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Kolk MZH, Ruipérez-Campillo S, Allaart CP, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias. Sci Rep 2024; 14:14889. [PMID: 38937555 PMCID: PMC11211323 DOI: 10.1038/s41598-024-65357-x] [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: 02/28/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024] Open
Abstract
The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71-0.96), a sensitivity of 0.98 (95% CI 0.75-1.00) and a specificity of 0.73 (95% CI 0.58-0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65-0.94), ECG branch: 0.54 (95% CI 0.26-0.82), Clinical branch: 0.64 (95% CI 0.39-0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation.
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, The Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Computer Science (D-INFK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, Zurich, Switzerland
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, The Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, The Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, The Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands.
- Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, The Netherlands.
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Scott MJ. Perioperative Patients With Hemodynamic Instability: Consensus Recommendations of the Anesthesia Patient Safety Foundation. Anesth Analg 2024; 138:713-724. [PMID: 38153876 PMCID: PMC10916753 DOI: 10.1213/ane.0000000000006789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 12/30/2023]
Abstract
In November of 2022, the Anesthesia Patient Safety Foundation held a Consensus Conference on Hemodynamic Instability with invited experts. The objective was to review the science and use expert consensus to produce best practice recommendations to address the issue of perioperative hemodynamic instability. After expert presentations, a modified Delphi process using discussions, voting, and feedback resulted in 17 recommendations regarding advancing the perioperative care of the patient at risk of, or with, hemodynamic instability. There were 17 high-level recommendations. These recommendations related to the following 7 domains: Current Knowledge (5 statements); Preventing Hemodynamic Instability-Related Harm During All Phases of Care (4 statements); Data-Driven Quality Improvement (3 statements); Informing Patients (2 statements); The Importance of Technology (1 statement); Launch a National Campaign (1 statement); and Advancing the Science (1 statement). A summary of the recommendations is presented in Table 1 .
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Affiliation(s)
- Michael J. Scott
- From the Department of Anesthesiology and Critical Care Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Anesthesia Critical Care and Pain Medicine, University College London, London, United Kingdom
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4
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Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [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: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
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Lee H, Yang HL, Ryu HG, Jung CW, Cho YJ, Yoon SB, Yoon HK, Lee HC. Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU. NPJ Digit Med 2023; 6:215. [PMID: 37993540 PMCID: PMC10665411 DOI: 10.1038/s41746-023-00960-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/05/2023] [Indexed: 11/24/2023] Open
Abstract
Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5-24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875-0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093-0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.
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Affiliation(s)
- Hyeonhoon Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Data Science Research, Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun-Lim Yang
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Medical Device Development Support, Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Ho Geol Ryu
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Youn Joung Cho
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Soo Bin Yoon
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun-Kyu Yoon
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyung-Chul Lee
- Department of Data Science Research, Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
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Marijon E, Narayanan K, Smith K, Barra S, Basso C, Blom MT, Crotti L, D'Avila A, Deo R, Dumas F, Dzudie A, Farrugia A, Greeley K, Hindricks G, Hua W, Ingles J, Iwami T, Junttila J, Koster RW, Le Polain De Waroux JB, Olasveengen TM, Ong MEH, Papadakis M, Sasson C, Shin SD, Tse HF, Tseng Z, Van Der Werf C, Folke F, Albert CM, Winkel BG. The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action. Lancet 2023; 402:883-936. [PMID: 37647926 DOI: 10.1016/s0140-6736(23)00875-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 04/13/2023] [Accepted: 04/25/2023] [Indexed: 09/01/2023]
Abstract
Despite major advancements in cardiovascular medicine, sudden cardiac death (SCD) continues to be an enormous medical and societal challenge, claiming millions of lives every year. Efforts to prevent SCD are hampered by imperfect risk prediction and inadequate solutions to specifically address arrhythmogenesis. Although resuscitation strategies have witnessed substantial evolution, there is a need to strengthen the organisation of community interventions and emergency medical systems across varied locations and health-care structures. With all the technological and medical advances of the 21st century, the fact that survival from sudden cardiac arrest (SCA) remains lower than 10% in most parts of the world is unacceptable. Recognising this urgent need, the Lancet Commission on SCD was constituted, bringing together 30 international experts in varied disciplines. Consistent progress in tackling SCD will require a completely revamped approach to SCD prevention, with wide-sweeping policy changes that will empower the development of both governmental and community-based programmes to maximise survival from SCA, and to comprehensively attend to survivors and decedents' families after the event. International collaborative efforts that maximally leverage and connect the expertise of various research organisations will need to be prioritised to properly address identified gaps. The Commission places substantial emphasis on the need to develop a multidisciplinary strategy that encompasses all aspects of SCD prevention and treatment. The Commission provides a critical assessment of the current scientific efforts in the field, and puts forth key recommendations to challenge, activate, and intensify efforts by both the scientific and global community with new directions, research, and innovation to reduce the burden of SCD worldwide.
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Affiliation(s)
- Eloi Marijon
- Division of Cardiology, European Georges Pompidou Hospital, AP-HP, Paris, France; Université Paris Cité, Inserm, PARCC, Paris, France; Paris-Sudden Death Expertise Center (Paris-SDEC), Paris, France.
| | - Kumar Narayanan
- Université Paris Cité, Inserm, PARCC, Paris, France; Paris-Sudden Death Expertise Center (Paris-SDEC), Paris, France; Medicover Hospitals, Hyderabad, India
| | - Karen Smith
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Silverchain Group, Melbourne, VIC, Australia
| | - Sérgio Barra
- Department of Cardiology, Hospital da Luz Arrábida, Vila Nova de Gaia, Portugal
| | - Cristina Basso
- Cardiovascular Pathology Unit-Azienda Ospedaliera and Department of Cardiac Thoracic and Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | - Marieke T Blom
- Department of General Practice, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lia Crotti
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy; Istituto Auxologico Italiano, IRCCS, Center for Cardiac Arrhythmias of Genetic Origin, Cardiomyopathy Unit and Laboratory of Cardiovascular Genetics, Department of Cardiology, Milan, Italy
| | - Andre D'Avila
- Department of Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Hospital SOS Cardio, Santa Catarina, Brazil
| | - Rajat Deo
- Department of Cardiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Florence Dumas
- Université Paris Cité, Inserm, PARCC, Paris, France; Paris-Sudden Death Expertise Center (Paris-SDEC), Paris, France; Emergency Department, Cochin Hospital, Paris, France
| | - Anastase Dzudie
- Cardiology and Cardiac Arrhythmia Unit, Department of Internal Medicine, DoualaGeneral Hospital, Douala, Cameroon; Yaounde Faculty of Medicine and Biomedical Sciences, University of Yaounde 1, Yaounde, Cameroon
| | - Audrey Farrugia
- Hôpitaux Universitaires de Strasbourg, France, Strasbourg, France
| | - Kaitlyn Greeley
- Division of Cardiology, European Georges Pompidou Hospital, AP-HP, Paris, France; Université Paris Cité, Inserm, PARCC, Paris, France; Paris-Sudden Death Expertise Center (Paris-SDEC), Paris, France
| | | | - Wei Hua
- Cardiac Arrhythmia Center, FuWai Hospital, Beijing, China
| | - Jodie Ingles
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia
| | - Taku Iwami
- Kyoto University Health Service, Kyoto, Japan
| | - Juhani Junttila
- MRC Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Rudolph W Koster
- Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | | | - Theresa M Olasveengen
- Department of Anesthesia and Intensive Care Medicine, Oslo University Hospital and Institute of Clinical Medicine, Oslo, Norway
| | - Marcus E H Ong
- Singapore General Hospital, Duke-NUS Medical School, Singapore
| | - Michael Papadakis
- Cardiovascular Clinical Academic Group, St George's University of London, London, UK
| | | | - Sang Do Shin
- Department of Emergency Medicine at the Seoul National University College of Medicine, Seoul, South Korea
| | - Hung-Fat Tse
- University of Hong Kong, School of Clinical Medicine, Queen Mary Hospital, Hong Kong Special Administrative Region, China; Cardiac and Vascular Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Zian Tseng
- Division of Cardiology, UCSF Health, University of California, San Francisco Medical Center, San Francisco, California
| | - Christian Van Der Werf
- University of Amsterdam, Heart Center, Amsterdam, Netherlands; Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Fredrik Folke
- Department of Cardiology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christine M Albert
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Bo Gregers Winkel
- Department of Cardiology, University Hospital Copenhagen, Rigshospitalet, Copenhagen, Denmark
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Okada Y, Mertens M, Liu N, Lam SSW, Ong MEH. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resusc Plus 2023; 15:100435. [PMID: 37547540 PMCID: PMC10400904 DOI: 10.1016/j.resplu.2023.100435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] Open
Abstract
Aim Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field. Main text We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models. Conclusion In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.
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Affiliation(s)
- Yohei Okada
- Duke-NUS Medical School, National University of Singapore, Singapore
- Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mayli Mertens
- Antwerp Center for Responsible AI, Antwerp University, Belgium
- Centre for Ethics, Department of Philosophy, Antwerp University, Belgium
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Sean Shao Wei Lam
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital
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8
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Kolk MZH, Ruipérez-Campillo S, Deb B, Bekkers EJ, Allaart CP, Rogers AJ, Van Der Lingen ALCJ, Alvarez Florez L, Isgum I, De Vos BD, Clopton P, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit. Europace 2023; 25:euad271. [PMID: 37712675 PMCID: PMC10516624 DOI: 10.1093/europace/euad271] [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: 06/02/2023] [Accepted: 08/07/2023] [Indexed: 09/16/2023] Open
Abstract
AIMS Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features. METHODS AND RESULTS A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84). CONCLUSIONS ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.
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Affiliation(s)
- Maarten Z H Kolk
- Department of Cardiology, Heart Center, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA
- Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Zurich, Switzerland
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Albert J Rogers
- Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA
| | - Anne-Lotte C J Van Der Lingen
- Department of Cardiology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Laura Alvarez Florez
- Department of Cardiology, Heart Center, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Ivana Isgum
- Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Bob D De Vos
- Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA
| | - Arthur A M Wilde
- Department of Cardiology, Heart Center, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Reinoud E Knops
- Department of Cardiology, Heart Center, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA
| | - Fleur V Y Tjong
- Department of Cardiology, Heart Center, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Department of Medicine and Cardiovascular Institute, Stanford University, 780 Welch Road, MC 5773, Stanford, CA 94305, USA
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Golcuk Y, Kaymak Golcuk B. Limitations and Future Directions for Risk Assessment of Ventricular Arrhythmia in Acute Myocardial Infarction [Letter]. Clin Interv Aging 2023; 18:557-558. [PMID: 37035452 PMCID: PMC10075258 DOI: 10.2147/cia.s411242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 03/30/2023] [Indexed: 04/04/2023] Open
Affiliation(s)
- Yalcin Golcuk
- Muğla Sıtkı Koçman University, Faculty of Medicine, Department of Emergency Medicine, Muğla, Turkey
- Correspondence: Yalcin Golcuk, Muğla Sıtkı Koçman University, Faculty of Medicine, Department of Emergency Medicine, Muğla, Turkey, Tel +90 252 214 13 23, Fax +90 252 212 35 99, Email ;
| | - Burcu Kaymak Golcuk
- Muğla Training and Research Hospital, Clinical Biochemistry Service, Muğla, Turkey
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