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A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.
Am J Cardiol
November 2024
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut. Electronic address:
Article Synopsis
- AI-ECG can effectively detect hypertrophic cardiomyopathy (HCM) and track treatment responses using 12-lead ECGs.
- The study analyzed data from patients undergoing surgical reduction and those receiving mavacamten at multiple healthcare centers, finding no improvement in HCM scores after surgery, but a significant decrease in scores among patients taking mavacamten.
- This highlights AI-ECG's potential for ongoing monitoring of heart condition improvements following medication rather than surgical interventions.
An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.
medRxiv
December 2024
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Background: Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility.
Objective: To leverage images of 12-lead ECGs for automated detection and prediction of multiple SHDs using an ensemble deep learning approach.
Methods: We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH).
Multidimensional prediction of continuous positive airway pressure adherence.
Sleep Med
October 2024
Department of Respiratory Medicine, Nara Medical University, 840 Shijocho, Kashihara, Nara, 634-8521, Japan.
Objective: Continuous positive airway pressure (CPAP) is the standard treatment for obstructive sleep apnea (OSA). Unsatisfactory adherence to CPAP is an important clinical issue to resolve. Cluster analysis is a powerful tool to distinguish subgroups in a multidimensional fashion.
View Article and Find Full Text PDFArtificial Intelligence Enabled Prediction of Heart Failure Risk from Single-lead Electrocardiograms.
medRxiv
December 2024
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Article Synopsis
- The study highlights the challenge of effectively stratifying heart failure (HF) risk despite existing therapies, proposing that portable devices that record single-lead electrocardiograms (ECGs) could improve community-based assessments.
- An artificial intelligence (AI) algorithm was evaluated for its ability to predict HF risk from these single-lead ECGs, using data from multiple cohorts including Yale New Haven Health System, UK Biobank, and ELSA-Brasil.
- Results indicated that individuals screened positively by the AI-ECG model had a significantly higher risk for developing HF, with increases in risk correlating with higher model probabilities, suggesting it could be a valuable tool for early identification of at-risk patients
Inappropriate Laboratory Testing: Significant Waste Quantified by a Large-Scale Year-Long Study of Medicare and Commercial Payer Reimbursement.
Arch Pathol Lab Med
June 2024
the Department of Pathology and Immunology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas (Singh).
Context.—: Laboratory testing, beyond what is essential for managing health, is considered low-value care, posing patient risks and wasting resources. Measuring excess testing on a national level is crucial to identify waste and optimize healthcare resource allocation for maximum impact.
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