基于人工智能的全自动应激左心室射血分数作为应激-CMR 患者的预后指标

IF 6.7 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Solenn Toupin, Théo Pezel, Thomas Hovasse, Francesca Sanguineti, Stéphane Champagne, Thierry Unterseeh, Suzanne Duhamel, Teodora Chitiboi, Athira J Jacob, Indraneel Borgohain, Puneet Sharma, Trecy Gonçalves, Paul-Jun Martial, Emmanuel Gall, Jeremy Florence, Alexandre Unger, Philippe Garot, Jérôme Garot
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引用次数: 0

摘要

目的:确定在接受应激CMR的患者中,基于全自动应激人工智能(AI)的左室射血分数(LVEFAI)能否提供高于传统预后指标的增量预后价值,以预测死亡:2016年至2018年期间,我们开展了一项纵向研究,纳入了所有转诊接受血管舒张应激CMR的连续患者。使用人工智能算法结合多个深度学习网络进行左心室分割,评估 LVEFAI。主要结果是通过法国国家死亡登记处评估的全因死亡。在9712名患者(66±15岁,67%为男性)中,压力LVEFAI与专家(LVEFexpert)测量的LVEF之间存在极好的相关性(r=0.94,p):基于人工智能的应激时全自动 LVEF 测量结果与接受应激 CMR 患者的死亡发生率独立相关,其预后价值高于传统的风险因素、诱导性缺血和 LGE。
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-based fully automated stress left ventricular ejection fraction as a prognostic marker in patients undergoing stress cardiovascular magnetic resonance.

Aims: This study aimed to determine in patients undergoing stress cardiovascular magnetic resonance (CMR) whether fully automated stress artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic value to predict death above traditional prognosticators.

Methods and results: Between 2016 and 2018, we conducted a longitudinal study that included all consecutive patients referred for vasodilator stress CMR. LVEFAI was assessed using AI algorithm combines multiple deep learning networks for LV segmentation. The primary outcome was all-cause death assessed using the French National Registry of Death. Cox regression was used to evaluate the association of stress LVEFAI with death after adjustment for traditional risk factors and CMR findings. In 9712 patients (66 ± 15 years, 67% men), there was an excellent correlation between stress LVEFAI and LVEF measured by expert (LVEFexpert) (r = 0.94, P < 0.001). Stress LVEFAI was associated with death [median (interquartile range) follow-up 4.5 (3.7-5.2) years] before and after adjustment for risk factors [adjusted hazard ratio, 0.84 (95% confidence interval, 0.82-0.87) per 5% increment, P < 0.001]. Stress LVEFAI had similar significant association with death occurrence compared with LVEFexpert. After adjustment, stress LVEFAI value showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement: 0.11; net reclassification improvement = 0.250; integrative discrimination index = 0.049, all P < 0.001; likelihood-ratio test P < 0.001), with an incremental prognostic value over LVEFAI determined at rest.

Conclusion: AI-based fully automated LVEF measured at stress is independently associated with the occurrence of death in patients undergoing stress CMR, with an additional prognostic value above traditional risk factors, inducible ischaemia and late gadolinium enhancement.

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来源期刊
European Heart Journal - Cardiovascular Imaging
European Heart Journal - Cardiovascular Imaging CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
11.60
自引率
9.70%
发文量
708
审稿时长
4-8 weeks
期刊介绍: European Heart Journal – Cardiovascular Imaging is a monthly international peer reviewed journal dealing with Cardiovascular Imaging. It is an official publication of the European Association of Cardiovascular Imaging, a branch of the European Society of Cardiology. The journal aims to publish the highest quality material, both scientific and clinical from all areas of cardiovascular imaging including echocardiography, magnetic resonance, computed tomography, nuclear and invasive imaging. A range of article types will be considered, including original research, reviews, editorials, image focus, letters and recommendation papers from relevant groups of the European Society of Cardiology. In addition it provides a forum for the exchange of information on all aspects of cardiovascular imaging.
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