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O’Driscoll JM, Hawkes W, Beqiri A, Mumith A, Parker A, Upton R, McCourt A, Woodward W, Dockerill C, Sabharwal N, Kardos A, Augustine DX, Balkhausen K, Chandrasekaran B, Firoozan S, Marciniak A, Heitner S, Yadava M, Kaul S, Sarwar R, Sharma R, Woodward G, Leeson P. Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography. Eur Heart J Open 2022; 2:oeac059. [PMID: 36284642 PMCID: PMC9580364 DOI: 10.1093/ehjopen/oeac059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/26/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
AIMS To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. METHODS AND RESULTS SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and -17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69-0.87) to 0.83 (0.74-0.91) or 0.84 (0.75-0.92), respectively. CONCLUSION AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI.
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Affiliation(s)
| | | | - Arian Beqiri
- Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford OX4 2SU, UK
| | - Angela Mumith
- Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford OX4 2SU, UK
| | - Andrew Parker
- Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford OX4 2SU, UK
| | - Ross Upton
- Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford OX4 2SU, UK
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Annabelle McCourt
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - William Woodward
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Cameron Dockerill
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Nikant Sabharwal
- Oxford Heart Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Attila Kardos
- Department of Cardiology, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes MK6 5LD, UK
| | - Daniel X Augustine
- Department of Cardiology, Royal United Hospitals NHS Foundation Trust, Bath BA1 3NG, UK
- Department for Health, University of Bath, Bath BA2 7JU, UK
| | - Katrin Balkhausen
- Department of Cardiology, Royal Berkshire NHS Foundation Trust, Reading RG1 5AN, UK
| | | | - Soroosh Firoozan
- Department of Cardiology, Buckinghamshire Healthcare NHS Trust, High Wycombe HP7 0JD, UK
| | - Anna Marciniak
- Department of Cardiology, St George’s University Hospitals NHS Foundation Trust, Blackshaw Road, Tooting, London SW17 0QT, UK
| | - Stephen Heitner
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mrinal Yadava
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sanjiv Kaul
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rizwan Sarwar
- Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford OX4 2SU, UK
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Oxford Heart Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Experimental Therapeutics, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Rajan Sharma
- Department of Cardiology, St George’s University Hospitals NHS Foundation Trust, Blackshaw Road, Tooting, London SW17 0QT, UK
| | - Gary Woodward
- Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford OX4 2SU, UK
| | - Paul Leeson
- Corresponding author. Tel: +44 (0)1865 572846, Fax: +44 (0)1865 740449,
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