利用心电图影像增强人工智能在癌症治疗相关心脏功能障碍风险分层中的应用

癌症治疗相关心脏功能障碍（CTRCD）的风险分层策略依赖于专门的影像进行序列监测，限制了其推广应用。我们旨在探讨应用人工智能（AI）于心电图（ECG）影像，作为影像风险生物标志物的替代品，并分析其与早期CTRCD的相关性。在美国某医疗系统（2013-2023年）中，筛选出1550名未患心肌病的患者（中位年龄60岁，四分位数51-69岁，女性1223人，占78.9%），这些患者接受了蒽环类药物或曲妥珠单抗治疗乳腺癌或非霍奇金淋巴瘤，并在治疗前12个月内曾进行心电图检测。我们利用经过验证的左心室收缩功能AI模型对基线ECG进行分析，并根据AI-ECG预测的左心室收缩功能概率将患者分为低风险（<0.01）、中风险（0.01-0.1）和高风险（≥0.1）组（阳性筛查）。分析其与早期CTRCD（新发心肌病、心力衰竭或左心室射血分数<50%）或左心室射血分数<40%的关系，观察时间最长至治疗后12个月。在机制分析中，评估全长应变（global longitudinal strain）与AI-ECG左心室收缩功能概率的关系，基于两项在相距15天内完成的研究数据。结果显示，在无已知心肌病的1550名患者中（中位随访14.1个月），83人（5.4%）高风险组，562人（36.3%）中风险组，905人（58.4%）低风险组。高风险组的AI-ECG筛查（≥0.1）与低风险组（<0.01）相比，CTRCD发生风险增加了3.4倍（调整后风险比，3.35 [95% CI，2.25-4.99]）；左心室射血分数<40%的风险提高了13.5倍（调整后风险比，13.52 [95% CI，5.06-36.10]）。事后分析显示，CTRCD事件发生后6至12个月内，AI-ECG概率呈逐步上升。在1428个时间相关的心脏超声和心电图资料中，AI-ECG预测的左心室收缩功能与全长应变显著相关（概率<0.1时全长应变为-19%，中位数，四分位数-21%至-17%；概率≥0.5时为-15%，四分位数-15%至-9%；P<0.001）。结论：应用于基线ECG的AI模型可以有效分层乳腺癌和非霍奇金淋巴瘤患者中与蒽环类药物或曲妥珠单抗相关的早期CTRCD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.