利用心电图图像对癌症治疗相关心功能障碍进行人工智能增强风险分层。

IF 6.2 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Evangelos K Oikonomou, Veer Sangha, Lovedeep S Dhingra, Arya Aminorroaya, Andreas Coppi, Harlan M Krumholz, Lauren A Baldassarre, Rohan Khera
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引用次数: 0

摘要

背景:癌症治疗相关心功能障碍(CTRCD)的风险分层策略依赖于专业成像的连续监测,这限制了其可扩展性。我们旨在研究人工智能(AI)在心电图(ECG)图像中的应用,将其作为成像风险生物标志物的替代物,并研究其与早期 CTRCD 的关联。研究方法在美国的一个医疗系统中(2013-2023 年),我们确定了 1550 名无心肌病的患者(年龄 60 [IQR:51-69] 岁,女性 1223 [78.9%]),这些患者因乳腺癌或非霍奇金淋巴瘤接受了蒽环类药物和/或曲妥珠单抗治疗,并在治疗前 12 个月进行了心电图检查。我们将经过验证的左心室收缩功能障碍(LVSD)人工智能模型应用到基线心电图图像中,并根据人工智能-心电图 LVSD 概率结果定义了低、中、高风险组:在 1550 名无已知心肌病的患者中(中位随访时间:14.1 [IQR:13.4-17.1] 个月),83 人(5.4%)、562 人(36.3%)和 905 人(58.4%)根据基线 AI-ECG 分为高危、中危和低危。高风险与低风险 AI-ECG 筛查(≥0.1 vs 结论:高风险与低风险 AI-ECG 筛查之间存在差异:对基线心电图图像应用 AI 可以对乳腺癌或非霍奇金淋巴瘤治疗过程中与蒽环类或曲妥珠单抗暴露相关的早期 CTRCD 风险进行分层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

Background: 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 electrocardiographic (ECG) images as a surrogate for imaging risk biomarkers, and its association with early CTRCD. Methods: Across a U.S.-based health system (2013-2023), we identified 1,550 patients (age 60 [IQR:51-69] years, 1223 [78.9%] women) without cardiomyopathy who received anthracyclines and/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 (LVSD) to baseline ECG images and defined low, intermediate, and high-risk groups based on AI-ECG LVSD 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 [LVEF]<50%), or LVEF<40%, up to 12 months post-treatment. In a mechanistic analysis, we assessed the association between global longitudinal strain (GLS) and AI-ECG LVSD probabilities in studies performed within 15 days of each other. Results: Among 1,550 patients without known cardiomyopathy (median follow-up: 14.1 [IQR:13.4-17.1] months), 83 (5.4%), 562 (36.3%) and 905 (58.4%) were classified as high, intermediate, and low risk by baseline AI-ECG. A high- vs low-risk AI-ECG screen (≥0.1 vs <0.01) was associated with a 3.4-fold and 13.5-fold higher incidence of CTRCD (adj.HR 3.35 [95%CI:2.25-4.99]) and LVEF<40% (adj.HR 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 1,428 temporally-linked echocardiograms and ECGs, AI-ECG LVSD probabilities were associated with worse GLS (GLS -19% [IQR:-21 to -17%] for probabilities <0.1, to -15% [IQR:-15 to -9%] for ≥0.5 [p<0.001]). Conclusions: 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 or non-Hodgkin lymphoma therapy.

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来源期刊
Circulation-Cardiovascular Quality and Outcomes
Circulation-Cardiovascular Quality and Outcomes CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
8.50
自引率
2.90%
发文量
357
审稿时长
4-8 weeks
期刊介绍: Circulation: Cardiovascular Quality and Outcomes, an American Heart Association journal, publishes articles related to improving cardiovascular health and health care. Content includes original research, reviews, and case studies relevant to clinical decision-making and healthcare policy. The online-only journal is dedicated to furthering the mission of promoting safe, effective, efficient, equitable, timely, and patient-centered care. Through its articles and contributions, the journal equips you with the knowledge you need to improve clinical care and population health, and allows you to engage in scholarly activities of consequence to the health of the public. Circulation: Cardiovascular Quality and Outcomes considers the following types of articles: Original Research Articles, Data Reports, Methods Papers, Cardiovascular Perspectives, Care Innovations, Novel Statistical Methods, Policy Briefs, Data Visualizations, and Caregiver or Patient Viewpoints.
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