Use of Smartphone-operated ECG for home ECG surveillance in COVID-19 patients

Remote Patient Monitoring at Home in Patients With COVID-19: Narrative Review

BACKGROUND

During the pandemic, health care providers implemented remote patient monitoring (RPM) for patients experiencing COVID-19. RPM is an interaction between health care professionals and patients who are in different locations, in which certain patient functioning parameters are assessed and followed up for a certain duration of time. The implementation of RPM in these patients aimed to reduce the strain on hospitals and primary care.

OBJECTIVE

With this literature review, we aim to describe the characteristics of RPM interventions, report on patients with COVID-19 receiving RPM, and provide an overview of outcome variables such as length of stay (LOS), hospital readmission, and mortality.

METHODS

A combination of different searches in several database types (traditional databases, trial registers, daily [Google] searches, and daily PubMed alerts) was run daily from March 2020 to December 2021. A search update for randomized controlled trials (RCTs) was performed in April 2022.

RESULTS

The initial search yielded more than 4448 articles (not including daily searches). After deduplication and assessment for eligibility, 241 articles were retained describing 164 telemonitoring studies from 160 centers. None of the 164 studies covering 248,431 patients reported on the presence of a randomized control group. Studies described a "prehosp" group (96 studies) with patients who had a suspected or confirmed COVID-19 diagnosis and who were not hospitalized but closely monitored at home or a "posthosp" group (32 studies) with patients who were monitored at home after hospitalization for COVID-19. Moreover, 34 studies described both groups, and in 2 studies, the description was unclear. In the prehosp and posthosp groups, there were large variations in the number of emergency department (ED) visits (0%-36% and 0%-16%, respectively) and no convincing evidence that RPM leads to less or more ED visits or hospital readmissions (0%-30% and 0%-22%, respectively). Mortality was generally low, and there was weak to no evidence that RPM is associated with lower mortality. Moreover, there was no evidence that RPM shortens previous LOS. A literature update identified 3 small-scale RCTs, which could not demonstrate statistically significant differences in these outcomes. Most papers claimed savings; however, the scientific base for these claims was doubtful. The overall patient experiences with RPM were positive, as patients felt more reassured, although many patients declined RPM for several reasons (eg, technological embarrassment, digital literacy).

CONCLUSIONS

Based on these results, there is no convincing evidence that RPM in COVID-19 patients avoids ED visits or hospital readmissions and shortens LOS or reduces mortality. On the other hand, there is no evidence that RPM has adverse outcomes. Further research should focus on developing, implementing, and evaluating an RPM framework.

Validation of a multiple‑lead smartphone-based electrocardiograph with automated lead placement for layman use in patients with hypertrophic cardiomyopathy

BACKGROUND

A smartphone 12-Lead ECG that enables layman ECG screening is still lacking. We aimed to validate D-Heart ECG device, a smartphone 8/12 Lead electrocardiograph with an image processing algorithm to guide secure electrode placement by non-professional users.

METHODS

One-hundred-fourty-five patients with HCM were enrolled. Two uncovered chest images were acquired using the smartphone camera. An image with virtual electrodes placement by imaging processing algorithm software was compared to the 'gold standard' electrode placement by a doctor. D-Heart 8 and 12-Lead ECG were obtained, immediately followed by 12‑lead ECGs and were assessed by 2 independent observers. Burden of ECG abnormalities was defined by a score based on the sum of 9 criteria, identifying four classes of increasing severity.

RESULTS

A total of 87(60%) patients presented a normal/mildly abnormal ECG, whereas 58(40%) had moderate or severe ECG alteration. Eight(6%) patients had ≥1 misplaced electrode. D-Heart 8-Lead and 12‑lead ECGs concordance according to Cohen's weighted kappa test was 0,948 (p < 0,001, agreement of 97.93%). Concordance was high for the Romhilt-Estes score (k_w = 0,912; p < 0.01). Concordance between D-Heart 12-Lead ECG and standard 12-Lead ECG was perfect (k_w = 1). PR and QRS intervals measurements comparison with Bland-Altman method showed good accuracy (95% limit of agreement ±18 ms for PR and ± 9 ms for QRS).

CONCLUSIONS

D-Heart 8/12-Lead ECGs proved accurate, allowing an assessment of ECG abnormalities comparable to the standard 12‑lead ECG in patients with HCM. The image processing algorithm provided accurate electrode placement, standardizing exam quality, potentially opening perspectives for layman ECG screening campaigns.

Layman electrocardiographic screening using smartphone-based multiple‑lead ECG device in school children

BACKGROUND

Pre-partecipation ECG screening of large populations has a significant socioeconomic impact. Technological progress now allows for high-tech-low-cost ECG screening using validated smartphone-based devices capable of guiding to the correct performance of a 12‑lead ECG by layman with no medical background.

METHODS

We enrolled 728 (364, 52% males) individuals, aged 12-13 years who underwent ECG screening with a smartphone 12‑lead ECG during school hours by layman volunteers. Correct electrodes placement was provided by a validated image-processing algorithm by the smartphone camera in the App. ECG interpretation was via a telecardiology platform and alterations classified following current standards.

RESULTS

A total of 741 ECGs were recorded, of which 13(2%) were technically not interpretable. Mean PR, QRS and QTc were: 145 ± 22, 85 ± 19 and 387 ± 57 msec. No QTc prolongation was observed. Mean QRS axis was 15°; 26 (4%) patients presented an iRBB. T-wave inversion from V1-V3 was present in 145 (21%) subjects. Twenty-one(3%) patients were referred to second level examination: deep Q-waves in inferior leads in 12(1.6%), ventricular ectopics in 5(0.7%), anterior T-waves inversions V1-V4 in 3(0.4%); extreme right axis deviation in 1(0.3%). Second line investigations did not provide any definitive diagnosis. Total project costs (material equipment and human cost) was 14.460€, 19.51€ per individual. The potential net saving with respect to current pre-participation screening cost was 19%.

CONCLUSIONS

Layman 12‑lead Smartphone-ECG population screening proved feasible and effective, with a rate of non-interpretable ECG of <5%. Potential cost-saving in ECG screening and recording was 19%, providing an appealing opportunity when large campaigns should be addressed also in developing countries.

Emerging role of artificial intelligence in cardiac electrophysiology

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

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.

Role of Digital Health During Coronavirus Disease 2019 Pandemic and Future Perspectives

Coronavirus disease 2019 revolutionized the digital health care. This pandemic was the catalyst for not only a sudden but also widespread paradigm shift in patient care, with nearly 80% of the US population indicating that they have used one form of digital health. Cardiac electrophysiology took the initiative to enroll patients in device clinics for remote monitoring and triage patients accordingly. Although challenges remain in making digital health available to masses, the future of digital health will be tested in the postpandemic time, and we believe these changes will continue to be expansive and widely applicable to physicians and patients.

Feasibility of a Combined Mobile-Health Electrocardiographic and Rapid Diagnostic Test Screening for Chagas-Related Cardiac Alterations

BACKGROUND

Chronic Chagas cardiomyopathy (CChC) is the most common cause of death related to Chagas disease (CD). The aim of this study was to assess the feasibility of a combined rapid diagnostic test (RDT) and electrocardiographic (ECG) screening in a remote rural village of the Bolivian Chaco, with a high prevalence of CChC.

METHODS

Consecutive healthy volunteers > 15 years were enrolled in the community of Palmarito (municipality of Gutierrez, Santa Cruz Department, Bolivia) in February 2019. All patients performed an RDT with Chagas Stat-Pak® (CSP, Chembio Diagnostic System, Medford, NY, USA) and an ECG by D-Heart® technology, a low-cost, user-friendly smartphone-based 8-lead Bluetooth ECG. RDTs were read locally while ECGs were sent to a cardiology clinic which transmitted reports within 24 h from recording.

RESULTS

Among 140 people (54 men, median age 38(interquartile range 23-54) years), 98 (70%) were positive for Trypanosoma cruzi infection, with a linear, age-dependent, increasing trend (p < 0.001). Twenty-five (18%) individuals showed ECG abnormalities compatible with CD. Prevalence of ECG abnormalities was higher in infected individuals and was associated with higher systolic blood pressure and smoking. Following screening, 22 (16%) individuals underwent clinical evaluation and chest X-ray and two were referred for further evaluation. At multivariate analysis, positive CSP results (OR = 4.75, 95%CI 1.08-20.96, p = 0.039) and smoking (OR = 4.20, 95%CI 1.18-14.92, p = 0.027) were independent predictors of ECG abnormalities. Overall cost for screening implementation was <10 $.

CONCLUSIONS

Combined mobile-Health and RDTs was a reliable and effective low-cost strategy to identify patients at high risk of disease needing cardiologic assessment suggesting potential future applications.