使用心电图图像的人工智能增强风险分层与癌症治疗相关的心脏功能障碍

与癌症治疗相关的心脏功能障碍（CTRCD）的风险分层策略依赖于串行监测，从而限制了其可扩展性。我们的目的是研究人工智能（AI）在ECG图像中的应用，作为对风险生物标志物进行成像的替代品及其与早期CTRCD的相关性。用于乳腺癌或非霍奇金淋巴瘤，并且在治疗前进行了ECG≤12个月。我们分别基于AI-ECG左心室收缩期功能障碍概率，分别部署了左室收缩功能障碍的经过验证的左室收缩功能障碍的AI模型，并定义了低危，中间和高风险组的AI模型，分别为<0.01、0.01至0.01至0.1和0.1.1和nomentim.1（正屏幕）。我们探索了早期CTRCD（新的心肌病，心力衰竭或左心室射血分数<50％）或治疗后12个月的左心室射血分数<40％。在机械分析中，我们评估了全球纵向应变与AI-ECG左心室收缩功能障碍概率之间的关联。分别按基线AI-ECG分别归类为高，中间和低风险。高风险与低风险AI-ECG屏幕（≥0.1对<0.01）与CTRCD的发生率（调整后危险比，3.35 [95％CI，2.25-4.99]）相关，并增加13.5倍，左心室散发分数<40％（调整Hazard Ratio，3.0％），3.05％，13.5％，135％C.95％，5％CTRCD [95％CI，2.25-4.99]）。 分别。事后分析在CTRCD事件发生后6至12个月内支持AI-ECG概率的纵向增加。在1428个时间连接的超声心动图和ECG中，AI -ECG左心室收缩功能障碍概率与较差的全球纵向应变（全球纵向应变，-19％，四分位间范围，-21％至-17％）的概率<0.1，至-15％in Interfartile范围，-15％，-15％to -15％，-15％[-15％] [p <0.001]）。适用于基线ECG图像的AI可以在乳腺癌和非霍奇金淋巴瘤治疗的情况下分层与蒽环类或曲妥珠单抗相关的早期CTRCD的风险。

Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to ECG images as a surrogate for imaging risk biomarkers and its association with early CTRCD.Across a US-based health system (2013-2023), we identified 1550 patients (aged, 60 [interquartile range, 51-69] years, 1223 [78.9%] women) without cardiomyopathy who received anthracyclines or trastuzumab for breast cancer or non-Hodgkin lymphoma and had ECG performed ≤12 months before treatment. We deployed a validated AI model of left ventricular systolic dysfunction to baseline ECG images and defined low-, intermediate-, and high-risk groups based on AI-ECG left ventricular systolic dysfunction probabilities of <0.01, 0.01 to 0.1, and ≥0.1 (positive screen), respectively. We explored the association with early CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction <50%), or left ventricular ejection fraction <40%, up to 12 months after treatment. In a mechanistic analysis, we assessed the association between global longitudinal strain and AI-ECG left ventricular systolic dysfunction probabilities in studies performed within 15 days of each other.Among 1550 patients without known cardiomyopathy (median follow-up, 14.1 [interquartile range, 13.4-17.1] months), 83 (5.4%), 562 (36.3%), and 905 (58.4%) were classified as high, intermediate, and low risk, respectively, by baseline AI-ECG. A high-risk versus low-risk AI-ECG screen (≥0.1 versus <0.01) was associated with a 3.4-fold and 13.5-fold higher incidence of CTRCD (adjusted hazard ratio, 3.35 [95% CI, 2.25-4.99]) and left ventricular ejection fraction <40% (adjusted hazard ratio, 13.52 [95% CI, 5.06-36.10]), respectively. Post hoc analyses supported longitudinal increases in AI-ECG probabilities within 6 to 12 months of a CTRCD event. Among 1428 temporally linked echocardiograms and ECGs, AI-ECG left ventricular systolic dysfunction probabilities were associated with worse global longitudinal strain (global longitudinal strain, -19% [interquartile range, -21% to -17%] for probabilities <0.1, to -15% [interquartile range, -15% to -9%] for ≥0.5 [P<0.001]).AI applied to baseline ECG images can stratify the risk of early CTRCD associated with anthracycline or trastuzumab exposure in the setting of breast cancer and non-Hodgkin lymphoma therapy.