms. Pearson correlation coefficients vary between 0.899 and 0.914. By contrast, QT interval estimation on these datasets with a standard method for automated ECG analysis (NeuroKit2) yields MAEs between 22.29ms and 90.79ms, and Pearson correlation coefficients 0.345 and 0.620. These results demonstrate the utility of QTNet across distinct datasets and patient populations, thereby highlighting the potential utility of DL models for ubiquitous QT tracking.Clinical Relevance- QTNet can be applied to inpatient or ambulatory Lead-I ECG signals to track QT intervals. The method facilitates ambulatory monitoring of patients at risk of QT prolongation.

Comparison of AliveCor KardiaMobile Six-Lead ECG with Standard ECG in Pediatric Patients

The AliveCor KardiaMobile (ACKM) is a remote electrocardiogram (ECG) monitoring device. Little research has been conducted on its accuracy with pediatric patients. This prospective study aims to compare the ACKM six-lead device with a standard fifteen-lead ECG in measuring the QTc, QRS, and axis in pediatric patients. Pediatric patients ages 5 to 21 years were enrolled prospectively to have their ECG recorded using an ACKM six-lead device following a recording with the standard 15-lead ECG. A pediatric electrophysiologist measured the QTc, QRS interval, and QRS axis for both ECGs. Bland-Altman analysis was performed to assess agreement among measurements. The study included 141 patients. The mean age was 12.3 ± 4.4 years. Average heart rate was 79 ± 16 bpm. The mean difference in the QTc measurements for a paired standard ECG and ACKM was - 0.6 ms [95% confidence interval - 48 to 47 ms]. Of the ACKM QTc measurements, 117 (83%) were within 30 ms of the standard ECG. The mean difference in paired QRS measurements was - 1.3 ms [95% confidence interval - 23 to 21 ms]. Of the ACKM QRS measurements, 134 (95%) were within 20 ms of the standard ECG. The measured axis was the same for 84% of ACKM and standard ECGs. Over 80% of the ACKM six-lead ECGs produced QTc, QRS, and axis deviation measurements within a clinically useful range of the standard ECG. However, it is not accurate enough to be used consistently in place of a standard ECG for QTc and QRS measurement for pediatric patients.

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess the cardiac electrical activities and detect possible abnormal conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for ECG signal analysis. The first-level model is composed of a Memory-Augmented Deep AutoEncoder with GAN (MadeGAN), which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmia identification, which is achieved by integrating the transfer learning technique to transfer knowledge from the first-level learning with the multi-branching architecture to handle the data-lacking and imbalanced data issues. We evaluate the performance of the proposed framework using real-world ECG data from the MIT-BIH arrhythmia database. Experimental results show that our proposed model outperforms existing methods that are commonly used in current practice.

Clinical Validation of a Smartphone-based Handheld ECG Device: A Validation Study

BACKGROUND

Remote cardiac monitoring and screening have already become an integral telemedicine component. The wide usage of several different wireless electrocardiography (ECG) devices warrants a validation study on their accuracy and reliability.

METHODS

Totally, 300 inpatients with the Nabz Hooshmand-1 handheld ECG device and the GE MAC 1200 ECG system (as the reference) were studied to check the accuracy of the devices in 1 and 6-limb lead performance. Simultaneous 10-second resting ECGs were assessed for the most common ECG parameters in lead I. Afterward, 6-lead ECGs (limb leads), were performed immediately and studied for their morphologies.

RESULTS

Of the 300 patients, 297 had acceptable ECG quality in both devices for simultaneous lead I ECGs. The ECGs were inspected on-screen by a cardiologist for their rhythms, rates, axes, numbers, morphologies of premature atrial and ventricular beats, morphologies and amplitudes of PQRST waves, P-wave durations, QRS-wave durations, P-R intervals, and QT intervals. No significant differences were detected between the devices, and no major abnormalities were missed. Six-limb lead ECGs were obtained in 284 patients, of whom 281 had acceptable quality in ECGs by both devices. The morphology matching evaluation of the ECGs demonstrated an overall 98% compatibility rate, with the highest compatibility in lead I and the lowest in lead augmented vector foot.

CONCLUSIONS

The diagnosis of critical pathological rhythms, including atrial fibrillation and high-grade atrioventricular node block, was not missed by the Nabz Hooshmand-1 and GE MAC 1200 ECG devices. Accordingly, rhythm detection as the primary purpose of handheld ECG devices was highly accurate. Both devices had acceptable sensitivity to diagnose long P-R and long and short QT intervals. Although the modern technology of smartphones and the physical inability for the 6-limb mode might cause old patients difficulty in utilizing such devices, their use for screening and follow-up is safe.

The Relationship between Body Composition and ECG Ventricular Activity in Young Adults

This study aimed to determine the correlation between body composition (measured as weight, body mass index, and body fat percentage (BFP)) and electrocardiographic ventricular parameters (the QT and TQ intervals and the ratios between the electrical diastole and electrical systole (TQ/QT) and between the cardiac cycle and electrical diastole (RR/TQ), both for uncorrected and corrected intervals) in a sample of 50 healthy subjects (age interval 19-23 years, mean age 21.27 ± 1.41 years, 33 women and 17 men). Subjects' measurements were performed with a bioimpedancemetry body composition analyzer and a portable ECG monitor with six leads. Starting from the correlations obtained between the investigated continuous variables, we performed a standard linear regression analysis between the body composition parameters and the ECG ones. Our results revealed that some of our regression models are statistically significant (p < 0.001). Thus, a specific part of the variability of the dependent variables (ECG ventricular activity parameters for corrected QT intervals) is explained by the independent variable BFP. Therefore, body composition influences ventricular electrical activity in young adults, which implies a differentiated interpretation of the electrocardiogram in these situations.

Clinical Validation of Automated Corrected QT-Interval Measurements From a Single Lead Electrocardiogram Using a Novel Smartwatch

Introduction

The Withings Scanwatch (Withings SA, Issy les Moulineaux, France) offers automated analysis of the QTc. We aimed to compare automated QTc-measurements using a single lead ECG of a novel smartwatch (Withings Scanwatch, SW-ECG) with manual-measured QTc from a nearly simultaneously recorded 12-lead ECG.

Methods

We enrolled consecutive patients referred to a tertiary hospital for cardiac workup in a prospective, observational study. The QT-interval of the 12-lead ECG was manually interpreted by two blinded, independent cardiologists through the tangent-method. Bazett's formula was used to calculate QTc. Results were compared using the Bland-Altman method.

Results

A total of 317 patients (48% female, mean age 63 ± 17 years) were enrolled. HR-, QRS-, and QT-intervals were automatically calculated by the SW in 295 (93%), 249 (79%), and 177 patients (56%), respectively. Diagnostic accuracy of SW-ECG for detection of QTc-intervals ≥ 460 ms (women) and ≥ 440 ms (men) as quantified by the area under the curve was 0.91 and 0.89. The Bland-Altman analysis resulted in a bias of 6.6 ms [95% limit of agreement (LoA) -59 to 72 ms] comparing automated QTc-measurements (SW-ECG) with manual QTc-measurement (12-lead ECG). In 12 patients (6.9%) the difference between the two measurements was greater than the LoA.

Conclusion

In this clinical validation of a direct-to-consumer smartwatch we found fair to good agreement between automated-SW-ECG QTc-measurements and manual 12-lead-QTc measurements. The SW-ECG was able to automatically calculate QTc-intervals in one half of all assessed patients. Our work shows, that the automated algorithm of the SW-ECG needs improvement to be useful in a clinical setting.

ESC guidance for the diagnosis and management of cardiovascular disease during the COVID-19 pandemic: part 2-care pathways, treatment, and follow-up

AIMS

Since its emergence in early 2020, the novel severe acute respiratory syndrome coronavirus 2 causing coronavirus disease 2019 (COVID-19) has reached pandemic levels, and there have been repeated outbreaks across the globe. The aim of this two part series is to provide practical knowledge and guidance to aid clinicians in the diagnosis and management of cardiovascular (CV) disease in association with COVID-19.

METHODS AND RESULTS

A narrative literature review of the available evidence has been performed, and the resulting information has been organized into two parts. The first, which was reported previously, focused on the epidemiology, pathophysiology, and diagnosis of CV conditions that may be manifest in patients with COVID-19. This second part addresses the topics of: care pathways and triage systems and management and treatment pathways, both of the most commonly encountered CV conditions and of COVID-19; and information that may be considered useful to help patients with CV disease (CVD) to avoid exposure to COVID-19.

CONCLUSION

This comprehensive review is not a formal guideline but rather a document that provides a summary of current knowledge and guidance to practicing clinicians managing patients with CVD and COVID-19. The recommendations are mainly the result of observations and personal experience from healthcare providers. Therefore, the information provided here may be subject to change with increasing knowledge, evidence from prospective studies, and changes in the pandemic. Likewise, the guidance provided in the document should not interfere with recommendations provided by local and national healthcare authorities.

ESC guidance for the diagnosis and management of cardiovascular disease during the COVID-19 pandemic: part 2-care pathways, treatment, and follow-up

AIMS

Since its emergence in early 2020, the novel severe acute respiratory syndrome coronavirus 2 causing coronavirus disease 2019 (COVID-19) has reached pandemic levels, and there have been repeated outbreaks across the globe. The aim of this two part series is to provide practical knowledge and guidance to aid clinicians in the diagnosis and management of cardiovascular (CV) disease in association with COVID-19.

METHODS AND RESULTS

A narrative literature review of the available evidence has been performed, and the resulting information has been organized into two parts. The first, which was reported previously, focused on the epidemiology, pathophysiology, and diagnosis of CV conditions that may be manifest in patients with COVID-19. This second part addresses the topics of: care pathways and triage systems and management and treatment pathways, both of the most commonly encountered CV conditions and of COVID-19; and information that may be considered useful to help patients with CV disease (CVD) to avoid exposure to COVID-19.

CONCLUSION

This comprehensive review is not a formal guideline but rather a document that provides a summary of current knowledge and guidance to practicing clinicians managing patients with CVD and COVID-19. The recommendations are mainly the result of observations and personal experience from healthcare providers. Therefore, the information provided here may be subject to change with increasing knowledge, evidence from prospective studies, and changes in the pandemic. Likewise, the guidance provided in the document should not interfere with recommendations provided by local and national healthcare authorities.

Mobile health technologies in the diagnosis and management of atrial fibrillation

PURPOSE OF REVIEW

Atrial fibrillation is the most common sustained rhythm abnormality and is associated with stroke, heart failure, cognitive decline, and premature death. Digital health technologies using consumer-grade mobile technologies (i.e. mHealth) capable of recording heart rate and rhythm can now reliably detect atrial fibrillation using single lead or multilead ECG or photoplethysmography (PPG). This review will discuss how these developments are being used to detect and manage atrial fibrillation.

RECENT FINDINGS

Studies have established the accuracy of mHealth devices for atrial fibrillation detection. The feasibility of using mHealth technology to screen for atrial fibrillation has also been established, though the utility of screening is controversial. In addition to screening, key aspects of atrial fibrillation management can also be performed remotely and effectively using mHealth, though with some important limitations.

SUMMARY

mHealth technologies have proven disruptive in the diagnosis and management of atrial fibrillation. Healthcare providers can leverage these advances to better care for their atrial fibrillation patients whenever necessary.

Mobile Health for Arrhythmia Diagnosis and Management

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.

Manual QT interval measurement with a smartphone-operated single-lead ECG versus 12-lead ECG: a within-patient diagnostic validation study in primary care

OBJECTIVE

To determine the accuracy of QT measurement in a smartphone-operated, single-lead ECG (1L-ECG) device (AliveCor KardiaMobile 1L).

DESIGN

Cross-sectional, within-patient diagnostic validation study.

SETTING/PARTICIPANTS

Patients underwent a 12-lead ECG (12L-ECG) for any non-acute indication in primary care, April 2017-July 2018.

INTERVENTION

Simultaneous recording of 1L-ECGs and 12L-ECGs with blinded manual QT assessment. OUTCOMES OF INTEREST: (1) Difference in QT interval in milliseconds (ms) between the devices; (2) measurement agreement between the devices (excellent agreement <20 ms and clinically acceptable agreement <40 ms absolute difference); (3) sensitivity and specificity for detection of extreme QTc (short (≤340 ms) or long (≥480 ms)), on 1L-ECGs versus 12L-ECGs as reference standard. In case of significant discrepancy between lead I/II of 12L-ECGs and 1L-ECGs, we developed a correction tool by adding the difference between QT measurements of 12L-ECG and 1L-ECGs.

RESULTS

250 ECGs of 125 patients were included. The mean QTc interval, using Bazett's formula (QTcB), was 393±25 ms (mean±SD) in 1L-ECGs and 392±27 ms in lead I of 12L-ECGs, a mean difference of 1±21 ms, which was not statistically different (paired t-test (p=0.51) and Bland Altman method (p=0.23)). In terms of agreement between 1L-ECGs and lead I, QTcB had excellent agreement in 66.9% and clinically acceptable agreement in 93.4% of observations. The sensitivity and specificity of detecting extreme QTc were 0% and 99.2%, respectively. The comparison of 1L-ECG QTcB with lead II of 12L-ECGs showed a significant difference (p=<0.01), but when using a correction factor (+9 ms) this difference was cancelled (paired t-test (p=0.43) or Bland Altman test (p=0.57)). Moreover, it led to improved rates of excellent (71.3%) and clinically acceptable (94.3%) agreement.

CONCLUSION

Smartphone-operated 1L-ECGs can be used to accurately measure the QTc interval compared with simultaneously obtained 12L-ECGs in a primary care population. This may provide an opportunity for monitoring the effects of potential QTc-prolonging medications.

Clinical validation and evaluation of a novel six-lead handheld electrocardiogram recorder compared to the 12-lead electrocardiogram in unselected cardiology patients (EVALECG Cardio)

Aims

Handheld electrocardiogram (ECG) monitors are increasingly used by both healthcare workers and patients to diagnose cardiac arrhythmias. There is a lack of studies validating the use of handheld devices against the standard 12-lead ECG. The Kardia 6L is a novel handheld ECG monitor which can produce a 6-lead ECG. In this study, we compare the 6L ECG against the 12-lead ECG.

Methods and results

A prospective study consisting of unselected cardiac inpatients and outpatients at Leeds Teaching Hospital NHS Trust. All participants had a 12- and 6-lead ECGs. All ECG parameters were analysed by using a standard method template for consistency between independent observers. Electrocardiograms from the recorders were compared by the following statistical methods: linear regression, Bland-Altman, receiver operator curve, and kappa analysis. There were 1015 patients recruited. The mean differences between recorders were small for PR, QRS, cardiac axis, with receiver operator analysis area under the curve (AUC) of >80%. Mean differences for QT and QTc (between recorders) were also small, with AUCs for QT leads of >75% and AUCs for QTc leads of >60%. Key findings from Bland-Altman analysis demonstrate overall an acceptable agreement with few outliers instances (<6%, Bland-Altman analysis).

Conclusion

Several parameters recorded by the Kardia 6L (QT interval in all six leads, rhythm detection, PR interval, QRS duration, and cardiac axis) perform closely to the gold standard 12-lead ECG. However, that consistency weakens for left ventricular hypertrophy, QRS amplitudes (Lead I and AVL), and ischaemic changes.

When Manual Analysis of 12-Lead ECG Holter Plays a Critical Role in Discovering Unknown Patterns of Increased Arrhythmogenic Risk: A Case Report of a Patient Treated with Tamoxifen and Subsequent Pneumonia in COVID-19

Several medicines, including cancer therapies, are known to alter the electrophysiological function of ventricular myocytes resulting in abnormal prolongation and dispersion of ventricular repolarization (quantified by multi-lead QTc measurement). This effect could be amplified by other concomitant factors (e.g., combination with other drugs affecting the QT, and/or electrolyte abnormalities, such as especially hypokalemia, hypomagnesaemia, and hypocalcemia). Usually, this condition results in higher risk of torsade de point and other life-threatening arrhythmias, related to unrecognized unpaired cardiac ventricular repolarization reserve (VRR). Being VRR a dynamic phenomenon, QT prolongation might often not be identified during the 10-s standard 12-lead ECG recording at rest, leaving the patient at increased risk for life-threatening event. We report the case of a 49-year woman, undergoing tamoxifen therapy for breast cancer, which alteration of ventricular repolarization reserve, persisting also after correction of concomitant recurrent hypokalemia, was evidenced only after manual measurements of the corrected QT (QTc) interval from selected intervals of the 12-lead ECG Holter monitoring. This otherwise missed finding was fundamental to drive the discontinuation of tamoxifen, shifting to another "safer" therapeutic option, and to avoid the use of potentially arrhythmogenic antibiotics when treating a bilateral pneumonia in recent COVID-19.

Toward Smart Monitoring with Phones, Watches, and Wearable Sensors

Smartphones are increasingly powerful computers that fit in our pocket. Thanks to dedicated applications or "Apps," they can connect with external sensors to record, analyze, display, store, and share multiple physiologic signals and data. In addition, because modern smartphones are equipped with accelerometers, gyroscopes, cameras, and pressure sensors, they can also be used to directly gather physiologic information. Smartphones and connected sensors are creating opportunities to empower patients, individualize perioperative care, follow patients during their surgical journey, and simplify clinicians' life.

Kardia Mobile and ISTEL HR applicability in clinical practice: a comparison of Kardia Mobile, ISTEL HR, and standard 12-lead electrocardiogram records in 98 consecutive patients of a tertiary cardiovascular care centre

Aims

Mobile, portable ECG-recorders allow the assessment of heart rhythm in out-of-hospital conditions and may prove useful for monitoring patients with cardiovascular diseases. However, the effectiveness of these portable devices has not been tested in everyday practice.

Methods and results

A group of 98 consecutive cardiology patients [62 males (63%), mean age 69 ± 12.9 years] were included in an academic care centre. For each patient, a standard 12-lead electrocardiogram (SE), as well as a Kardia Mobile 6L (KM) and Istel (IS) HR-2000 ECG were performed. Two groups of experienced physicians analysed obtained recordings. After analysing ECG tracings from SE, KM, and IS, quality was marked as good in 82%, 80%, and 72% of patients, respectively (P < 0.001). There were no significant differences between devices in terms of detecting sinus rhythm [SE (60%, n = 59), KM (58%, n = 56), and IS (61%, n = 60); SE vs. KM P = 0.53; SE vs. IS P = 0.76) and atrial fibrillation [SE (22%, n = 22), KM (22%, n = 21), and IS (18%, n = 18); (SE vs. KM P = 0.65; SE vs. IS = 0.1)]. KM had a sensitivity of 88.1% and a specificity of 89.7% for diagnosing sinus rhythm. IS showed 91.5% and 84.6% sensitivity and specificity, respectively. The sensitivity of KM in detecting atrial fibrillation was higher than IS (86.4% vs. 77.3%), but their specificity was comparable (97.4% vs. 98.7%).

Conclusion

Novel, portable devices are useful in showing sinus rhythm and detecting atrial fibrillation in clinical practice. However, ECG measurements concerning conduction and repolarization should be clarified with a standard 12-lead electrocardiogram.

Enhancing Healthcare Access-Smartphone Apps in Arrhythmia Screening: Viewpoint

Atrial fibrillation is the most commonly reported arrhythmia and, if undiagnosed or untreated, may lead to thromboembolic events. It is therefore desirable to provide screening to patients in order to detect atrial arrhythmias. Specific mobile apps and accessory devices, such as smartphones and smartwatches, may play a significant role in monitoring heart rhythm in populations at high risk of arrhythmia. These apps are becoming increasingly common among patients and professionals as a part of mobile health. The rapid development of mobile health solutions may revolutionize approaches to arrhythmia screening. In this viewpoint paper, we assess the availability of smartphone and smartwatch apps and evaluate their efficacy for monitoring heart rhythm and arrhythmia detection. The findings obtained so far suggest they are on the right track to improving the efficacy of early detection of atrial fibrillation, thus lowering the risk of stroke and reducing the economic burden placed on public health.

Remote Monitoring of the QT Interval and Emerging Indications for Arrhythmia Prevention

QT interval prolongation is a marker of increased risk for life-threatening arrhythmias, and needs to be promptly recognized. Many effective drugs, however, prolong QTc (QT interval corrected for heart rate) in genetically predisposed subjects. The possibility of remote monitoring and QTc measurement for up to 2 weeks, continuously providing physicians with real time data, allows life-saving interventions or changes in drug therapy. This applies especially to patients with the long QT syndrome and to those taking drugs blocking the IKr current and prolonging the QT interval. Patch monitors recording ECG traces continuously are available and contribute to effective arrhythmic prevention.

Use of Smartphones and Wearables for Arrhythmia Monitoring

Smartphones and other wearable electronic devices increasingly are used for ambulatory cardiac rhythm assessment. These consumer technologies have been evaluated in several studies for diagnosis and management of atrial fibrillation. Diverse mobile health applications, including management of other arrhythmias and medical conditions, are expanding alongside advances in technology. Electronic devices owned by millions of consumers have the potential to alter health care delivery as well as research design and implementation. This review provides an up-to-date discussion of the available mobile health technologies, specific applications and limitations for arrhythmia evaluation, their impact on health care systems, and key areas for future investigation.

Validating and implementing cardiac telemetry for continuous QTc monitoring: A novel approach to increase healthcare personnel safety during the COVID-19 pandemic

BACKGROUND

Minimizing direct patient contact among healthcare personnel is crucial for mitigating infectious risk during the coronavirus disease 2019 (COVID-19) pandemic. The use of remote cardiac telemetry as an alternative to 12‑lead electrocardiography (ECG) for continuous QTc monitoring may facilitate this strategy, but its application has not yet been validated or implemented.

METHODS

In the validation component of this two-part prospective cohort study, a total of 65 hospitalized patients with simultaneous ECG and telemetry were identified. QTc obtained via remote telemetry as measured by 3 independent, blinded operators were compared with ECG as assessed by 2 board-certified electrophysiologists as the gold-standard. Pearson correlation coefficients were calculated to measure the strength of linear correlation between the two methods. In a separate cohort comprised of 68 COVID-19 patients treated with combined hydroxychloroquine and azithromycin, telemetry-based QTc values were compared at serial time points after medication administration using Friedman rank-sum test of repeated measures.

RESULTS

Telemetry-based QTc measurements highly correlated with QTc values derived from ECG, with correlation coefficients of 0.74, 0.79, 0.85 (individual operators), and 0.84 (mean of all operators). Among the COVID-19 cohort, treatment led to a median QTc increase of 15 milliseconds between baseline and following the 9th dose (p = 0.002), with 8 (12%) patients exhibiting an increase in QTc ≥ 60 milliseconds and 4 (6%) developing QTc ≥ 500 milliseconds.

CONCLUSIONS

Cardiac telemetry is a validated clinical tool for QTc monitoring that may serve an expanding role during the COVID-19 pandemic strengthened by its remote and continuous monitoring capability and ubiquitous presence throughout hospitals.

Antiarrhythmic drug loading at home using remote monitoring: a virtual feasibility study during COVID-19 social distancing

The epidemiological necessity for distancing during the COVID-19 pandemic has resulted in postponement of non-emergent hospitalizations and increase use of telemedicine. The feasibility of virtual antiarrhythmic drug (AAD) loading specifically with digital QTc electrocardiographic monitoring (EM) in conjunction with telemedicine video visits is not well established.

We tested the hypothesis that existing digital health technologies and virtual communication platforms could provide EM and support medically guided AAD loading for patients with symptomatic tachyarrhythmia in the ambulatory setting, while reducing physical contact between patient and healthcare system.

A prospective pilot, case series approved by the institutional ethics committee, entailing three subjects with symptomatic arrhythmia during the COVID-19 pandemic who were enrolled for virtual AAD loading at home. Clinicians met with participants twice daily via video visits conducted after QTc analysis (Kardia 6L mobile sensor) and telemetry review (Mobile Cardiac Outpatient Telemetry of silent arrhythmias). Participants received direct instruction to either terminate the study or proceed with the next single dose of AAD.

All participants completed contactless loading of 5 AAD doses, without untoward event. Scheduled video visits allowed dialogue and participant counseling where decision making was guided by remote review of EM. Participant adherence with transmissions and scheduled visits was 98.3%; a single electrocardiogram was delayed beyond the two-hours-post-dose schedule. This virtual approach reduced overall expenditures based on retrospective comparison with previous AAD load hospitalizations.

We found that a ‘virtual hospitalization’ for AAD loading with remote electrocardiographic monitoring and twice daily virtual rounding is feasible using existing digital health technologies.

Portable single-lead electrocardiogram device is accurate for QTc evaluation in hospitalized patients

Background

Many commonly used drugs can prolong the QTc interval (QTc), which can lead to potentially life-threatening arrhythmias. In the current era of the COVID-19 pandemic, it is worth mentioning that the disease itself and several drugs used for its treatment have been associated with QTc prolongation.

Objective

To evaluate the agreement and clinical precision of a portable single-lead electrocardiogram (ECG) device to measure the QTc interval compared to the standard 12-lead ECG.

Methods

In sequential tests, QTc of ECG recordings obtained with the KardiaMobile (KM-1L) device (AliveCor, San Francisco, CA) were compared to QTc obtained with conventional 12-lead ECG. Agreement was evaluated using Bland-Altman plots and Lin’s concordance coefficient. Consistency between the 2 devices in determining QTc prolongation (QTc ≥470 ms in males or ≥480 ms in females) was evaluated with kappa statistics.

Results

A total of 128 patients with a presumed or confirmed diagnosis of COVID-19 admitted to a university hospital were included. QTc intervals measured with KM-1L were similar to QTc measured with conventional ECG (442.45 ± 40.5 vs 441.65 ± 40.3 ms, P = .15). Bland–Altman analysis showed no significant difference in QTc values (average difference of -0.797, 95% limits of agreement:-13.179; 11.585). Lin’s concordance coefficient showed an excellent agreement (0.988, P < .001). Concordance between the 2 devices for determining QTc prolongation was excellent (kappa >0.90).

Conclusion

ECG recordings obtained with KM-1L allow an accurate QTc interval assessment. Considering its simplicity of use, this approach has advantages over conventional ECG and can provide an alternative for the evaluation of QTc in hospitalized patients, during the current time of the COVID-19 pandemic and beyond.

QT Interval Monitoring with Handheld Heart Rhythm ECG Device in COVID-19 Patients

Background

QTc prolongation is an adverse effect of COVID-19 therapies. The use of a handheld device in this scenario has not been addressed.

Objectives

To evaluate the feasibility of QTc monitoring with a smart device in COVID-19 patients receiving QTc-interfering therapies.

Methods

Prospective study of consecutive COVID-19 patients treated with hydroxychloroquine ± azithromycin ± lopinavir-ritonavir. ECG monitoring was performed with 12-lead ECG or with KardiaMobile-6L. Both registries were also sequentially obtained in a cohort of healthy patients. We evaluated differences in QTc in COVID-19 patients between three different monitoring strategies: 12-lead ECG at baseline and follow-up (A), 12-lead ECG at baseline and follow-up with the smart device (B), and fully monitored with handheld 6-lead ECG (group C). Time needed to obtain an ECG registry was also documented.

Results

One hundred and eighty-two COVID-19 patients were included (A: 119(65.4%); B: 50(27.5%); C: 13(7.1%). QTc peak during hospitalization did significantly increase in all groups. No differences were observed between the three monitoring strategies in QTc prolongation (p = 0.864). In the control group, all but one ECG registry with the smart device allowed QTc measurement and mean QTc did not differ between both techniques (p = 0.612), displaying a moderate reliability (ICC 0.56 [0.19-0.76]). Time of ECG registry was significantly longer for the 12-lead ECG than for handheld device in both cohorts (p < 0.001).

Conclusion

QTc monitoring with KardiaMobile-6L in COVID-19 patients was feasible. Time of ECG registration was significantly lower with the smart device, which may offer an important advantage for prevention of virus dissemination among healthcare providers.

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study

BACKGROUND

QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 2019 (COVID-19). For the provision of widespread use, surrogates for 12‑lead ECG QTc assessment may be useful. This prospective observational study compared QTc duration assessed by artificial intelligence (AI-QTc) (Cardiologs®, Paris, France) on smartwatch single‑lead electrocardiograms (SW-ECGs) with those measured on 12‑lead ECGs, in patients with early stage COVID-19 treated with a hydroxychloroquine-azithromycin regimen.

METHODS

Consecutive patients with COVID-19 who needed hydroxychloroquine-azithromycin therapy, received a smartwatch (Withings Move ECG®, Withings, France). At baseline, day-6 and day-10, a 12‑lead ECG was recorded, and a SW-ECG was transmitted thereafter. Throughout the drug regimen, a SW-ECG was transmitted every morning at rest. Agreement between manual QTc measurement on a 12‑lead ECG and AI-QTc on the corresponding SW-ECG was assessed by the Bland-Altman method.

RESULTS

85 patients (30 men, mean age 38.3 ± 12.2 years) were included in the study. Fair agreement between manual and AI-QTc values was observed, particularly at day-10, where the delay between the 12‑lead ECG and the SW-ECG was the shortest (-2.6 ± 64.7 min): 407 ± 26 ms on the 12‑lead ECG vs 407 ± 22 ms on SW-ECG, bias -1 ms, limits of agreement -46 ms to +45 ms; the difference between the two measures was <50 ms in 98.2% of patients.

CONCLUSION

In real-world epidemic conditions, AI-QTc duration measured by SW-ECG is in fair agreement with manual measurements on 12‑lead ECGs. Following further validation, AI-assisted SW-ECGs may be suitable for QTc interval monitoring.

REGISTRATION

ClinicalTrial.govNCT04371744.

Accuracy of a single-lead mobile smartphone electrocardiogram for QT interval measurement in patients undergoing maintenance methadone therapy

STUDY OBJECTIVE

Methadone is associated with QT interval prolongation and torsades de pointes. Expert panel recommendations advocate a pre-methadone electrocardiogram (ECG) and another ECG at 30 days of therapy in patients with risk factors. Some guidelines recommend a pre-methadone ECG and routine ECG monitoring in all methadone patients, but this is controversial due to the resources required. Availability of a convenient, less resource-intensive method of ECG monitoring for patients taking methadone is desirable. The objective of this study was to assess the accuracy of a handheld smartphone ECG (iECG) for QT measurement in patients on maintenance methadone therapy in an urban opioid treatment program.

DESIGN

Prospective study.

SETTING

Urban opioid treatment program.

PATIENTS

n = 115 patients in normal sinus rhythm who were on steady-state maintenance methadone therapy INTERVENTION: Patients (n = 115) underwent a simultaneous 12-lead ECG and a single-lead iECG.

MEASUREMENTS AND MAIN RESULTS

The first three QT and RR intervals from lead II of the 12-lead ECG and simulated lead I from the iECG were compared using the Bland-Altman analysis of measurement agreement. Mean [± standard deviation) age was 34 ± 11 years; 71% were female, 75% were white. Compared to the 12-lead ECG, the iECG was associated with a QTc bias of - 0.14 ms (SD = 12 ms, 95% CI = -2.4 to 2.1 ms). The absolute mean difference in QTc between the two methods was 9.5 ± 7.1 ms. For identification of patients with methadone-associated QTc prolongation, the iECG performed moderately well [c-statistic 0.97 (95% CI 0.91-0.99); sensitivity and specificity 75% (95% CI 43-95%) and 99% (95% CI 94-99%), respectively]. The positive and negative likelihood ratios of the iECG for identifying patients with methadone-associated QTc prolongation were 77.25 (95% CI 10.69 to 558.18) and 0.25 (95% CI 0.09 to 0.67), respectively, while the positive and negative predictive values were 90% (95% CI 56-99%) and 97% (95% CI 92-99%), respectively. The accuracy of the iECG for identifying patients with QTc prolongation was 97% (95% CI 91-99%).

CONCLUSION

A handheld smartphone ECG is accurate for QT interval measurement in patients taking maintenance methadone therapy, and its performance is moderately good for identifying patients with methadone-associated QTc prolongation.

Comparison between a 6‑lead smartphone ECG and 12‑lead ECG in athletes

Athletes sometimes experience transient arrhythmias during intense exercise, which may be difficult to capture with traditional Holter monitors. New and highly portable technology, such as smartphone electrocardiogram (ECG) devices, may be useful in documenting and contribute to diagnosis of exercise-induced arrhythmias. There are little data available regarding the new Kardia 6 lead device (6L) and no data regarding its use in athletic populations. In this short communication, we present pilot data from 30 healthy athletes who underwent a 12‑lead ECG and subsequent 6L reading. Our pilot data show relatively high levels of agreement for QTc and PR interval and QRS duration, with the 6L readings slightly but significantly shorter on average.

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.

Bedaquiline for multidrug-resistant tuberculosis and QTc prolongation in California

Background

Bedaquiline (BDQ) is recommended for the treatment of multidrug-resistant tuberculosis (MDR TB), however, it has the potential to prolong QTc interval. We assessed the frequency and severity of QTc prolongation in patients receiving BDQ in California.

Methods

Based on chart review for patients receiving BDQ as part of MDR TB therapy from January 2013–May 2019, we analyzed QTc values at six pre-specified time points during BDQ therapy (baseline, 2, 4, 8, 12, and 24 weeks), as well as peak QTc, time to peak QTc, and the clinical characteristics of patients who had QTc elevation >500 milliseconds (ms) during therapy.

Results

A total of 37 patients were treated with BDQ during the analysis period, with a total of 449 QTc measurements available for analysis. Most patients (89%) received at least one QTc-prolonging drug in addition to BDQ. Median QTc values at all pre-specified time points were <450 ms. Median peak QTc was 455 ms (interquartile range [IQR]: 437–486) and median time to peak was 57 days (IQR: 19–156). Four patients (11%) had a non-transient elevation in QTc to >500 ms, including one patient with profound hypokalemia and one receiving concurrent chemotherapy, but none had cardiac arrhythmia. Less than 10% of patient in our cohort had ECGs performed at all six pre-specified time points.

Discussion

BDQ was generally well-tolerated in a cohort of patients treated for MDR TB in California, with 11% of patients experiencing a non-transient QTc elevation >500 ms, and no episodes of arrhythmia. Frequent ECG monitoring during BDQ therapy presents a challenge for TB clinicians, even in well-resourced countries.

Trends beyond the new normal: from remote monitoring to digital connectivity

COVID pandemic emergency has forced changes from traditional in-person visits to application of telemedicine in order to overcome the barriers and to deliver care. COVID-19 has accelerated adoption of digital health. During this time, the distance is itself a prevention tool and the use of technology to deliver healthcare services and information has driven the discovery of mobile and connected health services. Health services should to be prepared to integrate the old model of remote monitoring of CIEDs and adopt new digital tools such as mobile Apps and connected sensors.

COVID-19 infection and cardiac arrhythmias

As the coronavirus 2019 (COVID-19) pandemic marches unrelentingly, more patients with cardiac arrhythmias are emerging due to the effects of the virus on the respiratory and cardiovascular (CV) systems and the systemic inflammation that it incurs, and also as a result of the proarrhythmic effects of COVID-19 pharmacotherapies and other drug interactions and the associated autonomic imbalance that enhance arrhythmogenicity. The most worrisome of all arrhythmogenic mechanisms is the QT prolonging effect of various anti-COVID pharmacotherapies that can lead to polymorphic ventricular tachycardia in the form of torsade des pointes and sudden cardiac death. It is therefore imperative to monitor the QT interval during treatment; however, conventional approaches to such monitoring increase the transmission risk for the staff and strain the health system. Hence, there is dire need for contactless monitoring and telemetry for inpatients, especially those admitted to the intensive care unit, as well as for outpatients needing continued management. In this context, recent technological advances have ushered in a new era in implementing digital health monitoring tools that circumvent these obstacles. All these issues are herein discussed and a large body of recent relevant data are reviewed.

Comparison between smartphone electrocardiography and standard three-lead base apex electrocardiography in healthy horses

BACKGROUND

Cardiac arrhythmias are commonly auscultated during routine physical examinations in horses and determining the underlying electrical abnormality using an ECG is important. The most commonly used device is a three-lead base apex system (Televet), however few practitioners carry this for routine visits. With recognition of the utility of smartphone-based ECGs in humans, dogs and ruminants, the AliveCor single-lead bipolar smartphone-based ECG has gained popularity. The objective of this study was to determine if AliveCor and Televet ECG measurements were comparable in healthy horses using multiple observers.

METHODS

ECGs were performed on 15 healthy horses simultaneously using the AliveCor and Televet.

RESULTS

There was very good to perfect interdevice and interobserver agreement for heart rate and RR interval measurement, and moderate-to-good interdevice and interobserver agreement for detection of non-pathological arrhythmias. Interdevice agreement for measurement of P-wave and QRS duration, QT, PR and T-peak to T-end interval was poor to fair. Interestingly, interobserver agreement for P-wave and QRS duration, QT, PR, and T-peak to T-end interval measurements was fair to good.

CONCLUSION

Overall, the AliveCor is comparable to the Televet for heart rate and RR measurement, and for the detection of non-pathogenic arrhythmias with acceptable agreement between observers.

Drug-Induced Arrhythmias: A Scientific Statement From the American Heart Association

Many widely used medications may cause or exacerbate a variety of arrhythmias. Numerous antiarrhythmic agents, antimicrobial drugs, psychotropic medications, and methadone, as well as a growing list of drugs from other therapeutic classes (neurological drugs, anticancer agents, and many others), can prolong the QT interval and provoke torsades de pointes. Perhaps less familiar to clinicians is the fact that drugs can also trigger other arrhythmias, including bradyarrhythmias, atrial fibrillation/atrial flutter, atrial tachycardia, atrioventricular nodal reentrant tachycardia, monomorphic ventricular tachycardia, and Brugada syndrome. Some drug-induced arrhythmias (bradyarrhythmias, atrial tachycardia, atrioventricular node reentrant tachycardia) are significant predominantly because of their symptoms; others (monomorphic ventricular tachycardia, Brugada syndrome, torsades de pointes) may result in serious consequences, including sudden cardiac death. Mechanisms of arrhythmias are well known for some medications but, in other instances, remain poorly understood. For some drug-induced arrhythmias, particularly torsades de pointes, risk factors are well defined. Modification of risk factors, when possible, is important for prevention and risk reduction. In patients with nonmodifiable risk factors who require a potentially arrhythmia-inducing drug, enhanced electrocardiographic and other monitoring strategies may be beneficial for early detection and treatment. Management of drug-induced arrhythmias includes discontinuation of the offending medication and following treatment guidelines for the specific arrhythmia. In overdose situations, targeted detoxification strategies may be needed. Awareness of drugs that may cause arrhythmias and knowledge of distinct arrhythmias that may be drug-induced are essential for clinicians. Consideration of the possibility that a patient's arrythmia could be drug-induced is important.