Wearable Biosensors in Congenital Heart Disease

Tandon, Animesh; Nguyen, Hoang H.; Avula, Sravani; Seshadri, Dhruv R.; Patel, Akash; Fares, Maan; Baloğlu, Orkun; Jafari, Roozbeh; Inan, Omer T.; Drummond, Colin K.

doi:10.1016/j.jacadv.2023.100267

Cited by 15 publications

(16 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its use in adults has been wideranging, including prediction of ventricular dysfunction, [3][4][5][6][7] ventricular hypertrophy, 8-10 ventricular dilation, 9,11 atrial fibrillation and other arrhythmias, 17,26,40,41 and age, 42,43 sex, 42 and time to death. 8,43 Our findings provide proof-ofconcept evidence that similar ECG applications can be explored in children and suggest that deep learning may also be applicable to other data streams (eg, wearable biosensor data) that could aid in predicting outcomes for children 44 similar to what has been performed in adults. 45 As a direct case example of clinical usefulness, ECGechocardiogram pairs from the ED were considered, a setting in which AI-pECG could be of substantial clinical and economic value.…”

Section: Clinical Significance and Implicationsmentioning

confidence: 64%

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Mayourian,

La Cava,

Vaid

et al. 2024

Circulation

View full text Add to dashboard Cite

BACKGROUND: Artificial intelligence–enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS: A convolutional neural network was trained on paired ECG–echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert–classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children’s Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS: The training cohort comprised 92 377 ECG–echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V5 and V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). CONCLUSIONS: This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.

show abstract

Section: Clinical Significance and Implicationsmentioning

confidence: 64%

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Mayourian,

La Cava,

Vaid

et al. 2024

Circulation

View full text Add to dashboard Cite

show abstract

“…Hardware design choices in terms of sampling frequency may need to be altered to capture extreme values. 3 Further research to evaluate the accuracy of wearable device data in patients with congenital heart disease would help clinicians understand the quality of these data. 4…”

Section: Hardware Challenges In Wearables Use In Congenital Heart Dis...mentioning

confidence: 99%

“…Outside of heart rate data, photoplethysmography or other novel physiologic markers may lead to earlier identification and opportunity for timely intervention in patients with chronic congenital heart disease or cardiomyopathy and evolving heart failure. 3 Additional outcome data on the use of wearable devices in the congenital heart disease population are essential for optimal care. An important limitation to clinical use of wearables in everyday practice is lack of integration into the clinical workflow.…”

Section: Future Wearables Use In Congenital Heart Diseasementioning

confidence: 99%

Advancing Wearable Biosensors for Congenital Heart Disease: Patient and Clinician Perspectives: A Science Advisory From the American Heart Association

Tandon,

Avari Silva,

Bhatt

et al. 2024

Circulation

Self Cite

View full text Add to dashboard Cite

Wearable biosensors (wearables) enable continual, noninvasive physiologic and behavioral monitoring at home for those with pediatric or congenital heart disease. Wearables allow patients to access their personal data and monitor their health. Despite substantial technologic advances in recent years, issues with hardware design, data analysis, and integration into the clinical workflow prevent wearables from reaching their potential in high-risk congenital heart disease populations. This science advisory reviews the use of wearables in patients with congenital heart disease, how to improve these technologies for clinicians and patients, and ethical and regulatory considerations. Challenges related to the use of wearables are common to every clinical setting, but specific topics for consideration in congenital heart disease are highlighted.

show abstract

“…One available approach to enhance real-time interaction and guidance during echocardiography is through the implementation of 'tele-echocardiography', where a sonographer captures images at the patient's bedside while a cardiologist remotely reviews the images in realtime, providing instructions for image acquisition and optimization [73]. Additionally, utilizing cloud-based picture archiving and communication system (PACS) platforms enables seamless transmission and viewing of stored echocardiograms on any computer, from any location [74]. Neonatal telemedicine has proven to be accurate and cost-effective, and has effectively prevented unnecessary transports in up to 75% of cases [74,75].…”

Section: Screeningmentioning

confidence: 99%

“…Additionally, utilizing cloud-based picture archiving and communication system (PACS) platforms enables seamless transmission and viewing of stored echocardiograms on any computer, from any location [74]. Neonatal telemedicine has proven to be accurate and cost-effective, and has effectively prevented unnecessary transports in up to 75% of cases [74,75]. In remote and less densely populated regions, a substantial number of neonates encounter challenges in promptly accessing local pediatric sonographers and pediatric cardiologists skilled in echocardiographic interpretation.…”

Section: Screeningmentioning

confidence: 99%

E-Health: A Game Changer in Fetal and Neonatal Cardiology?

Padovani,

Singh,

Pass

et al. 2023

JCM

View full text Add to dashboard Cite

Technological advancements have greatly impacted the healthcare industry, including the integration of e-health in pediatric cardiology. The use of telemedicine, mobile health applications, and electronic health records have demonstrated a significant potential to improve patient outcomes, reduce healthcare costs, and enhance the quality of care. Telemedicine provides a useful tool for remote clinics, follow-up visits, and monitoring for infants with congenital heart disease, while mobile health applications enhance patient and parents’ education, medication compliance, and in some instances, remote monitoring of vital signs. Despite the benefits of e-health, there are potential limitations and challenges, such as issues related to availability, cost-effectiveness, data privacy and security, and the potential ethical, legal, and social implications of e-health interventions. In this review, we aim to highlight the current application and perspectives of e-health in the field of fetal and neonatal cardiology, including expert parents’ opinions.

show abstract

Wearable Biosensors in Congenital Heart Disease

Cited by 15 publications

References 56 publications

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Advancing Wearable Biosensors for Congenital Heart Disease: Patient and Clinician Perspectives: A Science Advisory From the American Heart Association

E-Health: A Game Changer in Fetal and Neonatal Cardiology?

Contact Info

Product

Resources

About