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Lüscher TF, Wenzl FA, D'Ascenzo F, Friedman PA, Antoniades C. Artificial intelligence in cardiovascular medicine: clinical applications. Eur Heart J 2024:ehae465. [PMID: 39158472 DOI: 10.1093/eurheartj/ehae465] [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: 03/18/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 08/20/2024] Open
Abstract
Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.
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Affiliation(s)
- Thomas F Lüscher
- Royal Brompton and Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College London, UK
- Cardiovascular Academic Group, King's College, London, UK
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
| | - Florian A Wenzl
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
- National Disease Registration and Analysis Service, NHS, London, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic and Mayo Foundation, Rochester, MN, USA
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
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la Roi-Teeuw HM, van Royen FS, de Hond A, Zahra A, de Vries S, Bartels R, Carriero AJ, van Doorn S, Dunias ZS, Kant I, Leeuwenberg T, Peters R, Veerhoek L, van Smeden M, Luijken K. Don't be misled: 3 misconceptions about external validation of clinical prediction models. J Clin Epidemiol 2024; 172:111387. [PMID: 38729274 DOI: 10.1016/j.jclinepi.2024.111387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/24/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.
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Affiliation(s)
- Hannah M la Roi-Teeuw
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.
| | - Florien S van Royen
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Anne de Hond
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Anum Zahra
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Sjoerd de Vries
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands
| | - Richard Bartels
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Alex J Carriero
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Sander van Doorn
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Zoë S Dunias
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Ilse Kant
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Tuur Leeuwenberg
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Ruben Peters
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Laura Veerhoek
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Maarten van Smeden
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Kim Luijken
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
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Nakajima K, Nakata T, Doi T, Verschure DO, Frantellizzi V, De Feo MS, Tada H, Verberne HJ. Cardiac sympathetic activity and lethal arrhythmic events: insight into bell-shaped relationship between 123I-meta-iodobenzylguanidine activity and event rates. EJNMMI Res 2024; 14:67. [PMID: 39033243 PMCID: PMC11264658 DOI: 10.1186/s13550-024-01131-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/12/2024] [Indexed: 07/23/2024] Open
Abstract
BACKGROUND 123I-meta-iodobenzylguanidine (mIBG) has been applied to patients with chronic heart failure (CHF). However, the relationship between 123I-mIBG activity and lethal arrhythmic events (ArE) is not well defined. This study aimed to determine this relationship in Japanese and European cohorts. RESULTS We calculated heart-to-mediastinum (H/M) count ratios and washout rates (WRs) of 827 patients using planar 123I-mIBG imaging. We defined ArEs as sudden cardiac death, arrhythmic death, and potentially lethal events such as sustained ventricular tachycardia, cardiac arrest with resuscitation, and appropriate implantable cardioverter defibrillator (ICD) discharge, either from a single ICD or as part of a cardiac resynchronization therapy device (CRTD). We analyzed the incidence of ArE with respect to H/M ratios, WRs and New York Heart Association (NYHA) functional classes among Japanese (J; n = 581) and European (E; n = 246) cohorts. We also simulated ArE rates versus H/M ratios under specific conditions using a machine-learning model incorporating 13 clinical variables. Consecutive patients with CHF were selected in group J, whereas group E comprised candidates for cardiac electronic devices. Groups J and E mostly comprised patients with NYHA functional classes I/II (95%) and II/III (91%), respectively, and 21% and 72% were respectively implanted with ICD/CRTD devices. The ArE rate increased with lower H/M ratios in group J, but the relationship was bell-shaped, with a high ArE rate within the intermediate H/M range, in group E. This bell-shaped curve was also evident in patients with NYHA classes II/III in the combined J and E groups, particularly in those with a high (> 15%) mIBG WR and with ischemic, but not in those with non-ischemic etiologies. Machine learning-based prediction of ArE risk aligned with these findings, indicating a bell-shaped curve in NYHA class II/III but not in class I. CONCLUSIONS The relationship between cardiac 123I-mIBG activity and lethal arrhythmic events is influenced by the background of patients. The bell-shaped relationship in NYHA classes II/III, high WR, and ischemic etiology likely aids in identifying patients at high risk for ArEs.
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Affiliation(s)
- Kenichi Nakajima
- Department of Nuclear Medicine/Functional imaging and Artificial Intelligence, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan.
| | - Tomoaki Nakata
- Department of Cardiology, Hakodate-Goryoukaku Hospital, Hakodate, Japan
| | - Takahiro Doi
- Department of Cardiology, Teine Keijinkai Hospital, Sapporo, Japan
| | - Derk O Verschure
- Department of Cardiology, Zaans Medical Center, Zaandam, The Netherlands
| | - Viviana Frantellizzi
- Department of Radiological Sciences, Oncology and Anatomo-Pathology, Sapienza - University of Rome, Rome, Italy
| | - Maria Silvia De Feo
- Department of Radiological Sciences, Oncology and Anatomo-Pathology, Sapienza - University of Rome, Rome, Italy
| | - Hayato Tada
- Department of Cardiology, Kanazawa University, Kanazawa, Japan
| | - Hein J Verberne
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location AMC, University of Amsterdam, Amsterdam, The Netherlands
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Jain SS, Elias P, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2487-2496. [PMID: 38593945 DOI: 10.1016/j.jacc.2024.03.401] [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] [Received: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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Jia Y, Cui N, Jia T, Song J. Prognostic models for patients suffering a heart failure with a preserved ejection fraction: a systematic review. ESC Heart Fail 2024; 11:1341-1351. [PMID: 38318693 PMCID: PMC11098651 DOI: 10.1002/ehf2.14696] [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: 08/27/2023] [Revised: 01/02/2024] [Accepted: 01/09/2024] [Indexed: 02/07/2024] Open
Abstract
The purpose of this study was to systematically review the development, performance, and applicability of prognostic models developed for predicting poor events in patients with heart failure with preserved ejection fraction (HFpEF). Databases including Embase, PubMed, Web of Science Core Collection, the Cochrane Library, China National Knowledge Infrastructure, Wan Fang, Wei Pu, and China Biological Medicine were queried from their respective dates of inception to 1 June 2023, to examine multivariate models for prognostic prediction in HFpEF. Both forward and backward citations of all studies were included in our analysis. Two researchers individually used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and assess the quality of the models using the Predictive Mode Bias Risk Assessment Tool (PROBAST). Among the 6897 studies screened, 16 studies derived and/or validated a total of 39 prognostic models. The sample size ranges for model development, internal validation, and external validation are 119 to 5988, 152 to 1000, and 30 to 5957, respectively. The most frequently employed modelling technique was Cox proportional hazards regression. Six studies (37.50%) conducted internal validation of models; bootstrap and k-fold cross-validation were the commonly used methods for internal validation of models. Ten of these models (25.64%) were validated externally, with reported the c-statistic in the external validation set ranging from 0.70 to 0.96, while the remaining models await external validation. The MEDIA echo score and I-PRESERVE-sudden cardiac death prediction mode have been externally validated using multiple cohorts, and the results consistently show good predictive performance. The most frequently used predictors identified among the models were age, n-terminal pro-brain natriuretic peptide, ejection fraction, albumin, and hospital stay in the last 5 months owing to heart failure. All study predictor domains and outcome domains were at low risk of bias, high or unclear risk of bias of all prognostic models due to underreporting in the area of analysis. All studies did not evaluate the clinical utility of the prognostic models. Predictive models for predicting prognostic outcomes in patients with HFpEF showed good discriminatory ability but their utility and generalization remain uncertain due to the risk of bias, differences in predictors between models, and the lack of clinical application studies. Future studies should improve the methodological quality of model development and conduct external validation of models.
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Affiliation(s)
- Ying‐Ying Jia
- Department of NursingThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
- Department of NursingZhejiang University School of MedicineHangzhouChina
| | - Nian‐Qi Cui
- School of NursingKunming Medical UniversityKunmingChina
| | - Ting‐Ting Jia
- Department of General SurgeryGansu Provincial People's Hospital, Cadre WardLanzhouChina
| | - Jian‐Ping Song
- Department of NursingThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
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Lehmann DH, Gomes B, Vetter N, Braun O, Amr A, Hilbel T, Müller J, Köthe U, Reich C, Kayvanpour E, Sedaghat-Hamedani F, Meder M, Haas J, Ashley E, Rottbauer W, Felbel D, Bekeredjian R, Mahrholdt H, Keller A, Ong P, Seitz A, Hund H, Geis N, André F, Engelhardt S, Katus HA, Frey N, Heuveline V, Meder B. Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data. Lancet Digit Health 2024; 6:e407-e417. [PMID: 38789141 DOI: 10.1016/s2589-7500(24)00063-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND With increasing numbers of patients and novel drugs for distinct causes of systolic and diastolic heart failure, automated assessment of cardiac function is important. We aimed to provide a non-invasive method to predict diagnosis of patients undergoing cardiac MRI (cMRI) and to obtain left ventricular end-diastolic pressure (LVEDP). METHODS For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital's AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done. FINDINGS 66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77-0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77-7·95), with a Pearson correlation of 0·57 (95% CI 0·56-0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79-0·84) for ischaemic cardiomyopathy and 0·92 (0·91-0·94) for hypertrophic cardiomyopathy. INTERPRETATION Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function. FUNDING Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCardiology section of the German Cardiac Society, and AI Health Innovation Cluster Heidelberg.
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Affiliation(s)
- David Hermann Lehmann
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Bruna Gomes
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; Department of Medicine, Department of Genetics, and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Niklas Vetter
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Olivia Braun
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Ali Amr
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Thomas Hilbel
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Jens Müller
- Computer Vision and Learning Lab, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Ulrich Köthe
- Computer Vision and Learning Lab, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Christoph Reich
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Elham Kayvanpour
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Farbod Sedaghat-Hamedani
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Manuela Meder
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Jan Haas
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Euan Ashley
- Department of Medicine, Department of Genetics, and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Dominik Felbel
- Department of Cardiology, Ulm University Heart Center, Ulm, Germany
| | - Raffi Bekeredjian
- Clinic for Cardiology and Angiology, Robert-Bosch Krankenhaus, Stuttgart, Germany
| | - Heiko Mahrholdt
- Clinic for Cardiology and Angiology, Robert-Bosch Krankenhaus, Stuttgart, Germany
| | - Andreas Keller
- Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Peter Ong
- Clinic for Cardiology and Angiology, Robert-Bosch Krankenhaus, Stuttgart, Germany
| | - Andreas Seitz
- Clinic for Cardiology and Angiology, Robert-Bosch Krankenhaus, Stuttgart, Germany
| | - Hauke Hund
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Nicolas Geis
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Florian André
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Sandy Engelhardt
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Hugo A Katus
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; eCardiology, German Cardiac Society, Düsseldorf, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Norbert Frey
- Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; eCardiology, German Cardiac Society, Düsseldorf, Germany; German Center for Cardiovascular Research, Berlin, Germany
| | - Vincent Heuveline
- Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
| | - Benjamin Meder
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany; eCardiology, German Cardiac Society, Düsseldorf, Germany; German Center for Cardiovascular Research, Berlin, Germany.
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7
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Hautala AJ, Shavazipour B, Afsar B, Tulppo MP, Miettinen K. Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation. Front Public Health 2024; 12:1378349. [PMID: 38864016 PMCID: PMC11165052 DOI: 10.3389/fpubh.2024.1378349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/08/2024] [Indexed: 06/13/2024] Open
Abstract
Introduction Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD. Methods The data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total). Results The ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = - 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811). Discussion Presence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.
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Affiliation(s)
- Arto J. Hautala
- Faculty of Sports and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | | | - Bekir Afsar
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Mikko P. Tulppo
- Research Unit of Biomedicine and Internal Medicine, Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Kaisa Miettinen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
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8
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Jacquemyn X, Van Onsem E, Dufendach K, Brown JA, Kliner D, Toma C, Serna-Gallegos D, Sá MP, Sultan I. Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00448-3. [PMID: 38815806 DOI: 10.1016/j.jtcvs.2024.05.017] [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: 04/30/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
OBJECTIVES With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes after transcatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI. Key objectives consisted in summarizing model performance, evaluating adherence to reporting guidelines, and transparency. METHODS We searched PubMed, SCOPUS, and Embase through August 2023. We selected published machine learning models predicting TAVI outcomes. Two reviewers independently screened articles, extracted data, and assessed the study quality according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Outcomes included summary C-statistics and model risk of bias assessed with the Prediction Model Risk of Bias Assessment Tool. C-statistics were pooled using a random-effects model. RESULTS Twenty-one studies (118,153 patients) employing various ML algorithms (76 models) were included in the systematic review. Predictive ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60-0.70), 31.6% acceptable (C-statistic 0.70-0.80), and 30.3% demonstrated excellent (C-statistic >0.80) performance. Meta-analyses revealed excellent predictive performance for early mortality (C-statistic: 0.81; 95% confidence interval [CI], 0.65-0.91), acceptable performance for 1-year mortality (C-statistic: 0.76; 95% CI, 0.67-0.84), and acceptable performance for predicting permanent pacemaker implantation (C-statistic: 0.75; 95% CI, 0.51-0.90). CONCLUSIONS ML models for TAVI outcomes exhibit adequate-to-excellent performance, suggesting potential clinical utility. We identified concerns in methodology and transparency, emphasizing the need for improved scientific reporting standards.
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Affiliation(s)
- Xander Jacquemyn
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
| | | | - Keith Dufendach
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - James A Brown
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Dustin Kliner
- UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa; Department of Interventional Cardiology, University of Pittsburgh, Pittsburgh, Pa
| | - Catalin Toma
- UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa; Department of Interventional Cardiology, University of Pittsburgh, Pittsburgh, Pa
| | - Derek Serna-Gallegos
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Michel Pompeu Sá
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Ibrahim Sultan
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
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9
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Patel SJ, Yousuf S, Padala JV, Reddy S, Saraf P, Nooh A, Fernandez Gutierrez LMA, Abdirahman AH, Tanveer R, Rai M. Advancements in Artificial Intelligence for Precision Diagnosis and Treatment of Myocardial Infarction: A Comprehensive Review of Clinical Trials and Randomized Controlled Trials. Cureus 2024; 16:e60119. [PMID: 38864061 PMCID: PMC11164835 DOI: 10.7759/cureus.60119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2024] [Indexed: 06/13/2024] Open
Abstract
Coronary artery disease (CAD) is still a serious global health issue that has a substantial impact on death and illness rates. The goal of primary prevention strategies is to lower the risk of developing CAD. Nevertheless, current methods usually rely on simple risk assessment instruments that might overlook significant individual risk factors. This limitation highlights the need for innovative methods that can accurately assess cardiovascular risk and offer personalized preventive care. Recent advances in machine learning and artificial intelligence (AI) have opened up interesting new avenues for optimizing primary preventive efforts for CAD and improving risk prediction models. By leveraging large-scale databases and advanced computational techniques, AI has the potential to fundamentally alter how cardiovascular risk is evaluated and managed. This review looks at current randomized controlled studies and clinical trials that explore the application of AI and machine learning to improve primary preventive measures for CAD. The emphasis is on their ability to recognize and include a range of risk elements in sophisticated risk assessment models.
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Affiliation(s)
- Syed J Patel
- Internal Medicine, S Nijalingappa Medical College and Hanagal Sri Kumareshwar Hospital and Research Centre, Bagalkot, IND
| | - Salma Yousuf
- Public Health, Jinnah Sindh Medical University, Karachi, PAK
| | | | - Shruta Reddy
- Internal Medicine, Sri Venkata Sai Medical College and Hospital, Mahbubnagar, IND
| | - Pranav Saraf
- Internal Medicine, Sri Ramaswamy Memorial Medical College and Hospital, Kattankulathur, IND
| | - Alaa Nooh
- Internal Medicine, China Medical University, Shenyang, CHN
| | | | | | - Rameen Tanveer
- Internal Medicine, Lakehead University, Thunder Bay, CAN
| | - Manju Rai
- Biotechnology, Shri Venkateshwara University, Gajraula, IND
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10
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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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11
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [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] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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12
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Moreno-Sánchez PA, García-Isla G, Corino VDA, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput Biol Med 2024; 172:108235. [PMID: 38460311 DOI: 10.1016/j.compbiomed.2024.108235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/11/2024]
Abstract
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.
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Affiliation(s)
| | - Guadalupe García-Isla
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Valentina D A Corino
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Luca Mainardi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
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13
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Gupta U, Paluru N, Nankani D, Kulkarni K, Awasthi N. A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. Heliyon 2024; 10:e26787. [PMID: 38562492 PMCID: PMC10982903 DOI: 10.1016/j.heliyon.2024.e26787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.
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Affiliation(s)
- Utkarsh Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Naveen Paluru
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Deepankar Nankani
- Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati, Assam, 781039, India
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Pessac, Bordeaux, F-33000, France
- University of Bordeaux, INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, F-33000, France
| | - Navchetan Awasthi
- Faculty of Science, Mathematics and Computer Science, Informatics Institute, University of Amsterdam, Amsterdam, 1090 GH, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, 1081 HV, the Netherlands
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14
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Corianò M, Lanera C, De Michieli L, Perazzolo Marra M, Iliceto S, Gregori D, Tona F. Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study. PLoS One 2024; 19:e0297793. [PMID: 38421987 PMCID: PMC10903812 DOI: 10.1371/journal.pone.0297793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/12/2024] [Indexed: 03/02/2024] Open
Abstract
Prediction of major arrhythmic events (MAEs) in dilated cardiomyopathy represents an unmet clinical goal. Computational models and artificial intelligence (AI) are new technological tools that could offer a significant improvement in our ability to predict MAEs. In this proof-of-concept study, we propose a deep learning (DL)-based model, which we termed Deep ARrhythmic Prevention in dilated cardiomyopathy (DARP-D), built using multidimensional cardiac magnetic resonance data (cine videos and hypervideos and LGE images and hyperimages) and clinical covariates, aimed at predicting and tracking an individual patient's risk curve of MAEs (including sudden cardiac death, cardiac arrest due to ventricular fibrillation, sustained ventricular tachycardia lasting ≥30 s or causing haemodynamic collapse in <30 s, appropriate implantable cardiac defibrillator intervention) over time. The model was trained and validated in 70% of a sample of 154 patients with dilated cardiomyopathy and tested in the remaining 30%. DARP-D achieved a 95% CI in Harrell's C concordance indices of 0.12-0.68 on the test set. We demonstrate that our DL approach is feasible and represents a novelty in the field of arrhythmic risk prediction in dilated cardiomyopathy, able to analyze cardiac motion, tissue characteristics, and baseline covariates to predict an individual patient's risk curve of major arrhythmic events. However, the low number of patients, MAEs and epoch of training make the model a promising prototype but not ready for clinical usage. Further research is needed to improve, stabilize and validate the performance of the DARP-D to convert it from an AI experiment to a daily used tool.
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Affiliation(s)
- Mattia Corianò
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padova, Italy
| | - Corrado Lanera
- Department of Cardiac Thoracic Vascular Sciences and Public Health, UBEP, Padova, Italy
| | - Laura De Michieli
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padova, Italy
| | | | - Sabino Iliceto
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padova, Italy
| | - Dario Gregori
- Department of Cardiac Thoracic Vascular Sciences and Public Health, UBEP, Padova, Italy
| | - Francesco Tona
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padova, Italy
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15
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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Pieszko K, Hiczkiewicz J, Łojewska K, Uziębło-Życzkowska B, Krzesiński P, Gawałko M, Budnik M, Starzyk K, Wożakowska-Kapłon B, Daniłowicz-Szymanowicz L, Kaufmann D, Wójcik M, Błaszczyk R, Mizia-Stec K, Wybraniec M, Kosmalska K, Fijałkowski M, Szymańska A, Dłużniewski M, Kucio M, Haberka M, Kupczyńska K, Michalski B, Tomaszuk-Kazberuk A, Wilk-Śledziewska K, Wachnicka-Truty R, Koziński M, Kwieciński J, Wolny R, Kowalik E, Kolasa I, Jurek A, Budzianowski J, Burchardt P, Kapłon-Cieślicka A, Slomka PJ. Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry. Eur Heart J 2024; 45:32-41. [PMID: 37453044 PMCID: PMC10757867 DOI: 10.1093/eurheartj/ehad431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 05/03/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
AIMS Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features. METHODS AND RESULTS Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score. A decision threshold allowing for a 99% negative predictive value was defined in the development cohort. A protocol where TOE in patients on chronic OAC is performed depending on the LAT-AI score was validated in the external cohort. In the external testing cohort, LAT was found in 5.5% of patients. LAT-AI achieved an AUC of 0.85 [95% confidence interval (CI): 0.82-0.89], outperforming LVEF (0.81, 95% CI 0.76-0.86, P < .0001) and CHA2DS2-VASc score (0.69, 95% CI: 0.63-0.7, P < .0001) in the entire external cohort. Based on the proposed protocol, 40% of patients on chronic OAC from the external cohort would safely avoid TOE. CONCLUSION LAT-AI allows accurate prediction of LAT. A LAT-AI-based protocol could be used to guide the decision to perform TOE despite chronic OAC.
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Affiliation(s)
- Konrad Pieszko
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
- WSSP ZOZ Nowa Sol, Nowa Sol, Poland
| | - Jarosław Hiczkiewicz
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
- WSSP ZOZ Nowa Sol, Nowa Sol, Poland
| | | | - Beata Uziębło-Życzkowska
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Paweł Krzesiński
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Monika Gawałko
- ‘Club 30’, Polish Cardiac Society, Poland
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Institute of Pharmacology, West German Heart and Vascular Centre, University Duisburg-Essen, Essen, Germany
| | - Monika Budnik
- ‘Club 30’, Polish Cardiac Society, Poland
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Katarzyna Starzyk
- 1st Clinic of Cardiology and Electrotherapy, Swietokrzyskie Cardiology Centre, Kielce, Poland
| | - Beata Wożakowska-Kapłon
- 1st Clinic of Cardiology and Electrotherapy, Swietokrzyskie Cardiology Centre, Kielce, Poland
| | | | - Damian Kaufmann
- Department of Cardiology and Electrotherapy, Medical University of Gdansk, Gdansk, Poland
| | - Maciej Wójcik
- Department of Cardiology, Medical University of Lublin, Lublin, Poland
| | - Robert Błaszczyk
- Department of Cardiology, Medical University of Lublin, Lublin, Poland
| | - Katarzyna Mizia-Stec
- 1st Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, Katowice, Poland
| | - Maciej Wybraniec
- 1st Department of Cardiology, School of Medicine in Katowice, Medical University of Silesia, Katowice, Poland
| | | | | | - Anna Szymańska
- Department of Heart Diseases, Postgraduate Medical School, Warsaw, Poland
| | | | - Michał Kucio
- Department of Cardiology, School of Health Sciences, Medical University of Silesia, Katowice, Poland
| | - Maciej Haberka
- Department of Cardiology, School of Health Sciences, Medical University of Silesia, Katowice, Poland
| | | | - Błażej Michalski
- Department of Cardiology, Medical University of Lodz, Lodz, Poland
| | | | | | - Renata Wachnicka-Truty
- Department of Cardiology and Internal Medicine, Medical University of Gdansk, Gdynia, Poland
| | - Marek Koziński
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Medicine, Medical University of Gdansk, Gdynia, Poland
| | - Jacek Kwieciński
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Rafał Wolny
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Ewa Kowalik
- Department of Congenital Heart Diseases, National Institute of Cardiology, Warsaw, Poland
| | - Iga Kolasa
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
| | - Agnieszka Jurek
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Cardiology and Internal Diseases, Military Institute of Medicine, Warsaw, Poland
| | - Jan Budzianowski
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Collegium Medicum, Zielona Gora, Poland
- WSSP ZOZ Nowa Sol, Nowa Sol, Poland
| | - Paweł Burchardt
- ‘Club 30’, Polish Cardiac Society, Poland
- Department of Biology and Lipid Disorders, Poznan University of Medical Sciences, Poznan, Poland
| | - Agnieszka Kapłon-Cieślicka
- ‘Club 30’, Polish Cardiac Society, Poland
- First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Piotr J Slomka
- Department of Medicine (Division of Artificial Intelligence in Medicine), Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, 90048, Los Angeles, CA, USA
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Xie J, Zhong W, Yang R, Wang L, Zhen X. Discriminative fusion of moments-aligned latent representation of multimodality medical data. Phys Med Biol 2023; 69:015015. [PMID: 38052076 DOI: 10.1088/1361-6560/ad1271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/05/2023] [Indexed: 12/07/2023]
Abstract
Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling.
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Affiliation(s)
- Jincheng Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China
| | - Weixiong Zhong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Linjing Wang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong 510095, People's Republic of China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China
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19
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Affiliation(s)
- Filippo Crea
- Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
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20
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van Royen FS, Asselbergs FW, Alfonso F, Vardas P, van Smeden M. Five critical quality criteria for artificial intelligence-based prediction models. Eur Heart J 2023; 44:4831-4834. [PMID: 37897346 DOI: 10.1093/eurheartj/ehad727] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/30/2023] Open
Abstract
To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevancy, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, large enough sample size, and openness of code and software.
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Affiliation(s)
- Florien S van Royen
- Department of General Practice & Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Fernando Alfonso
- Department of Cardiology, Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, IIS-IP. CIVER-CV, Madrid, Spain
| | - Panos Vardas
- Biomedical Research Foundation Academy of Athens (BRFAA) and Hygeia Hospitals Group, Athens, Greece
| | - Maarten van Smeden
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG Utrecht, Netherlands
- Department of Data Science & Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands
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21
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de Hond A, Huisman M, Van Smeden M. Why the grass isn't always greener on the machine learning side. Eur J Intern Med 2023; 118:36-37. [PMID: 37879970 DOI: 10.1016/j.ejim.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/03/2023] [Indexed: 10/27/2023]
Affiliation(s)
- Anne de Hond
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno Huisman
- Department of Thrombosis and Hemostasis Leiden University Medical Center Leiden Netherlands
| | - Maarten Van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
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22
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Ali MM, Gandhi S, Sulaiman S, Jafri SH, Ali AS. Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices. J Pers Med 2023; 13:1625. [PMID: 38138852 PMCID: PMC10744376 DOI: 10.3390/jpm13121625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/24/2023] Open
Abstract
Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.
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Affiliation(s)
- Mohammed M. Ali
- Multidisciplinary Studies Programs, Eberly College of Arts and Sciences, West Virginia University, Morgantown, WV 26506, USA;
| | - Subi Gandhi
- Department of Medical Lab Sciences, Public Health and Nutrition Science, Tarleton State University, 1333 West Washington, Stephenville, TX 76402, USA;
| | - Samian Sulaiman
- Department of Cardiology, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV 26501, USA;
| | - Syed H. Jafri
- Department of Accounting, Finance and Economics, Tarleton State University, 1333 West Washington, Stephenville, TX 76402, USA;
| | - Abbas S. Ali
- Department of Cardiology, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV 26501, USA;
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23
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Neumann JT, Twerenbold R, Ojeda F, Aldous SJ, Allen BR, Apple FS, Babel H, Christenson RH, Cullen L, Di Carluccio E, Doudesis D, Ekelund U, Giannitsis E, Greenslade J, Inoue K, Jernberg T, Kavsak P, Keller T, Lee KK, Lindahl B, Lorenz T, Mahler SA, Mills NL, Mokhtari A, Parsonage W, Pickering JW, Pemberton CJ, Reich C, Richards AM, Sandoval Y, Than MP, Toprak B, Troughton RW, Worster A, Zeller T, Ziegler A, Blankenberg S. Personalized diagnosis in suspected myocardial infarction. Clin Res Cardiol 2023; 112:1288-1301. [PMID: 37131096 PMCID: PMC10449973 DOI: 10.1007/s00392-023-02206-3] [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] [Received: 03/15/2023] [Accepted: 04/11/2023] [Indexed: 05/04/2023]
Abstract
BACKGROUND In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. RESULTS Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. CONCLUSION We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS gov ; NCT01852123), LUND ( www. CLINICALTRIALS gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS gov ; NCT03111862), ROMI ( www. CLINICALTRIALS gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS gov ; NCT02060760).
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Affiliation(s)
- Johannes Tobias Neumann
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner SiteHamburg/Kiel/Lübeck, Hamburg, Germany
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Raphael Twerenbold
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner SiteHamburg/Kiel/Lübeck, Hamburg, Germany
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Francisco Ojeda
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sally J Aldous
- Department of Cardiology, Christchurch Hospital, Christchurch, New Zealand
| | - Brandon R Allen
- Department of Emergency Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Fred S Apple
- Departments of Laboratory Medicine and Pathology, Hennepin Healthcare/HCMC and University of Minnesota, Minneapolis, MN, USA
| | - Hugo Babel
- Cardio-CARE, Medizincampus Davos, Davos, Switzerland
| | - Robert H Christenson
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Louise Cullen
- Department of Emergency Medicine, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
| | | | - Dimitrios Doudesis
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Ulf Ekelund
- Department of Internal and Emergency Medicine, Lund University, Skåne University Hospital, Lund, Sweden
| | | | - Jaimi Greenslade
- Department of Emergency Medicine, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
| | - Kenji Inoue
- Juntendo University Nerima Hospital, Tokyo, Japan
| | - Tomas Jernberg
- Department of Clinical Sciences, Danderyd University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Peter Kavsak
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Till Keller
- Department of Cardiology, Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
| | - Kuan Ken Lee
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Bertil Lindahl
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Thiess Lorenz
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner SiteHamburg/Kiel/Lübeck, Hamburg, Germany
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon A Mahler
- Department of Emergency Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Nicholas L Mills
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Arash Mokhtari
- Department of Internal Medicine and Emergency Medicine and Department of Cardiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - William Parsonage
- Australian Centre for Health Service Innovation, Queensland University of Technology, Kelvin Grove, Australia
| | - John W Pickering
- Department of Medicine, Christchurch and Emergency Department, University of Otago, Christchurch Hospital, Christchurch, New Zealand
| | - Christopher J Pemberton
- Department of Medicine, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand
| | - Christoph Reich
- Department of Cardiology, Heidelberg University Hospital, Heidelberg, Germany
| | - A Mark Richards
- Department of Medicine, Christchurch and Emergency Department, University of Otago, Christchurch Hospital, Christchurch, New Zealand
| | - Yader Sandoval
- Minneapolis Heart Institute, Abbott Northwestern Hospital, and Minneapolis Heart Institute Foundation, Minneapolis, MN, USA
| | - Martin P Than
- Department of Medicine, Christchurch and Emergency Department, University of Otago, Christchurch Hospital, Christchurch, New Zealand
| | - Betül Toprak
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner SiteHamburg/Kiel/Lübeck, Hamburg, Germany
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Richard W Troughton
- Department of Medicine, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand
| | - Andrew Worster
- Division of Emergency Medicine, McMaster University, Hamilton, ON, Canada
| | - Tanja Zeller
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner SiteHamburg/Kiel/Lübeck, Hamburg, Germany
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas Ziegler
- Cardio-CARE, Medizincampus Davos, Davos, Switzerland
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Stefan Blankenberg
- Department of Cardiology, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
- German Center for Cardiovascular Research (DZHK), Partner SiteHamburg/Kiel/Lübeck, Hamburg, Germany.
- Population Health Research Department, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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Westphal P, Luo H, Shahmohammadi M, Prinzen FW, Delhaas T, Cornelussen RN. Machine learning-powered, device-embedded heart sound measurement can optimize AV delay in patients with CRT. Heart Rhythm 2023; 20:1316-1324. [PMID: 37247684 DOI: 10.1016/j.hrthm.2023.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/28/2023] [Accepted: 05/17/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Continuous optimization of atrioventricular (AV) delay for cardiac resynchronization therapy (CRT) is mainly performed by electrical means. OBJECTIVE The purpose of this study was to develop an estimation model of cardiac function that uses a piezoelectric microphone embedded in a pulse generator to guide CRT optimization. METHODS Electrocardiogram, left ventricular pressure (LVP), and heart sounds were simultaneously collected during CRT device implantation procedures. A piezoelectric alarm transducer embedded in a modified CRT device facilitated recording of heart sounds in patients undergoing a pacing protocol with different AV delays. Machine learning (ML) was used to produce a decision-tree ensemble model capable of estimating absolute maximal LVP (LVPmax) and maximal rise of LVP (LVdP/dtmax) using 3 heart sound-based features. To gauge the applicability of ML in AV delay optimization, polynomial curves were fitted to measured and estimated values. RESULTS In the data set of ∼30,000 heartbeats, ML indicated S1 amplitude, S2 amplitude, and S1 integral (S1 energy for LVdP/dtmax) as most prominent features for AV delay optimization. ML resulted in single-beat estimation precision for absolute values of LVPmax and LVdP/dtmax of 67% and 64%, respectively. For 20-30 beat averages, cross-correlation between measured and estimated LVPmax and LVdP/dtmax was 0.999 for both. The estimated optimal AV delays were not significantly different from those measured using invasive LVP (difference -5.6 ± 17.1 ms for LVPmax and +5.1 ± 6.7 ms for LVdP/dtmax). The difference in function at estimated and measured optimal AV delays was not statiscally significant (1 ± 3 mm Hg for LVPmax and 9 ± 57 mm Hg/s for LVdP/dtmax). CONCLUSION Heart sound sensors embedded in a CRT device, powered by a ML algorithm, provide a reliable assessment of optimal AV delays and absolute LVPmax and LVdP/dtmax.
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Affiliation(s)
- Philip Westphal
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands; Bakken Research Center, Medtronic, plc, Maastricht, The Netherlands
| | - Hongxing Luo
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Mehrdad Shahmohammadi
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Richard N Cornelussen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands; Bakken Research Center, Medtronic, plc, Maastricht, The Netherlands.
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25
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Klement W, El Emam K. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation. J Med Internet Res 2023; 25:e48763. [PMID: 37651179 PMCID: PMC10502599 DOI: 10.2196/48763] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/11/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements. OBJECTIVE We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies. METHODS We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items. RESULTS In total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting. CONCLUSIONS Our findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.
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Affiliation(s)
- William Klement
- University of Ottawa, Ottawa, ON, Canada
- CHEO Research Institute, Ottawa, ON, Canada
| | - Khaled El Emam
- University of Ottawa, Ottawa, ON, Canada
- CHEO Research Institute, Ottawa, ON, Canada
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26
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Toro-Tobon D, Loor-Torres R, Duran M, Fan JW, Singh Ospina N, Wu Y, Brito JP. Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions. Thyroid 2023; 33:903-917. [PMID: 37279303 PMCID: PMC10440669 DOI: 10.1089/thy.2023.0132] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.
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Affiliation(s)
- David Toro-Tobon
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Ricardo Loor-Torres
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mayra Duran
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jungwei W. Fan
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Naykky Singh Ospina
- Division of Endocrinology, Department of Medicine, University of Florida, Gainesville, Florida, USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Juan P. Brito
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Nadarajah R, Wu J, Hogg D, Raveendra K, Nakao YM, Nakao K, Arbel R, Haim M, Zahger D, Parry J, Bates C, Cowan C, Gale CP. Prediction of short-term atrial fibrillation risk using primary care electronic health records. Heart 2023; 109:1072-1079. [PMID: 36759177 PMCID: PMC10359547 DOI: 10.1136/heartjnl-2022-322076] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Abstract
OBJECTIVE Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)). METHODS We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C2HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. RESULTS Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA2DS2-VASc (0.784, 0.773 to 0.794) and C2HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). CONCLUSIONS FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.
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Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Jianhua Wu
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Dentistry, University of Leeds, Leeds, UK
| | - David Hogg
- School of Computing, University of Leeds, Leeds, UK
| | | | - Yoko M Nakao
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Kazuhiro Nakao
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Ronen Arbel
- Maximizing Health Outcomes Research Lab, Sapir College, Hof Ashkelon, Israel
- Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel
| | - Moti Haim
- Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Doron Zahger
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
- Cardiology, Soroka Medical Center, Beer Sheva, Israel
| | | | | | | | - Chris P Gale
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Cardiology, Leeds General Infirmary, Leeds, UK
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28
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West HW, Siddique M, Williams MC, Volpe L, Desai R, Lyasheva M, Thomas S, Dangas K, Kotanidis CP, Tomlins P, Mahon C, Kardos A, Adlam D, Graby J, Rodrigues JCL, Shirodaria C, Deanfield J, Mehta NN, Neubauer S, Channon KM, Desai MY, Nicol ED, Newby DE, Antoniades C. Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction. JACC Cardiovasc Imaging 2023; 16:800-816. [PMID: 36881425 PMCID: PMC10663979 DOI: 10.1016/j.jcmg.2022.11.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 11/09/2022] [Accepted: 11/17/2022] [Indexed: 02/11/2023]
Abstract
BACKGROUND Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented. OBJECTIVES This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care. METHODS The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value. RESULTS External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01). CONCLUSIONS Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.
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Affiliation(s)
- Henry W West
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Muhammad Siddique
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Caristo Diagnostics Pty Ltd, Oxford, United Kingdom
| | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Lucrezia Volpe
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ria Desai
- Northwestern University, Evanston, Illinois, USA
| | - Maria Lyasheva
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sheena Thomas
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Katerina Dangas
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Christos P Kotanidis
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Pete Tomlins
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Caristo Diagnostics Pty Ltd, Oxford, United Kingdom
| | - Ciara Mahon
- Royal Brompton and Harefield National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Attila Kardos
- Translational Cardiovascular Research Group, Department of Cardiology, Milton Keynes University Hospital, Milton Keynes, United Kingdom; Faculty of Medicine and Health Sciences, University of Buckingham, Buckingham, United Kingdom
| | - David Adlam
- Department of Cardiovascular Sciences and National Institute for Health Research Leicester Biomedical Research Centre, University of Leicester, Leicester, United Kingdom
| | - John Graby
- Royal United Hospitals Bath NHS Foundation Trust, Bath, United Kingdom
| | - Jonathan C L Rodrigues
- Royal United Hospitals Bath NHS Foundation Trust, Bath, United Kingdom; Department of Health, University of Bath, Bath, United Kingdom
| | - Cheerag Shirodaria
- Caristo Diagnostics Pty Ltd, Oxford, United Kingdom; Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | - Nehal N Mehta
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Keith M Channon
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | - Edward D Nicol
- Royal Brompton and Harefield National Health Service (NHS) Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
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Zoodsma RS, Bosch R, Alderliesten T, Bollen CW, Kappen TH, Koomen E, Siebes A, Nijman J. Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development. JMIR Cardio 2023; 7:e45190. [PMID: 37191988 DOI: 10.2196/45190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/16/2023] [Accepted: 04/24/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention. OBJECTIVE This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD. METHODS Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO2) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists. RESULTS A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct. CONCLUSIONS In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.
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Affiliation(s)
- Ruben S Zoodsma
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rian Bosch
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas Alderliesten
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Casper W Bollen
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Teus H Kappen
- Department of Anaesthesiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Erik Koomen
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Arno Siebes
- Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Joppe Nijman
- Department of Paediatric Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
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30
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Fraser AG, Biasin E, Bijnens B, Bruining N, Caiani EG, Cobbaert K, Davies RH, Gilbert SH, Hovestadt L, Kamenjasevic E, Kwade Z, McGauran G, O'Connor G, Vasey B, Rademakers FE. Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives. Expert Rev Med Devices 2023; 20:467-491. [PMID: 37157833 DOI: 10.1080/17434440.2023.2184685] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed. AREAS COVERED AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies. EXPERT OPINION The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.
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Affiliation(s)
- Alan G Fraser
- University Hospital of Wales, School of Medicine, Cardiff University, Heath Park, Cardiff, U.K
- KU Leuven, Leuven, Belgium
| | | | - Bart Bijnens
- Engineering Sciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Nico Bruining
- Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, the Netherlands
| | - Enrico G Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | | | - Rhodri H Davies
- Institute of Cardiovascular Science, University College London, London, U.K
| | - Stephen H Gilbert
- Technische Universität Dresden, Else Kröner Fresenius Center for Digital Health, Dresden, Germany
| | | | | | | | | | | | - Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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Brunetti ND, Curcio A, Nodari S, Parati G, Carugo S, Molinari M, Acquistapace F, Gensini G, Molinari G. The Italian Society of Cardiology and Working Group on Telecardiology and Informatics 2023 updated position paper on telemedicine and artificial intelligence in cardiovascular disease. J Cardiovasc Med (Hagerstown) 2023; 24:e168-e177. [PMID: 37186567 DOI: 10.2459/jcm.0000000000001447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
In 2015, the Italian Society of Cardiology and its Working Group on Telemedicine and Informatics issued a position paper on Telecardiology, resuming the most eminent evidence supporting the use of information and communication technology in principal areas of cardiovascular care, ranked by level of evidence. More than 5 years later and after the global shock inflicted by the SARS-CoV-2 pandemic, an update on the topic is warranted. Recent evidence and studies on principal areas of cardiovascular disease will be therefore reported and discussed, with particular focus on telemedicine for cardiovascular care in the COVID-19 context. Novel perspectives and opportunities disclosed by artificial intelligence and its applications in cardiovascular disease will also be discussed. Finally, modalities by which machine learning have realized remote patient monitoring and long-term care in recent years, mainly filtering critical clinical data requiring selective hospital admission, will be provided.
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Affiliation(s)
- Natale D Brunetti
- Division of Cardiology, Department of Medical & Surgical Sciences, University of Foggia, Foggia
| | - Antonio Curcio
- Division of Cardiology, Department of Medical and Surgical Sciences, University 'Magna Graecia' of Catanzaro, Catanzaro
| | - Savina Nodari
- Dept. of Medical and Surgical Specialities, Radiological Sciences and Public Health-University of Brescia Medical School
- University of Brescia Medical School, Brescia
| | | | - Stefano Carugo
- Department of Clinical Sciences and Community Health
- Cardiology Unit, Dept. of Internal Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore, University of Milan, Milan
| | - Martina Molinari
- Department of Cardiology, Ospedale 'P.A. Micone', ASL 3 Genovese, Genoa, Italy
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Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown) 2023; 24:e106-e115. [PMID: 37186561 DOI: 10.2459/jcm.0000000000001431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
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Affiliation(s)
- Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| | - Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Arturo Cesaro
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Paolo Calabrò
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Nicola De Luca
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Daniele Masarone
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Giuseppe Pacileo
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Nidal Tourkmani
- Cardiology and Cardiac Rehabilitation Unit, 'Mons. Giosuè Calaciura Clinic', Catania, Italy
- ABL, Guangzhou, China
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
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Wang Z, Zhang J, Zhang A, Sun Y, Su M, You H, Zhang R, Jin Q, Shi J, Zhao D, Ma J, Sen Li, Zhang L, Yang B. The role of epicardial and pericoronary adipose tissue radiomics in identifying patients with non-ST-segment elevation myocardial infarction from unstable angina. Heliyon 2023; 9:e15738. [PMID: 37153420 PMCID: PMC10160514 DOI: 10.1016/j.heliyon.2023.e15738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVES This study aimed to ascertain if the radiomics features of epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could identify non-ST-segment elevation myocardial infarction (NSTEMI) from unstable angina (UA). MATERIALS AND METHODS This retrospective case-control study included 108 patients with NSTEMI and 108 controls with UA. All patients were separated into training cohort (n = 116), internal validation cohort 1 (n = 50), and internal validation cohort 2 (n = 50) based on the time order of admission. The internal validation cohort 1 used the same scanner and scan parameters as the training cohort, while the internal validation cohort 2 used different canners and scan parameters than the training cohort. The EAT and PCAT radiomics features selected by maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted to build logistic regression models. Finally, we developed an EAT radiomics model, three vessel-based (right coronary artery [RCA], left anterior descending artery [LAD], and left circumflex artery [LCX]) PCAT radiomics models, and a combined model by combining the three PCAT radiomics models. Discrimination, calibration, and clinical application were employed to assess the performance of all models. RESULTS Eight radiomics features of EAT, sixteen of RCA-PCAT, fifteen of LAD-PCAT, and eighteen of LCX-PCAT were selected and used to construct radiomics models. The area under the curves (AUCs) of the EAT, RCA-PCAT, LAD-PCAT, LCX-PCAT and the combined models were 0.708 (95% CI: 0.614-0.802), 0.833 (95% CI:0.759-0.906), 0.720 (95% CI:0.628-0.813), 0.713 (95% CI:0.619-0.807), 0.889 (95% CI:0.832-0.946) in the training cohort, 0.693 (95% CI:0.546-0.840), 0.837 (95% CI: 0.729-0.945), 0.766 (95% CI: 0.625-0.907), 0.675 (95% CI: 0.521-0.829), 0.898 (95% CI: 0.802-0.993) in the internal validation cohort 1, and 0.691 (0.535-0.847), 0.822 (0.701-0.944), 0.760 (0.621-0.899), 0.674 (0.517-0.830), 0.866 (0.769-0.963) in the internal validation cohort 2, respectively. CONCLUSION Compared with the RCA-PCAT radiomics model, the EAT radiomics model had a limited ability to discriminate between NSTEMI and UA. The combination of the three vessel-based PCAT radiomics may have the potential to distinguish between NSTEMI and UA.
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Affiliation(s)
- Zhenguo Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Jianhua Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Anxiaonan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yu Sun
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research, Liaoning Province, China
| | - Mengwei Su
- Postgraduate College, China Medical University, Shenyang, China
| | - Hongrui You
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research, Liaoning Province, China
| | - Rongrong Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research, Liaoning Province, China
| | - Qiuyue Jin
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, Jinzhou Medical University, Jinzhou, China
| | - Jinglong Shi
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, Jinzhou Medical University, Jinzhou, China
| | - Di Zhao
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Jingji Ma
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Sen Li
- Department of Research & Development, Yizhun Medical AI Co. Ltd., Beijing, China
| | - Libo Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research, Liaoning Province, China
| | - Benqiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research, Liaoning Province, China
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Arnould L, Meriaudeau F, Guenancia C, Germanese C, Delcourt C, Kawasaki R, Cheung CY, Creuzot-Garcher C, Grzybowski A. Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review. Ophthalmol Ther 2023; 12:657-674. [PMID: 36562928 PMCID: PMC10011267 DOI: 10.1007/s40123-022-00641-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called "oculomics" using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.
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Affiliation(s)
- Louis Arnould
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France. .,University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France.
| | - Fabrice Meriaudeau
- Laboratory ImViA, IFTIM, Université Bourgogne Franche-Comté, 21078, Dijon, France
| | - Charles Guenancia
- Pathophysiology and Epidemiology of Cerebro-Cardiovascular Diseases, (EA 7460), Faculty of Health Sciences, Université de Bourgogne Franche-Comté, Dijon, France.,Cardiology Department, Dijon University Hospital, Dijon, France
| | - Clément Germanese
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France
| | - Cécile Delcourt
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France
| | - Ryo Kawasaki
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Catherine Creuzot-Garcher
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France.,Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.,Institute for Research in Ophthalmology, Poznan, Poland
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35
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Maurizi N, Fumagalli C, Skalidis I, Imberti JF, Faragli A, Targetti M, Lu H, Monney P, Muller O, Marchionni N, Cecchi F, Olivotto I. Validation of a multiple‑lead smartphone-based electrocardiograph with automated lead placement for layman use in patients with hypertrophic cardiomyopathy. J Electrocardiol 2023; 79:1-7. [PMID: 36893506 DOI: 10.1016/j.jelectrocard.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/04/2023] [Accepted: 02/25/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND A smartphone 12-Lead ECG that enables layman ECG screening is still lacking. We aimed to validate D-Heart ECG device, a smartphone 8/12 Lead electrocardiograph with an image processing algorithm to guide secure electrode placement by non-professional users. METHODS One-hundred-fourty-five patients with HCM were enrolled. Two uncovered chest images were acquired using the smartphone camera. An image with virtual electrodes placement by imaging processing algorithm software was compared to the 'gold standard' electrode placement by a doctor. D-Heart 8 and 12-Lead ECG were obtained, immediately followed by 12‑lead ECGs and were assessed by 2 independent observers. Burden of ECG abnormalities was defined by a score based on the sum of 9 criteria, identifying four classes of increasing severity. RESULTS A total of 87(60%) patients presented a normal/mildly abnormal ECG, whereas 58(40%) had moderate or severe ECG alteration. Eight(6%) patients had ≥1 misplaced electrode. D-Heart 8-Lead and 12‑lead ECGs concordance according to Cohen's weighted kappa test was 0,948 (p < 0,001, agreement of 97.93%). Concordance was high for the Romhilt-Estes score (kw = 0,912; p < 0.01). Concordance between D-Heart 12-Lead ECG and standard 12-Lead ECG was perfect (kw = 1). PR and QRS intervals measurements comparison with Bland-Altman method showed good accuracy (95% limit of agreement ±18 ms for PR and ± 9 ms for QRS). CONCLUSIONS D-Heart 8/12-Lead ECGs proved accurate, allowing an assessment of ECG abnormalities comparable to the standard 12‑lead ECG in patients with HCM. The image processing algorithm provided accurate electrode placement, standardizing exam quality, potentially opening perspectives for layman ECG screening campaigns.
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Affiliation(s)
- Niccolò Maurizi
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy; Cardiology Service, University Hospital of Lausanne, Lausanne, Switzerland.
| | - Carlo Fumagalli
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | - Ioannis Skalidis
- Cardiology Service, University Hospital of Lausanne, Lausanne, Switzerland
| | - Jacopo F Imberti
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Italy
| | - Alessandro Faragli
- Department of Internal Medicine and Cardiology, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Mattia Targetti
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | - Henri Lu
- Cardiology Service, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pierre Monney
- Cardiology Service, University Hospital of Lausanne, Lausanne, Switzerland
| | - Olivier Muller
- Cardiology Service, University Hospital of Lausanne, Lausanne, Switzerland
| | - Niccolò Marchionni
- Department of Clinical and Experimental Medicine, University of Florence, Italy
| | - Franco Cecchi
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy; Department of Clinical and Experimental Medicine, University of Florence, Italy
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36
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Antikainen E, Linnosmaa J, Umer A, Oksala N, Eskola M, van Gils M, Hernesniemi J, Gabbouj M. Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records. Sci Rep 2023; 13:3517. [PMID: 36864069 PMCID: PMC9978282 DOI: 10.1038/s41598-023-30657-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.
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Affiliation(s)
- Emmi Antikainen
- VTT Technical Research Centre of Finland Ltd., 33101, Tampere, Finland.
| | - Joonas Linnosmaa
- grid.6324.30000 0004 0400 1852VTT Technical Research Centre of Finland Ltd., 33101 Tampere, Finland
| | - Adil Umer
- grid.6324.30000 0004 0400 1852VTT Technical Research Centre of Finland Ltd., 33101 Tampere, Finland
| | - Niku Oksala
- grid.502801.e0000 0001 2314 6254Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland ,Finnish Cardiovascular Research Center Tampere, 33520 Tampere, Finland ,grid.412330.70000 0004 0628 2985Vascular Centre, Tampere University Hospital, 33520 Tampere, Finland
| | - Markku Eskola
- Finnish Cardiovascular Research Center Tampere, 33520 Tampere, Finland ,grid.412330.70000 0004 0628 2985Tays Heart Hospital, Tampere University Hospital, 33521 Tampere, Finland
| | - Mark van Gils
- grid.502801.e0000 0001 2314 6254Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
| | - Jussi Hernesniemi
- grid.502801.e0000 0001 2314 6254Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland ,Finnish Cardiovascular Research Center Tampere, 33520 Tampere, Finland ,grid.412330.70000 0004 0628 2985Tays Heart Hospital, Tampere University Hospital, 33521 Tampere, Finland
| | - Moncef Gabbouj
- grid.502801.e0000 0001 2314 6254Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland
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37
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Skalidis I, Fournier S, Skalidis E, Maurizi N. Virtual hospitals and digital doctors: how far are we from the CardioVerse? Eur Heart J 2023; 44:7-9. [PMID: 36300348 DOI: 10.1093/eurheartj/ehac603] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Ioannis Skalidis
- Cardiology Department, University Hospital of Lausanne, Rue de Bugnon 46, Lausanne 1011, Switzerland
- Cardiology Department, University Hospital of Heraklion, Voutes and Stavrakia, 71110, Heraklion, Greece
| | - Stephane Fournier
- Cardiology Department, University Hospital of Lausanne, Rue de Bugnon 46, Lausanne 1011, Switzerland
| | - Emmanouil Skalidis
- Cardiology Department, University Hospital of Heraklion, Voutes and Stavrakia, 71110, Heraklion, Greece
| | - Niccolo Maurizi
- Cardiology Department, University Hospital of Lausanne, Rue de Bugnon 46, Lausanne 1011, Switzerland
- Cardiomyopathy Unit, University hospital of Carregi, Largo Giovanni Alessandro Brambilla 3, 50134, Florence, Italy
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38
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Crea F. Acute and chronic heart failure: exciting therapeutic perspectives. Eur Heart J 2023; 44:1-4. [PMID: 36586417 DOI: 10.1093/eurheartj/ehac767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Filippo Crea
- Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
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39
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Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis. J Geriatr Cardiol 2022; 19:970-980. [PMID: 36632204 PMCID: PMC9807402 DOI: 10.11909/j.issn.1671-5411.2022.12.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG. METHODS We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed. RESULTS A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk. CONCLUSIONS According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
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40
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Siński M, Berka P, Lewandowski J, Sobieraj P, Piechocki K, Paleczny B, Siennicka A. Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control? J Clin Med 2022; 11:jcm11247454. [PMID: 36556072 PMCID: PMC9785044 DOI: 10.3390/jcm11247454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/06/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should not stop clinicians from achieving SBP targets; however, registries and analyses of randomized trials present conflicting results. The purpose of the study was to apply machine learning (ML) algorithms to determine, whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary outcome in the SPRINT trial database. Methods: ML experiments were performed using decision tree, random forest, k-nearest neighbor, naive Bayesian, multi-layer perceptron, and logistic regression algorithms, including and excluding DBP as the risk factor in an unselected and selected (DBP < 70 mmHg) study population. Results: Including DBP as the risk factor did not change the performance of the machine learning models evaluated using accuracy, AUC, mean, and weighted F-measure, and was not required to make proper predictions of stroke, MI, HF, and primary outcome. Conclusions: Analyses of the SPRINT trial data using ML algorithms imply that DBP should not be treated as an independent risk factor when intensifying blood pressure control.
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Affiliation(s)
- Maciej Siński
- Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland
| | - Petr Berka
- Department of Information and Knowledge Engineering, Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 4, 120 00 Prague, Czech Republic
| | - Jacek Lewandowski
- Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland
- Correspondence: ; Tel./Fax: +48-22-5991828
| | - Piotr Sobieraj
- Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland
| | - Kacper Piechocki
- Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland
| | - Bartłomiej Paleczny
- Department of Physiology and Pathophysiology, Wroclaw Medical University, Chałubińskiego 10, 50-368 Wroclaw, Poland
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, Chałubińskiego 10, 50-368 Wroclaw, Poland
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41
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20 Years of Critical Pathways. Crit Pathw Cardiol 2022; 21:161. [PMID: 36440852 DOI: 10.1097/hpc.0000000000000305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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42
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Szabo L, Raisi-Estabragh Z, Salih A, McCracken C, Ruiz Pujadas E, Gkontra P, Kiss M, Maurovich-Horvath P, Vago H, Merkely B, Lee AM, Lekadir K, Petersen SE. Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Front Cardiovasc Med 2022; 9:1016032. [PMID: 36426221 PMCID: PMC9681217 DOI: 10.3389/fcvm.2022.1016032] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/11/2022] [Indexed: 12/01/2023] Open
Abstract
A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.
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Affiliation(s)
- Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Mate Kiss
- Siemens Healthcare Hungary, Budapest, Hungary
| | - Pal Maurovich-Horvath
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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43
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Pap IA, Oniga S. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11413. [PMID: 36141685 PMCID: PMC9517043 DOI: 10.3390/ijerph191811413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.
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Affiliation(s)
- Iuliu Alexandru Pap
- Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430083 Baia Mare, Romania
| | - Stefan Oniga
- Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430083 Baia Mare, Romania
- Department of IT Systems and Networks, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary
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44
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Al-Zaiti S, Macleod R, Dam PV, Smith SW, Birnbaum Y. Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities. J Electrocardiol 2022; 74:65-72. [PMID: 36027675 DOI: 10.1016/j.jelectrocard.2022.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/13/2022]
Abstract
Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.
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Affiliation(s)
- Salah Al-Zaiti
- Department of Acute & Tertiary Care, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Robert Macleod
- Department of Biomedical Engineering, University of Utah, Salt Lake, UT, USA
| | - Peter Van Dam
- Department of Cardiology, University Medical Center Utrecht, the Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare and University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
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45
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Crea F. The far-reaching beneficial effects of sodium-glucose co-transporter 2 inhibitors in heart failure. Eur Heart J 2022; 43:2907-2910. [PMID: 35963617 DOI: 10.1093/eurheartj/ehac437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Filippo Crea
- Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
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