Parental Acquisition of Echocardiographic Images in Pediatric Heart Transplant Patients Using a Handheld Device: A Pilot Telehealth Study

Socioeconomic Determinants of Health: Remoteness From Care

Remoteness from care remains a major challenge to equitable provision of health services worldwide. Beyond the difficulties associated with geographically and climatically rugged terrain, there are also socioeconomic, cultural, and technological challenges associated with remote residence. The objective of this review is to examine the factors whereby remoteness can be associated with sociodemographic disadvantage in health care and describe some of the methodologies for measurement and analysis of remoteness, with examples from the literature, particularly focusing on Canada. As surrogates for remoteness, simple measurements of direct distance or travel time may correlate well with more complex measures and can be performed relative to specific health care services of interest (for example, tertiary obstetric service). These metrics may also be measured, as general proxies for service availability, to various sizes of population centres. More complex measures of remoteness may also incorporate modes of available transport and availability of specific services into an index such as the Canadian Index of Remoteness. As an important independent predictor of health, remoteness requires careful predictive modelling because of potential complex nonlinear relationships, edge effects created by health system zone boundaries, and covariance with other sociodemographic factors and Indigenous population proportions. To combat disadvantage caused by remoteness, innovation in health service delivery, policy, and technology is required. Health-resource allocation must be adequate, and innovative technological advances-such as remote monitoring, expert clinical support, and artificial intelligence algorithms-must be supported by development of appropriate technological infrastructure, targeting remote regions. With these, the barriers to equitable health imposed by remoteness can be overcome.

Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts

Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.

The Echocardiographic Evaluation of the Right Heart: Current and Future Advances

PURPOSE OF REVIEW

To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound.

RECENT FINDINGS

Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.

Impact of Technologic Innovation and COVID-19 Pandemic on Pediatric Cardiology Telehealth

Purpose of Review

Established telehealth practices in pediatrics and pediatric cardiology are evolving rapidly. This review examines several concepts in contemporary telemedicine in our field: recent changes in direct-to-consumer (DTC) pediatric telehealth (TH) and practice based on lessons learned from the pandemic, scientific data from newer technological innovations in pediatric cardiology, and how TH is shaping global pediatric cardiology practice.

Recent Findings

In 2020, the global pandemic of COVID-19 led to significant changes in healthcare delivery. The lockdown and social distancing guidelines accelerated smart adaptations and pivots to ensure continued pediatric care albeit in a virtual manner. Remote cardiac monitoring technology is continuing to advance at a rapid pace secondary to advances in the areas of Internet access, portable hand-held devices, and artificial intelligence.

Summary

TH should be approached programmatically by pediatric cardiac healthcare providers with careful selection of patients, technology platforms, infrastructure setup, documentation, and compliance. Payment parity with in-person visits should be advocated and legislated. Newer remote cardiac monitoring technology should be expanded for objective assessment and optimal outcomes. TH continues to be working beyond geographical boundaries in pediatric cardiology and should continue to expand and develop.

Tele-Clinic Visits in Pediatric Patients with Marfan Syndrome Using Parentally Acquired Echocardiography

OBJECTIVE

To test feasibility of tele-clinic visits using parentally acquired vital signs and focused echocardiographic images in patients with Marfan syndrome.

STUDY DESIGN

We included patients with Marfan syndrome aged 5-19 years followed in our clinic. We excluded patients with Marfan syndrome and history of previous aortic root (AoR) surgery, cardiomyopathy, arrhythmia, or AoR ≥4.5 cm. We trained parents in-person to acquire focused echocardiographic images on their children using a hand-held device as well as how to use a stadiometer, scale, blood pressure (BP) machine, and a digital stethoscope. Before tele-clinic visits, parents obtained the echocardiographic images and vital signs. We compared tele-clinic and on-site clinic visit data. Parental and clinic echocardiograms were independently analyzed.

RESULTS

Fifteen patient/parent pairs completed tele-clinic visits, conducted at a median of 7.0 (IQR 3.0-9.9) months from the in-person training session. Parents took a median of 70 (IQR 60-150) minutes to obtain the height, weight, heart rate, BP, cardiac sounds, and echocardiographic images before tele-clinic visits. Systolic BP was greater on-site than at home (median +13 mm Hg, P = .014). Height, weight, diastolic BP, heart rate, and AoR measurements were similar.

CONCLUSIONS

This study provides information for implementing tele-clinic visits using parentally acquired vital signs and echocardiographic images in patients with Marfan syndrome. The results show that tele-clinic visits are feasible and that parents were able to obtain focused echocardiographic images on their children.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03581682.

Telehealth in Pediatric Heart Transplant Patients: Exercise, Nutrition, and Parental Imaging

Telehealth is a promising new tool in medicine that has changed the landscape of medical care. The benefits of telehealth technology are immense, including improved access to care and potential savings in monetary and opportunity costs. Current challenges of incorporating telehealth services into regular clinical care include licensure and regulatory barriers, difficulty obtaining insurance reimbursements, and high costs of setting up successful telehealth infrastructures. These challenges threaten telehealth's future scalability and expansion to reach all patients in need.

Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN

PURPOSE

 The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems.

METHODS

 We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views.

RESULTS

 A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively.

CONCLUSION

 This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.