CAD: Chronic Coronary Syndromes
Theo Pezel, MD
Head of the Cardiovascular Imaging department
Lariboisiere University Hospital, APHP, Paris, France
Paris, Ile-de-France, France
Theo Pezel, MD
Head of the Cardiovascular Imaging department
Lariboisiere University Hospital, APHP, Paris, France
Paris, Ile-de-France, France
Philippe Garot, MD
Cardiologist
Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Massy, France
Solenn Toupin, PhD
Clinical scientist
Siemens Healthcare France, Scientific partnerships, Saint-Denis
Bordeaux, Aquitaine, France
Thomas Hovasse, MD
Cardiologist
Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Massy, France
Francesca Sanguineti, MD
Cardiologist
Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Massy, France
Stéphane Champagne, MD
Cardiologist
Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Massy, France
Stéphane Morisset
Biostatistician
Hôpital Lariboisière – APHP, France
Teodora Chitiboi, PhD
Research Scientist
Siemens Healthcare GmbH, Hamburg, Germany
Hamburg, Germany
Puneet Sharma, PhD
Research & Technology Manager
Siemens Medical Solutions USA, Inc., Princeton, NJ, United States
Princeton, New Jersey, United States
Thierry Unterseeh, MD
Cardiologist
Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Massy, France
Jerome Garot, PhD
Head
ICPS - Massy
Massy, Ile-de-France, France
Left ventricular global circumferential strain using cardiovascular magnetic resonance (CMR) is an accurate indicator to predict cardiovascular events. Although several studies have shown the excellent prognostic value of stress CMR, the prognostic value of stress global circumferential strain (sGCS) remains unknown. The aim of the study was 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.
Methods:
Between 2016 and 2018, a longitudinal study was carried out including 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 were used to evaluate the association of stress-GCS with the primary outcome after adjustment for traditional prognosticators including ischemia and LGE.
Results:
In 2,670 patients [65±12 years, 68% men, 1:1 matched patients (1,335 with normal and 1,335 with abnormal CMR)], stress-GCS was positively associated with MACE (median follow-up 5.2 (4.8-5.5) years) before and after adjustment for risk factors in the overall 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).
Conclusion:
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, defined by the absence of ischaemia and LGE.