Cardiovascular digital health journal最新文献

Telemedicine, telehealth, e-Health, and other related terms refer to the exchange of medical information or medical care from one site to another through electronic communication between a patient and a health care provider. As telemedicine infrastructure has changed since the coronavirus disease 2019 (COVID-19) pandemic, this review provides an overview of telemedicine use and effectiveness in cardiology, with emphasis on coronary artery disease in the postpandemic context. Prepandemic studies tend to report statistically insignificant or modest improvements in cardiovascular disease outcome from telemedicine use to usual care. In contrast, postpandemic studies tend to report positive outcomes or comparable acceptance of telemedicine use to usual care. Today, telemedicine can effectively replace in person follow-up visits to produce comparable (but not necessarily superior) outcomes in cardiovascular disease management. A benefit of telemedicine is the potential reduction in follow-up time or time to intervention, which may lead to earlier detection and prevention of adverse events. Nonetheless, barriers remain to effective telemedicine implementation in the postpandemic context. Ensuring accessible and user-friendly telemedicine devices, maintaining adherence to remote rehabilitation procedures, and normalizing use of telemedicine in routine follow-up visits are examples. Current knowledge gaps include the true economic cost of telemedicine infrastructure, feasibility of use in specific cardiology contexts, and sex/gender differences in telemedicine use. Future telemedicine developments will need to address these concerns before acceptance of telemedicine as the new standard of care.

Background

Depressive symptoms are common and share many biopsychosocial mechanisms with hypertension. Association studies between depressive symptoms and blood pressure (BP) have been inconsistent. Home BP monitoring may provide insight.

Objective

To investigate the association between depressive symptoms and digital home BP.

Methods

Electronic Framingham Heart Study (eFHS) participants were invited to obtain a smartphone app and digital BP cuff at research exam 3 (2016–2019). Participants with ≥3 weeks of home BP measurements within 1 year were included. Depressive symptoms were measured using the Center for Epidemiological Studies Depression Scale (CES-D). Multivariable linear mixed models were used to test the associations of continuous CES-D score and dichotomous depressive symptoms (CES-D ≥16) (independent) with home BP (dependent), adjusting for age, sex, cohort, number of weeks since baseline, lifestyle factors, diabetes, and cardiovascular disease.

Results

Among 883 participants (mean age 54 years, 59% women, 91% White), the median CES-D score was 4. Depressive symptom prevalence was 7.6%. Mean systolic and diastolic BP at exam 3 were 119 and 76 mm Hg; hypertension prevalence was 48%. A 1 SD higher CES-D score was associated with 0.9 (95% CI: 0.18–1.56, P = .01) and 0.6 (95% CI: 0.06–1.07, P = .03) mm Hg higher home systolic BP and diastolic BP, respectively. Dichotomous depressive symptoms were not significantly associated with home BP (P > .2).

Conclusion

Depressive symptoms were not associated with clinically substantive levels of home BP. The association between depression and cardiovascular disease risk factors warrants more data, which may be supported by mobile health measures.

Background

Patient monitoring devices are critical for alerting of potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates.

Objective

The purpose of this study was to evaluate the sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies.

Methods

Six commonly used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False-positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false-positive rates were determined. Differences were assessed using Fisher exact tests (sensitivity) and Z-tests (false-positive rates).

Results

B125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%; P <.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, which was significantly lower (P <.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by nonsustained VT.

Conclusion

We found significant performance differences among multiparameter bedside ECG monitoring systems using previously collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.

Background

During the COVID-19 pandemic, telemedicine was advocated and rapidly scaled up worldwide. However, little is known about for whom this type of care is acceptable.

Objective

To examine which patient characteristics (demographic, medical, psychosocial) are associated with telehealth care satisfaction, attitude toward telehealth, and preference regarding telehealth over time in a cardiac patient population.

Methods

In total, 317 patients were recruited at the Elisabeth-TweeSteden Hospital in The Netherlands. All patients who had received telehealth care (telephone and video) in the previous 2 months were approached for participation. Baseline, 3-month, and 6-month questionnaires were administered online. A 3-step latent class analysis was conducted to identify trajectories of telehealth use over time and the possible association of the found trajectories with external variables.

Results

Five trajectories (classes) were identified for satisfaction with telehealth and 4 for attitude toward telehealth. Patients with higher distress, lower physical and mental health, higher scores on pessimism, and negative affectivity were more likely to be less satisfied. Patients with no partner, more comorbidities, higher distress, lower physical and mental health, and higher scores on pessimism were more likely to hold a negative attitude toward telehealth. For the future application of telehealth, marital status, comorbidities, digital health literacy, and pessimism were significantly related.

Conclusion

Results show that patients’ profiles should be considered when offering telehealth care and that the “one size fits all” approach does not apply. Results can inform clinical practice on how to better implement remote health care in the future while considering a personalized approach.

Background

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death globally, and early detection of high-risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual’s elevated 10-year ASCVD risk score based on retinal images and limited demographic data.

Methods

The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual’s 10-year ASCVD risk score using the pooled cohort equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% non-Hispanic White, 99.9% diabetic), composed of 18,900 images from 8969 diabetic individuals. Elevated ASCVD risk was defined as a PCE score of ≥7.5%.

Results

In the UK Biobank internal validation dataset, the DL model achieved an area under the receiver operating characteristic curve of 0.89, sensitivity 84%, and specificity 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modifier, it achieved sensitivity 94%, specificity 72%, mean error -0.2%, and mean absolute error 3.1%.

Conclusion

This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, as well as the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.

Background

The availability of portable and wearable electrocardiographic (ECG) devices has increased secondary to technological development. Single-lead ECG recordings have been shown to reliably detect and characterize cardiac rhythms such as atrial fibrillation. Acquisition of precordial electrodes for full 12-lead ECG reconstruction from bipolar recordings is complicated by the absence of a body ground/Wilson central terminal electrode. The extent of difference between standard precordial leads and those from a wearable bipolar ECG recorder has not been characterized.

Objective

The purpose of this study was to characterize the precordial ECG lead set from sequential bipolar recordings from an ECG ring wearable device.

Methods

In 70 patients who wore an ECG device on a right-hand finger, sequential precordial leads (CR1–CR6) were obtained along with chest electrodes (V1–V6). During acquisition of the modified precordial lead CR6, a full standardized 12-lead ECG capture was obtained. Signal quality was assessed using automated analysis software, and correlation values between the ring-derived ECG precordial leads and standard ECG leads were compared with regard to QRS duration, QT width, and RR interval.

Results

High concordance in the morphologies of precordial ECG leads obtained in a standard fashion and those recorded through an ECG ring was observed. Morphologic alignment improved with increasing laterality of the precordial lead with chest to right arm ring recording (CR5, CR6) compared with anterior chest leads to right arm (CR1, CR2). Segmental measurements of QRS duration and QT segment were well aligned and of high correlation.

Conclusion

Wearable ring-based ECG technology is capable of high-fidelity recordings of precordial leads for nonsimultaneous reconstruction of complete ECG sets. These recordings correlate highly with surface-obtained QRS and QT duration measurements and have significant implications for clinical applications. Uninterpretable tracings were primarily due to electrode noise from poor electrode contact.

Background

Cardiac arrhythmias are a common health problem. Both common and rare genetic risk factors exist for cardiac arrhythmias. Cardiac amyloidosis is a rare disease that may manifest various arrhythmias. Few large-scale whole exome sequencing studies elucidating the contribution of rare variations to arrhythmias have been published.

Objective

To access gene collapsing analysis of rare variations for different types of cardiac arrhythmias in UK Biobank. Identified genes were analyzed in silico for probability to form amyloid fibrils.

Methods

We used 2 published UK Biobank portals (https://azphewas.com/ and https://app.genebass.org/) to access gene collapsing analysis of rare variations for different types of cardiac arrhythmias. Diagnosis of arrhythmia was based on the International Classification of Diseases, 10th Revision (ICD-10) codes: conduction disorders (I44, I45), paroxysmal tachycardia (I47), atrial fibrillation (I48), and other arrhythmias (I49).

Results

Rare variations in 5 genes were linked to conduction disorders (SCN5A, LMNA, SMAD6, HSPB9, TMEM95). The TTN gene was associated with both paroxysmal tachycardia and other arrhythmias. Atrial fibrillation was associated with rare variations in 8 genes (TTN, RPL3L, KLF1, TET2, NME3, KDM5B, PKP2, PMVK). Two of the genes linked to heart conduction disorders were potential amyloid-forming proteins (HSPB9, TMEM95), while none of the 8 genes linked to other types of arrhythmias were potential amyloid-forming proteins.

Conclusion

Rare variations in 13 genes were associated with arrhythmias in the UK Biobank. Two of the heart conduction disorder–linked genes are potential amyloid-forming candidates. Amyloid formation may be an underestimated cause of heart conduction disorders.

Background

Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by manufacturers’ algorithms is currently too high to be clinically useful.

Method

This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart device artificial intelligence (AI)-based algorithm for detecting AF from 4 commercially available smart devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and 4 smart device SL-ECGs. The novel AI algorithm (PulseAI, Belfast, United Kingdom) was compared with each manufacturer’s algorithm.

Results

We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI algorithm vs manufacturer algorithm were 88% vs 81% (P = .34) and 97% vs 77% (P < .001) for the AliveCor KardiaMobile, 86% vs 81% (P = .45) and 95% vs 83% (P < .001) for the Apple Watch 6, 91% vs 67% (P < .01) and 94% vs 82% (P < .001) for the Fitbit Sense, and 86% vs 82% (P = .63) and 94% vs 80% (P < .001) for the Samsung Galaxy Watch3, respectively. In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart devices using the AI-based algorithm compared to manufacturer’s algorithms (14%–17%), P < .001.

Conclusion

A novel AI algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers’ algorithms.