1
|
Kolk MZH, Ruipérez-Campillo S, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Prediction of sudden cardiac death using artificial intelligence: Current status and future directions. Heart Rhythm 2024:S1547-5271(24)03293-4. [PMID: 39245250 DOI: 10.1016/j.hrthm.2024.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 08/21/2024] [Accepted: 09/03/2024] [Indexed: 09/10/2024]
Abstract
Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among people who suffer a SCD, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators for SCD prevention. In response, artificial intelligence (AI) holds promise for personalized SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate nonlinear patterns between complex data and defined end points, and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.
Collapse
Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC, Amsterdam, The Netherlands
| | | | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC, Amsterdam, The Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC, Amsterdam, The Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, California
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC, Amsterdam, The Netherlands.
| |
Collapse
|
2
|
Villegas-Martinez M, de Villedon de Naide V, Muthurangu V, Bustin A. The beating heart: artificial intelligence for cardiovascular application in the clinic. MAGMA (NEW YORK, N.Y.) 2024; 37:369-382. [PMID: 38907767 DOI: 10.1007/s10334-024-01180-9] [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: 12/26/2023] [Revised: 04/25/2024] [Accepted: 06/13/2024] [Indexed: 06/24/2024]
Abstract
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.
Collapse
Affiliation(s)
- Manuel Villegas-Martinez
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France
| | - Victor de Villedon de Naide
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France
| | - Vivek Muthurangu
- Center for Cardiovascular Imaging, UCL Institute of Cardiovascular Science, University College London, London, WC1N 1EH, UK
| | - Aurélien Bustin
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France.
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France.
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| |
Collapse
|
3
|
Hall M, de Marvao A, Schweitzer R, Cromb D, Colford K, Jandu P, O’Regan DP, Ho A, Price A, Chappell LC, Rutherford MA, Story L, Lamata P, Hutter J. Preeclampsia Associated Differences in the Placenta, Fetal Brain, and Maternal Heart Can Be Demonstrated Antenatally: An Observational Cohort Study Using MRI. Hypertension 2024; 81:836-847. [PMID: 38314606 PMCID: PMC7615760 DOI: 10.1161/hypertensionaha.123.22442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/02/2024] [Indexed: 02/06/2024]
Abstract
BACKGROUND Preeclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multiorgan approach to magnetic resonance imaging (MRI) investigation of preeclampsia, with the acquisition of maternal cardiac, placental, and fetal brain anatomic and functional imaging. METHODS An observational study was performed recruiting 3 groups of pregnant women: those with preeclampsia, chronic hypertension, or no medical complications. All women underwent a cardiac MRI, and pregnant women underwent a placental-fetal MRI. Cardiac analysis for structural, morphological, and flow data were undertaken; placenta and fetal brain volumetric and T2* (which describes relative tissue oxygenation) data were obtained. All results were corrected for gestational age. A nonpregnant cohort was identified for inclusion in the statistical shape analysis. RESULTS Seventy-eight MRIs were obtained during pregnancy. Cardiac MRI analysis demonstrated higher left ventricular mass in preeclampsia with 3-dimensional modeling revealing additional specific characteristics of eccentricity and outflow track remodeling. Pregnancies affected by preeclampsia demonstrated lower placental and fetal brain T2*. Within the preeclampsia group, 23% placental T2* results were consistent with controls, these were the only cases with normal placental histopathology. Fetal brain T2* results were consistent with normal controls in 31% of cases. CONCLUSIONS We present the first holistic assessment of the immediate implications of preeclampsia on maternal heart, placenta, and fetal brain. As well as having potential clinical implications for the risk stratification and management of women with preeclampsia, this gives an insight into the disease mechanism.
Collapse
Affiliation(s)
- Megan Hall
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Antonio de Marvao
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- School of Cardiovascular Medicine (A.d.M., R.S.), King’s College London, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (A.d.M., R.S., D.P.O.)
| | - Ronny Schweitzer
- School of Cardiovascular Medicine (A.d.M., R.S.), King’s College London, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (A.d.M., R.S., D.P.O.)
| | - Daniel Cromb
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Priya Jandu
- GKT School of Medical Education (P.J.), King’s College London, United Kingdom
| | - Declan P O’Regan
- MRC London Institute of Medical Sciences, Imperial College London, United Kingdom (A.d.M., R.S., D.P.O.)
| | - Alison Ho
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
- Centre for Medical Engineering (A.P., P.L.), King’s College London, United Kingdom
| | - Lucy C. Chappell
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Lisa Story
- Department of Women and Children’s Health (M.H., A.d.M., A.H., L.C.C., L.S.), King’s College London, United Kingdom
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
| | - Pablo Lamata
- Centre for Medical Engineering (A.P., P.L.), King’s College London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain (M.H., D.C., K.C., A.H., A.P., M.A.R., L.S., J.H.), King’s College London, United Kingdom
- Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Germany (J.H.)
| |
Collapse
|
4
|
Svennberg E, Caiani EG, Bruining N, Desteghe L, Han JK, Narayan SM, Rademakers FE, Sanders P, Duncker D. The digital journey: 25 years of digital development in electrophysiology from an Europace perspective. Europace 2023; 25:euad176. [PMID: 37622574 PMCID: PMC10450797 DOI: 10.1093/europace/euad176] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 08/26/2023] Open
Abstract
AIMS Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal. RESULTS In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP. CONCLUSION Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.
Collapse
Affiliation(s)
- Emma Svennberg
- Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86 Stockholm, Sweden
| | - Enrico G Caiani
- Politecnico di Milano, Electronic, Information and Biomedical Engineering Department, Milan, Italy
- Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Nico Bruining
- Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, The Netherlands
| | - Lien Desteghe
- Research Group Cardiovascular Diseases, University of Antwerp, 2000 Antwerp, Belgium
- Department of Cardiology, Antwerp University Hospital, 2056 Edegem, Belgium
- Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium
- Department of Cardiology, Heart Centre Hasselt, Jessa Hospital, 3500 Hasselt, Belgium
| | - Janet K Han
- Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
- Cardiac Arrhythmia Center, University of California Los Angeles, Los Angeles, CA, USA
| | - Sanjiv M Narayan
- Cardiology Division, Cardiovascular Institute and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | | | - Prashanthan Sanders
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 5005 Adelaide, Australia
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| |
Collapse
|
5
|
Hall M, de Marvao A, Schweitzer R, Cromb D, Colford K, Jandu P, O'Regan DP, Ho A, Price A, Chappell LC, Rutherford MA, Story L, Lamata P, Hutter J. Characterisation of placental, fetal brain and maternal cardiac structure and function in pre-eclampsia using MRI. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23289069. [PMID: 37163073 PMCID: PMC10168502 DOI: 10.1101/2023.04.24.23289069] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Pre-eclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multi-system approach to MRI investigation of pre-eclampsia, with acquisition of maternal cardiac, placental, and fetal brain anatomical and functional imaging. Methods A prospective study was carried out recruiting pregnant women with pre-eclampsia, chronic hypertension, or no medical complications, and a non-pregnant female cohort. All women underwent a cardiac MRI, and pregnant women underwent a fetal-placental MRI. Cardiac analysis for structural, morphological and flow data was undertaken; placenta and fetal brain volumetric and T2* data were obtained. All results were corrected for gestational age. Results Seventy-eight MRIs were obtained during pregnancy. Pregnancies affected by pre-eclampsia demonstrated lower placental and fetal brain T2*. Within the pre-eclampsia group, three placental T2* results were within the normal range, these were the only cases with normal placental histopathology. Similarly, three fetal brain T2* results were within the normal range; these cases had no evidence of cerebral redistribution on fetal Dopplers. Cardiac MRI analysis demonstrated higher left ventricular mass in pre-eclampsia with 3D modelling revealing additional specific characteristics of eccentricity and outflow track remodelling. Conclusions We present the first holistic assessment of the immediate implications of pre-eclampsia on the placenta, maternal heart, and fetal brain. As well as having potential clinical implications for the risk-stratification and management of women with pre-eclampsia, this gives an insight into disease mechanism.
Collapse
Affiliation(s)
- Megan Hall
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Antonio de Marvao
- Department of Women and Children’s Health, King’s College London, UK
- School of Cardiovascular Medicine, King’s College London, UK
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Ronny Schweitzer
- School of Cardiovascular Medicine, King’s College London, UK
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Daniel Cromb
- Centre for the Developing Brain, King’s College London, UK
| | | | - Priya Jandu
- GKT School of Medical Education, King’s College London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, UK
| | - Alison Ho
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Anthony Price
- Centre for the Developing Brain, King’s College London, UK
- Centre for Medical Engineering, King’s College London, UK
| | - Lucy C. Chappell
- Department of Women and Children’s Health, King’s College London, UK
| | | | - Lisa Story
- Department of Women and Children’s Health, King’s College London, UK
- Centre for the Developing Brain, King’s College London, UK
| | - Pablo Lamata
- Centre for Medical Engineering, King’s College London, UK
| | - Jana Hutter
- Centre for the Developing Brain, King’s College London, UK
- Centre for Medical Engineering, King’s College London, UK
| |
Collapse
|
6
|
Loeffler SE, Trayanova N. Primer on Machine Learning in Electrophysiology. Arrhythm Electrophysiol Rev 2023; 12:e06. [PMID: 37427298 PMCID: PMC10323871 DOI: 10.15420/aer.2022.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/10/2023] [Indexed: 07/11/2023] Open
Abstract
Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.
Collapse
Affiliation(s)
- Shane E Loeffler
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University Baltimore, MD, US
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US
| |
Collapse
|
7
|
Suyama S, Kato S, Nakaura T, Azuma M, Kodama S, Nakayama N, Fukui K, Utsunomiya D. Machine learning to predict left ventricular reverse remodeling by guideline-directed medical therapy by utilizing texture feature of extracellular volume fraction in patients with non-ischemic dilated cardiomyopathy. Heart Vessels 2023; 38:361-370. [PMID: 36056933 DOI: 10.1007/s00380-022-02167-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/24/2022] [Indexed: 02/07/2023]
Abstract
Extracellular volume fraction (ECV) by cardiac magnetic resonance (CMR) allows for the non-invasive quantification of diffuse myocardial fibrosis. Texture analysis and machine learning are now gathering attention in the medical field to exploit the ability of diagnostic imaging for various diseases. This study aimed to investigate the predictive value of texture analysis of ECV and machine learning for predicting response to guideline-directed medical therapy (GDMT) for patients with non-ischemic dilated cardiomyopathy (NIDCM). A total of one-hundred and fourteen NIDCM patients [age: 63 ± 12 years, 91 (81%) males] were retrospectively analyzed. We performed texture analysis of ECV mapping of LV myocardium using dedicated software. We calculated nine histogram-based features (mean, standard deviation, maximum, minimum, etc.) and five gray-level co-occurrence matrices. Five machine learning techniques and the fivefold cross-validation method were used to develop prediction models for LVRR by GDMT based on 14 texture parameters on ECV mapping. We defined the LVRR as follows: LVEF increased ≥ 10% points and decreased LVEDV ≥ 10% on echocardiography after GDMT > 12 months. Fifty (44%) patients were classified as non-responders. The area under the receiver operating characteristics curve for predicting non-responder was 0.82 for eXtreme Gradient Boosting, 0.85 for support vector machine, 0.76 for multi-layer perception, 0.81 for Naïve Bayes, 0.77 for logistic regression, respectively. Mean ECV value was the most critical factor among texture features for differentiating NIDCM patients with LVRR and those without (0.28 ± 0.03 vs. 0.36 ± 0.06, p < 0.001). Machine learning analysis using the support vector machine may be helpful in detecting high-risk NIDCM patients resistant to GDMT. Mean ECV is the most crucial feature among texture features.
Collapse
Affiliation(s)
- Shun Suyama
- Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, Japan
| | - Shingo Kato
- Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, Japan. .,Department of Cardiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Kanagawa, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Japan
| | - Mai Azuma
- Department of Cardiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Kanagawa, Japan
| | - Sho Kodama
- Department of Cardiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Kanagawa, Japan
| | - Naoki Nakayama
- Department of Cardiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Kanagawa, Japan
| | - Kazuki Fukui
- Department of Cardiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Kanagawa, Japan
| | - Daisuke Utsunomiya
- Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, Japan
| |
Collapse
|
8
|
Zaidi HA, Jones RE, Hammersley DJ, Hatipoglu S, Balaban G, Mach L, Halliday BP, Lamata P, Prasad SK, Bishop MJ. Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events. Front Cardiovasc Med 2023; 10:1082778. [PMID: 36824460 PMCID: PMC9941157 DOI: 10.3389/fcvm.2023.1082778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/13/2023] [Indexed: 02/10/2023] Open
Abstract
Background Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD). Objective To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD. Methods Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling. Results Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38-2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08-1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29-1.99, p = <0.001. Conclusion Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.
Collapse
Affiliation(s)
- Hassan A. Zaidi
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| | - Richard E. Jones
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Daniel J. Hammersley
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Suzan Hatipoglu
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Gabriel Balaban
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Lukas Mach
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Brian P. Halliday
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sanjay K. Prasad
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Martin J. Bishop
- Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom
| |
Collapse
|
9
|
Asheghan MM, Javadikasgari H, Attary T, Rouhollahi A, Straughan R, Willi JN, Awal R, Sabe A, de la Cruz KI, Nezami FR. Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming. Front Cardiovasc Med 2023; 10:1130152. [PMID: 37082454 PMCID: PMC10111021 DOI: 10.3389/fcvm.2023.1130152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023] Open
Abstract
Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.
Collapse
Affiliation(s)
- Mohammad Mostafa Asheghan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Hoda Javadikasgari
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Taraneh Attary
- Bio-Intelligence Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Amir Rouhollahi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Ross Straughan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - James Noel Willi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Rabina Awal
- Mechanical Engineering Department, University of Louisiana at Lafayette, Louisiana, LA, United States
| | - Ashraf Sabe
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Kim I. de la Cruz
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Correspondence: Farhad R. Nezami
| |
Collapse
|
10
|
de Lepper AGW, Buck CMA, van 't Veer M, Huberts W, van de Vosse FN, Dekker LRC. From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy. JOURNAL OF THE ROYAL SOCIETY, INTERFACE 2022; 19:20220317. [PMID: 36128708 DOI: 10.1098/rsif.2022.0317] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs) later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) implantation to prevent VT-related sudden cardiac death is solely based on symptoms and left ventricular ejection fraction. Catheter ablation of scar-related VTs is performed following ICD therapy, reducing VTs, painful shocks, anxiety, depression and worsening heart failure. We postulate that better prediction of the occurrence and circuit of VT, will improve identification of patients at risk for VT and boost preventive ablation, reducing mortality and morbidity. For this purpose, multiple time-evolving aspects of the underlying pathophysiology, including the anatomical substrate, triggers and modulators, should be part of VT prediction models. We envision digital twins as a solution combining clinical expertise with three prediction approaches: evidence-based medicine (clinical practice), data-driven models (data science) and mechanistic models (biomedical engineering). This paper aims to create a mutual understanding between experts in the different fields by providing a comprehensive description of the clinical problem and the three approaches in an understandable manner, leveraging future collaborations and technological innovations for clinical decision support. Moreover, it defines open challenges and gains for digital twin solutions and discusses the potential of hybrid modelling.
Collapse
Affiliation(s)
| | - Carlijn M A Buck
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marcel van 't Veer
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Frans N van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Lukas R C Dekker
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
11
|
Xie E, Sung E, Saad E, Trayanova N, Wu KC, Chrispin J. Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death. Front Cardiovasc Med 2022; 9:884767. [PMID: 36072882 PMCID: PMC9441865 DOI: 10.3389/fcvm.2022.884767] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
Collapse
Affiliation(s)
- Eric Xie
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Sung
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elie Saad
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Natalia Trayanova
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| |
Collapse
|