Department of Scientific Partnerships, Siemens Healthcare France, 93200 Saint-Denis, France.
Department of Cardiology, Université Paris Cité, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010 Paris, France.
Eur Heart J Cardiovasc Imaging. 2024 Sep 30;25(10):1338-1348. doi: 10.1093/ehjci/jeae168.
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.
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.
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.
本研究旨在确定接受压力心血管磁共振（CMR）检查的患者中，完全基于人工智能（AI）的自动压力左心室射血分数（LVEFAI）是否能够提供额外的预后价值，以预测除传统预后因素外的死亡。
在 2016 年至 2018 年间，我们进行了一项纵向研究，该研究纳入了所有因进行血管扩张剂压力 CMR 检查而被转诊的连续患者。使用 AI 算法对 LVEFAI 进行评估，该算法结合了多个用于 LV 分割的深度学习网络。主要终点是使用法国国家死亡登记处评估的全因死亡。使用 Cox 回归评估压力 LVEFAI 与调整传统危险因素和 CMR 发现后的死亡之间的关联。在 9712 例患者（66±15 岁，67%为男性）中，压力 LVEFAI 与专家（LVEFexpert）测量的压力 LVEF 之间存在极好的相关性（r=0.94，P<0.001）。在调整危险因素后，压力 LVEFAI 与死亡相关[中位（四分位距）随访时间 4.5（3.7-5.2）年]（调整后的危险比为每增加 5%，0.84（95%置信区间，0.82-0.87），P<0.001）。压力 LVEFAI 与死亡发生的相关性与 LVEFexpert 相似。调整后，压力 LVEFAI 值在传统危险因素和压力 CMR 发现之上显示出对模型区分度和重新分类的最大改善（C 统计量改善：0.11；净重新分类改善=0.250；综合鉴别指数=0.049，均 P<0.001；似然比检验 P<0.001），与静息时 LVEFAI 相比具有额外的预后价值。
在接受压力 CMR 检查的患者中，基于 AI 的全自动压力 LVEF 与死亡的发生独立相关，在传统危险因素、诱发性缺血和晚期钆增强之上具有额外的预后价值。
