Risk Stratification of Cancer Therapy-Related Cardiac Dysfunction Using AI-Enhanced Electrocardiography
NCT07515859 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 31486
Last updated 2026-04-13
Summary
This study investigates the use of AI-enhanced electrocardiogram (ECG) for risk stratification of cancer therapy-related cardiac dysfunction (CTRCD) before the initiation of cancer therapy. The study includes patients treated with anthracyclines, HER2 inhibitors, or immune checkpoint inhibitors (ICIs) at Severance Hospital between May 2006 and November 2022, who underwent an ECG within 90 days prior to chemotherapy. The primary goal is to evaluate whether AI-ECG can accurately predict the risk of CTRCD and compare its performance to existing risk stratification models. In addition, we aim to assess whether the variation in AI-ECG scores between pre- and post-chemotherapy assessments could serve as a predictor of CTRCD. Eligible participants are adults without prior heart failure, cardiomyopathy, or myocarditis, and with baseline left ventricular ejection fraction (LVEF) ≥40%. For trajectory analysis, only patients with an additional ECG within 90 days after chemotherapy are included. The primary outcome is the development of CTRCD within 12 months after the last treatment cycle (and no more than 24 months after the first). The secondary outcomes are severe CTRCD (LVEF \<40%) and all-cause mortality.
This study aims to validate the clinical utility of AI-enhanced ECG as a simple, accessible, and cost-effective tool for predicting CTRCD across diverse cancer treatment regimens, including newer immunotherapies.
Conditions
- Cancer Therapy-Related Cardiac Dysfunction
- Heart Failure
- Left Ventricular Dysfunction
Interventions
- OTHER
-
No intervention (retrospective observational study)
This is a retrospective observational study using existing clinical data. No intervention or diagnostic procedure is applied to participants.
Sponsors & Collaborators
-
Korea Health Industry Development Institute
collaborator OTHER_GOV -
VUNO Inc.
collaborator INDUSTRY -
Yonsei University
lead OTHER
Eligibility
- Min Age
- 19 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2006-05-01
- Primary Completion
- 2022-12-31
- Completion
- 2022-12-31
Countries
- South Korea
Study Locations
More Related Trials
-
AI-Based Prediction of Atrial Fibrillation in ESUS Patients With ICM
NCT07347691 ·Status: RECRUITING
-
AI-Enabled Diagnosis and Prognosis of Hypertrophic Cardiomyopathy
NCT07263204 ·Status: RECRUITING
-
Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest
NCT06179849 ·Status: NOT_YET_RECRUITING
-
Prospective Evaluation of AI-ECG for SHD Detection
NCT07057466 ·Status: RECRUITING
-
Assessment for Long-Term Cardiovascular Impairment Associated With Trastuzumab Cardiotoxicity in HER2-Positive Breast Cancer Survivors
NCT02615054 ·Status: COMPLETED
-
External Validation of Artificial Intelligence-enabled Electrocardiography (AI-ECG) for the Detection of Left Ventricular Dysfunction (LVD)
NCT07038018 ·Status: NOT_YET_RECRUITING
-
Telesonography for Visually Estimating Ejection Fraction
NCT02960685 ·Status: UNKNOWN
-
Deep Learning Enhanced Detection of Aortic Stenosis - The DETECT-AS-Diagnostic Study
NCT06749145 ·Status: ENROLLING_BY_INVITATION ·Phase: NA
-
Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Identification of Structural Heart Disease
NCT06462989 ·Status: ENROLLING_BY_INVITATION ·Phase: NA
-
Novel On-body Evaluation of Cardiac Health for Oncology
NCT07158450 ·Status: ENROLLING_BY_INVITATION
-
A Multi-center Study on Artificial Intelligence-Based Quantitative Evaluation of Echocardiography
NCT07133516 ·Status: RECRUITING
-
Assessment of Left Ventricular Diastolic Function in Patients With Atrial Fibrillation
NCT04654806 ·Status: UNKNOWN
-
Safety and Efficacy Study of AI LVEF
NCT05140642 ·Status: COMPLETED ·Phase: NA
-
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
NCT07079592 ·Status: RECRUITING ·Phase: NA
-
Precision of AI-Based Cardiac Ultrasound for LVEF in the Elderly
NCT06478901 ·Status: COMPLETED
-
AI Assessment of Low-Gradient Aortic Stenosis Severity Based on Echocardiography
NCT07144189 ·Status: RECRUITING
-
Home-based Outpatient Multicenter Evaluation Using Electrocardiogram (HOME-ECG)
NCT07488052 ·Status: RECRUITING
-
Predicting Severe Cardiac Arrhythmias in the Perioperative Period Using AI-ECG
NCT07348991 ·Status: NOT_YET_RECRUITING
-
An Observational Study Using Artificial Intelligence (AI) Algorithms on Electrocardiography (ECG), Point-of-care Ultrasound (POCUS), and Transthoracic Echocardiophy (TTE) to Estimate the Under-diagnosis of Transthyretin Amyloid Cardiomyopathy (ATTR-CM) Across a Diverse Range of US Health Systems.
NCT07062848 ·Status: ACTIVE_NOT_RECRUITING
-
Implementation for Heart Failure Therapies Post-discharge Followed by CardiOSIgnal at HOME
NCT06944405 ·Status: NOT_YET_RECRUITING ·Phase: NA
-
Prospective Longitudinal Evaluation of AI-ECG in a NEwly Diagnosed Heart Failure
NCT05817136 ·Status: UNKNOWN
-
Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care
NCT06637293 ·Status: NOT_YET_RECRUITING ·Phase: NA
-
Pre-operative Focused Transthoracic Echocardiography for Prediction of Post-operative Cardiac Complications
NCT04114474 ·Status: COMPLETED
-
THE RELATIONSHIP BETWEEN CARDIAC RESYNCHRONİZATION THERAPY RESPONSE IN HEART FAILURE PATIENTS AND YKL-40 LEVELS
NCT07469137 ·Status: ACTIVE_NOT_RECRUITING
-
Use of Artificial Intelligence-Guided Echocardiography to assIst cardiovascuLar Patient managEment
NCT05558605 ·Status: RECRUITING ·Phase: NA