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

项目摘要/摘要 由于治疗和支持性护理的改进，儿童癌症的五年存活率现在超过了85%。 然而，接受蒽环类药物化疗或胸部定向放射治疗的患者存在剂量相关的风险。 治疗不良心血管后遗症，包括心肌病、冠状动脉疾病和瓣膜心脏病 疾病，对生活质量和总体生存有负面影响。及早认识和干预 管理儿童癌症幸存者(CCS)的心脏发病率可以提供改善的机会 剩余生活的质量。为了促进心肌病的早期发现，儿童肿瘤学小组的 指南建议每2至5年进行一次超声心动图(ECHO)终生筛查CCS。而当 提供了早期发现心肌功能障碍的机会，筛查指南没有确定 收缩功能保留的患者，未来可能发展为心肌病。我们最重要的是 长期目标是开发一种可推广的人工智能(AI)工具，使用可以识别 CCS是未来心肌病的高危人群。我们已经在圣犹大终身队列(SJLIFE)的子集上展示了 研究数据可高精度预测10年内CCS心肌病高危人群 (AUC为0.87)通过人工智能(AI)仅使用原始数字心电(ECG)数据。我们的目标是 这个项目是开发一个健壮的(目标1)，可推广(目标2)和远程适用(目标3)的人工智能工具， 可以从低成本和高度可访问的心电数据中识别具有心肌病风险的CCS。我们将实现我们的目标 目标通过以下三个具体目标： 目的1.开发一种人工智能工具来预测CCS中未来心肌病的风险：我们将利用以下数据 3731名SJLIFE参与者改进和内部验证一种新的AI工具，预测CCS的高风险 心肌病(定义为射血分数下降50%或10%)，在随后的3年、5年和10年。我们 我将使用信号处理和深度学习来生成表示ECG的特征，并在 预测心肌病的机器学习。 目标2.对阿姆斯特丹后期队列的一个子组执行人工智能工具的外部验证。 我们将在爱玛儿童医院对我们治疗儿童癌症的343个CCS的AI工具进行外部验证 荷兰的医院/学术医学中心。我们将评估AI-Tool性能的一致性 在后一组中，SJLIFE坚持测试队列。 目的3.评价智能手表远程预测心肌病的可行性。我们会收集 在SJLIFE参与者的日常考试中，通过智能手表对他们的子集进行心电图检查，并评估。 AI-Tool智能手表心电与临床心电风险预测的一致性。 影响：我们的结果提供了通过以下方式对CCS健康产生积极影响的潜力：1)确定哪些人可能受益 来自更频繁或更高级的心脏成像，以及2)远程和实时指导未来的研究 预测迟发性心肌病。 0

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