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: 10457160
• 负责人: Oguz Akbilgic
• 金额: $ 56.38万
• 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/10457160
• 关键词: Academic Medical Centers; Adult; Algorithms; Anthracycline; Artificial Intelligence; Cancer Survivorship; Cardiac; Cardiology; Cardiomyopathies; Cardiovascular system; Characteristics; Chest; Childhood; Clinical; 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; Methods; Modality; Modeling; Morbidity - disease rate; Myocardial; Myocardial dysfunction; Participant; Patients; Pediatric Hospitals; Pediatric Oncology; Pediatric Oncology Group; Performance; Quality of life; Radiation; 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; base; 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

项目摘要/摘要 由于治疗的改善和支持性护理，儿童癌症的五年生存率现在超过85％。 但是，接受蒽环类化疗或胸部指导辐射治疗的患者具有与剂量有关的风险 用于心血管后遗症，包括心肌病，冠状动脉疾病和瓣膜心脏 疾病，对生活质量和整体生存产生负面影响。早期认可和干预措施 在儿童癌症幸存者（CC）中管理心脏发病率可以提供改善的机会 剩余生活的质量。为了促进儿童肿瘤学组的早期发现心肌病 指南建议每2至5年对CC进行超声心动图（Echo）的终身筛查。尽管 筛查指南提供早期发现心肌功能障碍的机会 保留收缩功能的患者将来可能患有心肌病。我们的总体 长期目标是使用可以识别的ECG跟踪来开发可推广的人工智能（AI） - 工具 将来心肌病的CC风险很高。我们已经在圣裘德终生队列（SJLife）的子集中展示 可以高准确性预测患心肌病风险高的CC的CCS。 （AUC为0.87）仅使用原始数字心电图（ECG）数据通过人工智能（AI）。我们的目标 该项目是为了开发一个强大的（AIM 1），可推广（AIM 2）和远程适用（AIM 3）AI-Tool， 可以从低成本和高度访问的ECG数据中鉴定出心肌病风险的CC。我们将实现我们的 通过遵循三个特定目标来实现目标： 目标1。开发一种AI工具来预测CCS中未来心肌病的风险：我们将利用来自 3,731名SJLife参与者可以在内部进行完善和内部验证新型的AI工具，以预测CCS的高风险 在随后的3、5和10年中，心肌病（定义为射血分数<50％或> 10％下降）。我们 将使用信号处理和深度学习来生成代表ECG的功能，并在 机器学习以预测心肌病。 AIM 2。在后来的阿姆斯特丹队列的子组上对AI工具进行外部验证。 我们将在艾玛儿童的儿童癌症治疗的343 ccs上验证我们的AI-Tool 荷兰的医院/学术医疗中心。我们将评估Ai-Tool性能的一致性 后来的队列与SJLife的测试队列。 目标3。通过智能手表评估远程心肌病预测的可行性。我们将收集 ECG在SJLife参与者的子集中通过智能手表进行常规考试并评估。 使用智能手表ECG与临床心电图对AI-Tool进行风险预测的一致性。 影响：我们的结果提供了积极影响CCS健康的潜力1）确定可能受益的人 从更频繁或高级的心脏成像，以及2）指导未来的遥控和实时研究 预测晚期心肌病。 0