Artificial intelligence-based fully automated stress left ventricular ejection fraction as a prognostic marker in patients undergoing stress cardiovascular magnetic resonance

Author:

Toupin Solenn1ORCID,Pezel Théo23456ORCID,Hovasse Thomas6ORCID,Sanguineti Francesca6,Champagne Stéphane6,Unterseeh Thierry6,Duhamel Suzanne6,Chitiboi Teodora7,Jacob Athira J8,Borgohain Indraneel8,Sharma Puneet8,Gonçalves Trecy23456ORCID,Martial Paul-Jun23456,Gall Emmanuel2346ORCID,Florence Jeremy2346ORCID,Unger Alexandre2346ORCID,Garot Philippe6ORCID,Garot Jérôme6ORCID

Affiliation:

1. Department of Scientific Partnerships , Siemens Healthcare France, 93200 Saint-Denis , France

2. Department of Cardiology, Université Paris Cité, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP) , 75010 Paris , France

3. MIRACL.ai Laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere (AP-HP) , 75010 Paris , France

4. Inserm MASCOT - UMRS 942, University Hospital of Lariboisiere , 75010 Paris , France

5. Department of Radiology, Université Paris Cité, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP) , 75010 Paris , France

6. Institut Cardiovasculaire Paris Sud (ICPS), Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques CARTIER, Ramsay Santé , 6 Avenue du Noyer Lambert, 91300 Massy , France

7. Department of Engineering, Siemens Healthcare GmbH , Lindenplatz 2, 20099 Hamburg , Deutschland

8. Digital Technologies and Innovation, Siemens Healthineers , 755 College Road East, Princeton, NJ 08540 , USA

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

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