Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300, Massy, France.
Université de Paris Cité, Department of Cardiology, Hôpital Lariboisière-APHP, Inserm UMRS 942, France.
Eur Heart J Cardiovasc Imaging. 2023 Aug 23;24(9):1269-1279. doi: 10.1093/ehjci/jead100.
To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular (CV) magnetic resonance (CMR) can provide incremental prognostic value.
Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement. Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine-learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as CV mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2152 patients [66 ± 12 years, 77% men, 1:1 matched patients (1076 with normal and 1076 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8-5.5) years] after adjustment for risk factors in the propensity-matched population [adjusted hazard ratio (HR), 1.12 (95% CI, 1.06-1.18)], and patients with normal CMR [adjusted HR, 1.35 (95% CI, 1.19-1.53), both P < 0.001], but not in patients with abnormal CMR (P = 0.058). In patients with normal CMR, an increased stress-GCS showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.14; NRI = 0.430; IDI = 0.089, all P < 0.001; LR-test P < 0.001).
Stress-GCS is not a predictor of MACE in patients with ischaemia, but has an incremental prognostic value in those with a normal CMR although the absolute event rate remains low.
确定基于完全自动化人工智能的全局周向应变（GCS）在血管扩张剂应激心血管（CV）磁共振（CMR）评估中是否能够提供额外的预后价值。
在 2016 年至 2018 年期间，一项纵向研究纳入了所有通过诱导性缺血和/或晚期钆增强定义的异常应激 CMR 的连续患者。使用倾向评分匹配选择具有正常应激 CMR 的对照患者。使用基于短轴电影图像特征跟踪成像的全自动机器学习算法评估应激-GCS。主要结局是主要不良临床事件（MACE）的发生，定义为 CV 死亡率或非致死性心肌梗死。Cox 回归在倾向匹配人群中调整传统预后因素后，评估了应激-GCS 与主要结局之间的关联。在 2152 例患者[66±12 岁，77%为男性，1：1 匹配患者（正常 CMR1076 例，异常 CMR1076 例）]中，应激-GCS 与 MACE[中位随访 5.2（4.8-5.5）年]相关，在倾向匹配人群中调整危险因素后[调整后的危险比（HR），1.12（95%可信区间，1.06-1.18）]，且正常 CMR 患者[调整 HR，1.35（95%可信区间，1.19-1.53），均 P<0.001]，但异常 CMR 患者则不然（P=0.058）。在正常 CMR 患者中，应激-GCS 增加可显著提高模型判别和重新分类的效果，优于传统和应激 CMR 结果（C 统计量改善：0.14；NRI=0.430；IDI=0.089，均 P<0.001；LR 检验 P<0.001）。
在缺血患者中，应激-GCS 不是 MACE 的预测因素，但在 CMR 正常的患者中有额外的预后价值，尽管绝对事件发生率仍然较低。
