Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR

Abstract

To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular 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 (LGE). 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 cardiovascular mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2670 patients [65 ± 12 years, 68% men, 1:1 matched patients (1335 with normal and 1335 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 (HR: 1.43 [95% CI: 1.30–1.57], both P < 0.001), but not in patients with abnormal CMR (P = 0.33). In patients with normal CMR, an increased stress-GCS > −10% showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.27; NRI = 0.538; IDI = 0.108, all P < 0.001; LR-test P < 0.001). Stress-GCS is independently associated with MACE in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors and stress CMR findings in the group of patients with normal CMR. Prognostic value of AI-based Stress-GCS (Fig. 1).