Early Identification of Childhood Cancer Survivors at High Risk for Late Onset Cardiomyopathy: An Artificial Intelligence Approach utilizing Electrocardiography

早期识别迟发性心肌病高风险儿童癌症幸存者：利用心电图的人工智能方法

• 批准号: 10610470
• 负责人: Oguz Akbilgic
• 金额: $ 48.77万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10610470
• 关键词: Academic Medical Centers; Adult; Algorithms; Anthracycline; Artificial Intelligence; Cancer Survivorship; Cardiac; Cardiology; Cardiomyopathies; Cardiovascular system; Characteristics; Chest; Childhood; Clinical; Cohort Studies; Communities; Coronary Arteriosclerosis; Data; Development; Diagnosis; Dose; Drops; EFRAC; Early Diagnosis; Early identification; Echocardiography; Electrocardiogram; Epidemiology; Functional disorder; Future; Goals; Guidelines; Health; Heart Injuries; Heart Valve Diseases; Heart failure; Intervention; Lead; Left Ventricular Ejection Fraction; Left Ventricular Remodeling; Life; Machine Learning; Malignant Childhood Neoplasm; Measures; Modality; Modeling; Morbidity - disease rate; Myocardial; Myocardial dysfunction; Participant; Patients; Pediatric Hospitals; Pediatric Oncology; Pediatric Oncology Group; Performance; Quality of life; Radiation; Recommendation; Research; Research Personnel; Risk; Saint Jude Children' s Research Hospital; Shapes; Specificity; Subgroup; Supportive care; Survival Rate; Survivors; Technology; Testing; Time; Treatment-Related Cancer; Validation; Visit; Work; artificial intelligence method; cancer type; chemotherapy; childhood cancer survivor; cohort; cost; deep learning; demographics; digital; feature extraction; feature selection; follow-up; gradient boosting; heart function; heart imaging; heart preservation; high risk; improved; novel; novel strategies; predictive modeling; predictive tools; preservation; prevent; primary outcome; risk prediction; risk stratification; screening; screening guidelines; secondary outcome; signal processing; smart watch; tool; tumor progression

Project Summary/Abstract Due to improved treatment and supportive care, five-year survival rates for childhood cancer now exceed 85%. However, patients treated with anthracycline chemotherapy or chest-directed radiation have a dose-related risk for adverse cardiovascular sequelae, including cardiomyopathy, coronary artery disease and valvular heart disease, with a negative impact on quality of life and overall survival. Earlier recognition and interventions to manage cardiac morbidity among childhood cancer survivors (CCS) could provide opportunities to improve quality of remaining life. To facilitate early detection of cardiomyopathy, the Children's Oncology Group's guidelines recommend life-long screening of CCS with echocardiography (ECHO) every 2 to 5 years. While offering an opportunity for early detection of myocardial dysfunction, screening guidelines do not identify patients with preserved systolic function who may develop cardiomyopathy in the future. Our overarching long-term goal is to develop a generalizable artificial intelligence (AI)-tool using ECG tracings that can identify CCS at high risk for future cardiomyopathy. We have shown on a subset of St. Jude Lifetime Cohort (SJLIFE) study data that CCS at high risk for cardiomyopathy withing 10 years can be predicted with high accuracy (AUC of 0.87) via artificial intelligence (AI) using raw digital electrocardiography (ECG) data only. Our goal in this project is to develop a robust (Aim 1), generalizable (Aim 2), and remotely applicable (Aim 3) AI-tool that can identify CCS at cardiomyopathy risk from low-cost and highly-accessible ECG data. We will achieve our goal by following three specific aims: Aim 1. Develop an AI tool to predict risk of future cardiomyopathy among CCS: We will utilize data from 3,731 SJLIFE participants to refine and internally validate a novel AI-tool predicting CCS at high risk for cardiomyopathy (defined as ejection fraction < 50% or >10% drop), in the subsequent 3, 5, and 10 years. We will use signal processing and deep learning to generate features representing ECGs and use these features in machine learning to predict cardiomyopathy. Aim 2. Perform an external validation of the AI tool on a subgroup of the Amsterdam LATER Cohort. We will externally validate our AI-tool on 343 CCS treated for childhood cancer at the Emma Children's Hospital/Academic Medical Center in Netherland. We will assess the concordance of the AI-tool performance on the LATER cohort vs hold out test cohort at SJLIFE. Aim 3. Evaluate the feasibility of remote cardiomyopathy prediction via smartwatch. We will collect ECGs on a subset of SJLIFE participants via a smartwatch during their routine exam and assess the. concordance of risk predictions by AI-tool using smartwatch ECG vs clinical ECG. Impact: Our results offer the potential to positively impact CCS health by 1) identifying those who may benefit from more frequent or advanced cardiac imaging, and 2) guiding future studies in remote and real time prediction of late-onset cardiomyopathy. 0

项目总结/文摘