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Kunovac A, Hathaway QA, Burrage EN, Coblentz T, Kelley EE, Sengupta PP, Hollander JM, Chantler PD. Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2128-2138. [PMID: 35933241 PMCID: PMC9427680 DOI: 10.1016/j.ultrasmedbio.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 06/02/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
We used segmental strain analysis to evaluate whether intrinsic (diet-induced obesity [DIO]) and extrinsic (unpredictable chronic mild stress [UCMS]) stressors can alter deformational patterns of the left ventricle. Six-week-old male C57BL/6J mice were randomized into the lean or obese group (n = 24/group). Mice underwent 12 wk of DIO with a high-fat diet (HFD). At 18 wk, lean and obese mice were further randomized into UCMS and non-UCMS groups (UCMS, 7 h/d, 5 d/wk, for 8 wk). Echocardiography was performed at baseline (6 wk), post-HFD (18 wk) and post-UCMS (26 wk). Machine learning was applied to the DIO and UCMS groups. There was robust predictive accuracy (area under the receiver operating characteristic curve [AUC] = 0.921) when comparing obese with lean mice, with radial strain changes in the lateral (-64%, p ≤ 0.001) and anterior free (-53%, p < 0.001) walls being most informative. The ability to predict mice that underwent UCMS, irrespective of diet, was assessed (AUC = 0.886), revealing longitudinal strain rate of the anterior midwall and radial strain of the posterior septal wall as the top features. The wall segments indicate a predilection for changes in deformation patterns to the free wall (DIO) and septal wall (UCMS), indicating disease-specific alterations to the myocardium.
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Affiliation(s)
- Amina Kunovac
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA
| | - Quincy A Hathaway
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA.
| | - Emily N Burrage
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Tyler Coblentz
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Eric E Kelley
- Department of Physiology and Pharmacology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Partho P Sengupta
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA; Rutgers Robert Wood Johnson University Hospital, New Brunswick, New Jersey, USA
| | - John M Hollander
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA
| | - Paul D Chantler
- Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, USA
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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. EUROPEAN HEART JOURNAL 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] [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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Stepanova AI, Radova NF, Alekhin MN. Speckle Tracking Stress Echocardiography on Treadmill in Assessment of the Functional Significance of the Degree of Coronary Artery Disease. ACTA ACUST UNITED AC 2021; 61:4-11. [PMID: 33849412 DOI: 10.18087/cardio.2021.3.n1462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 12/18/2020] [Indexed: 11/18/2022]
Abstract
Aim To determine diagnostic capabilities of left ventricular (LV) global longitudinal systolic strain (GLSS) in stress echocardiography (stress-EchoCG) with a treadmill test for diagnosing the functional significance of the degree of coronary stenosis.Material and methods The study included 121 patients (73 men aged 68.3±7.7 years) with suspected or previously diagnosed ischemic heart disease (IHD). Speckle-tracking stress-EchCG (method of tracking speckles on two-dimensional gray-scale ultrasonic images) with a treadmill test and coronarography was performed for all patients. The patients were divided into 3 groups based on the severity of coronary artery (CA) stenosis according to the Gensini scale.Results LV GLSS at rest did not significantly differ between the study groups. After the exercise, LV GLSS was significantly lower in patients with pronounced CA stenosis than in patients without or with moderate CA stenosis (15.9±4.6 % vs. 20.6±3.7 % (p<0.001) and 19.6±3.0 % (p=0.003), respectively). Postexercise LV GLSS <16.9% suggested a pronounced CA stenosis with a sensitivity of 80% and a specificity of 70% (area under the curve, AUC, 0.76±0.06 at 95 % confidence interval, CI, 0.63-0.89; р<0.001). In the patient group without CA stenosis, LV GLSS showed a significant increase after completion of the exercise (from 19.1±3.1 to 20.6±3.7; p=0.04).Conclusion Evaluation of LV GLSS and its dynamics in stress-EchoCG with a treadmill test may be promising in patients with IHD, since in most patients with pronounced CA stenosis, LV GLSS is reduced at baseline and further reduces in response to exercise. In patients without CA stenosis, LV GLSS increases after completing the exercise.
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Affiliation(s)
- A I Stepanova
- Central State Medical Academy of Department оf Presidential Affairs, Moscow
| | - N F Radova
- Central State Medical Academy of Department оf Presidential Affairs, Moscow; Central Clinical Hospital with Out-patient Clinic of Department of Presidential Affairs, Moscow
| | - M N Alekhin
- Central State Medical Academy of Department оf Presidential Affairs, Moscow Central Clinical Hospital with Out-patient Clinic of Department of Presidential Affairs, Moscow
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Picano E, Zagatina A, Wierzbowska-Drabik K, Borguezan Daros C, D’Andrea A, Ciampi Q. Sustainability and Versatility of the ABCDE Protocol for Stress Echocardiography. J Clin Med 2020; 9:E3184. [PMID: 33008112 PMCID: PMC7601661 DOI: 10.3390/jcm9103184] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 09/23/2020] [Accepted: 09/28/2020] [Indexed: 12/19/2022] Open
Abstract
For the past 40 years, the methodology for stress echocardiography (SE) has remained basically unchanged. It is based on two-dimensional, black and white imaging, and is used to detect regional wall motion abnormalities (RWMA) in patients with known or suspected coronary artery disease (CAD). In the last five years much has changed and RWMA is not enough on its own to stratify patient risk and dictate therapy. Patients arriving at SE labs often have comorbidities and are undergoing full anti-ischemic therapy. The SE positivity rate based on RWMA fell from 70% in the eighties to 10% in the last decade. The understanding of CAD pathophysiology has shifted from a regional hydraulic disease to a systemic biologic disease. The conventional view of CAD encouraged the use of coronary anatomic imaging for diagnosis and the oculo-stenotic reflex for the deployment of therapy. This has led to a clinical oversimplification that ignores the lessons of pathophysiology and epidemiology, and in fact, CAD is not synonymous with ischemic heart disease. Patients with CAD may also have other vulnerabilities such as coronary plaque (step A of ABCDE-SE), alveolar-capillary membrane and pulmonary congestion (step B), preload and contractile reserve (step C), coronary microcirculation (step D) and cardiac autonomic balance (step E). The SE methodology based on two-dimensional echocardiography is now integrated with lung ultrasound (step B for B-lines), volumetric echocardiography (step C), color- and pulsed-wave Doppler (step D) and non-imaging electrocardiogram-based heart rate assessment (step E). In addition, qualitative assessment based on the naked eye has now become more quantitative, has been improved by contrast and based on cardiac strain and artificial intelligence. ABCDE-SE is now ready for large scale multicenter testing in the SE2030 study.
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Affiliation(s)
- Eugenio Picano
- Biomedicine Department, CNR Institute of Clinical Physiology, 56124 Pisa, Italy
| | - Angela Zagatina
- Cardiology Department, Saint Petersburg State University Clinic, Saint Petersburg State University, 199034 Saint Petersburg, Russia;
| | - Karina Wierzbowska-Drabik
- First Department and Chair of Cardiology, Bieganski Hospital, Medical University, 90926 Lodz, Poland;
| | | | | | - Quirino Ciampi
- Cardiolody Division, Fatebenefratelli Hospital, 82100 Benevento, Italy;
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