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Bacharova L, Chevalier P, Gorenek B, Jons C, Li Y, Locati ET, Maanja M, Pérez‐Riera AR, Platonov PG, Ribeiro ALP, Schocken D, Soliman EZ, Svehlikova J, Tereshchenko LG, Ugander M, Varma N, Elena Z, Ikeda T. ISE/ISHNE expert consensus statement on the ECG diagnosis of left ventricular hypertrophy: The change of the paradigm. Ann Noninvasive Electrocardiol 2024; 29:e13097. [PMID: 37997698 PMCID: PMC10770819 DOI: 10.1111/anec.13097] [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: 08/10/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023] Open
Abstract
The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria. The classical paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces, reflected in the augmented QRS amplitude. However, the low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm. The theoretical background for voltage measured at the body surface is defined by the solid angle theorem, which relates the measured voltage to spatial and non-spatial determinants. The spatial determinants are represented by the extent of the activation front and the distance of the recording electrodes. The non-spatial determinants comprise electrical characteristics of the myocardium, which are comparatively neglected in the interpretation of the QRS patterns. Various clinical conditions are associated with LVH. These conditions produce considerable diversity of electrical properties alterations thereby modifying the resultant QRS patterns. The spectrum of QRS patterns observed in LVH patients is quite broad, including also left axis deviation, left anterior fascicular block, incomplete and complete left bundle branch blocks, Q waves, and fragmented QRS. Importantly, the QRS complex can be within normal limits. The new paradigm stresses the electrophysiological background in interpreting QRS changes, i.e., the effect of the non-spatial determinants. This postulates that the role of ECG is not to estimate LV size in LVH, but to understand and decode the underlying electrical processes, which are crucial in relation to cardiovascular risk assessment.
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Affiliation(s)
| | - Philippe Chevalier
- Neuromyogene InstituteClaude Bernard UniversityVilleurbanneFrance
- Service de RythmologieHospices Civils de LyonLyonFrance
| | - Bulent Gorenek
- Eskisehir Osmangazi University Cardiology DepartmentEskisehirTurkey
| | - Christian Jons
- Department of CardiologyRigshospitalet, Copenhagen University HospitalCopenhagenDenmark
| | - Yi‐Gang Li
- Department of Cardiology, Xinhua HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Emanuela T. Locati
- Department of Arrhythmology and ElectrophysiologyIRCCS Policlinico San DonatoMilanoItaly
| | - Maren Maanja
- Department of Clinical PhysiologyKarolinska University Hospital, and Karolinska InstitutetStockholmSweden
| | | | - Pyotr G. Platonov
- Department of Cardiology, Clinical SciencesLund UniversityLundSweden
| | - Antonio Luiz Pinho Ribeiro
- Internal Medicine, Faculdade de Medicina da Universidade Federal de Minas GeraisBelo HorizonteBrazil
- Telehealth Center, Hospital das Clínicas da Universidade Federal de Minas GeraisBelo HorizonteBrazil
| | - Douglas Schocken
- Division of Cardiology, Department of MedicineDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Elsayed Z. Soliman
- Section on Cardiovascular Medicine, Department of Medicine, Epidemiological Cardiology Research CenterWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Jana Svehlikova
- Institute of Measurement Sciences, Slovak Academy of SciencesBratislavaSlovak Republic
| | - Larisa G. Tereshchenko
- Department of Quantitative Health SciencesLerner Research Institute, Cleveland ClinicClevelandOhioUSA
| | - Martin Ugander
- Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
- Department of Clinical PhysiologyKarolinska InstituteStockholmSweden
| | - Niraj Varma
- Cardiac Pacing & ElectrophysiologyHeart and Vascular Institute, Cleveland ClinicClevelandOhioUSA
| | - Zaklyazminskaya Elena
- Medical Genetics LaboratoryPetrovsky National Research Centre of SurgeryMoscowRussia
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Lundin M, Heiberg E, Nordlund D, Gyllenhammar T, Steding-Ehrenborg K, Engblom H, Carlsson M, Atar D, van der Pals J, Erlinge D, Borgquist R, Khoshnood A, Ekelund U, Nickander J, Themudo R, Nordin S, Kozor R, Bhuva AN, Moon JC, Maret E, Caidahl K, Sigfridsson A, Sörensson P, Schelbert EB, Arheden H, Ugander M. Prognostic utility and characterization of left ventricular hypertrophy using global thickness. Sci Rep 2023; 13:22806. [PMID: 38129418 PMCID: PMC10740032 DOI: 10.1038/s41598-023-48173-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) can accurately measure left ventricular (LV) mass, and several measures related to LV wall thickness exist. We hypothesized that prognosis can be used to select an optimal measure of wall thickness for characterizing LV hypertrophy. Subjects having undergone CMR were studied (cardiac patients, n = 2543; healthy volunteers, n = 100). A new measure, global wall thickness (GT, GTI if indexed to body surface area) was accurately calculated from LV mass and end-diastolic volume. Among patients with follow-up (n = 1575, median follow-up 5.4 years), the most predictive measure of death or hospitalization for heart failure was LV mass index (LVMI) (hazard ratio (HR)[95% confidence interval] 1.16[1.12-1.20], p < 0.001), followed by GTI (HR 1.14[1.09-1.19], p < 0.001). Among patients with normal findings (n = 326, median follow-up 5.8 years), the most predictive measure was GT (HR 1.62[1.35-1.94], p < 0.001). GT and LVMI could characterize patients as having a normal LV mass and wall thickness, concentric remodeling, concentric hypertrophy, or eccentric hypertrophy, and the three abnormal groups had worse prognosis than the normal group (p < 0.05 for all). LV mass is highly prognostic when mass is elevated, but GT is easily and accurately calculated, and adds value and discrimination amongst those with normal LV mass (early disease).
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Affiliation(s)
- Magnus Lundin
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Einar Heiberg
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - David Nordlund
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Tom Gyllenhammar
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Katarina Steding-Ehrenborg
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
- Department of Health Sciences, Physiotherapy, Lund University, Lund, Sweden
| | - Henrik Engblom
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Marcus Carlsson
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Dan Atar
- Department of Cardiology, and Institute of Clinical Medicine, Oslo University Hospital Ulleval, University of Oslo, Oslo, Norway
| | - Jesper van der Pals
- Arrhythmia Clinic, Skåne University Hospital, and Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - David Erlinge
- Department of Clinical Sciences, Cardiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Rasmus Borgquist
- Arrhythmia Clinic, Skåne University Hospital, and Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Ardavan Khoshnood
- Department of Clinical Sciences, Emergency and Internal Medicine, Lund University, Skåne University Hospital, Lund, Sweden
| | - Ulf Ekelund
- Department of Clinical Sciences, Emergency and Internal Medicine, Lund University, Skåne University Hospital, Lund, Sweden
| | - Jannike Nickander
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Raquel Themudo
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Sabrina Nordin
- Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Kozor
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia
| | - Anish N Bhuva
- Institute of Cardiovascular Science, University College London, London, UK
- Department of Cardiology, Barts Heart Centre, London, UK
| | - James C Moon
- Institute of Cardiovascular Science, University College London, London, UK
- Department of Cardiology, Barts Heart Centre, London, UK
| | - Eva Maret
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Kenneth Caidahl
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
- Institute of Medicine, University of Gothenburg and Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Andreas Sigfridsson
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Peder Sörensson
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | | | - Håkan Arheden
- Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Martin Ugander
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden.
- Kolling Institute, Royal North Shore Hospital, and University of Sydney, Sydney, Australia.
- Royal North Shore Hospital, University of Sydney, Kolling Building, Level 12, Room 612017, St Leonards, NSW, 2065, Australia.
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Bacharova L, Chevalier P, Gorenek B, Jons C, Li YG, Locati ET, Maanja M, Pérez-Riera AR, Platonov PG, Ribeiro ALP, Schocken D, Soliman EZ, Svehlikova J, Tereshchenko LG, Ugander M, Varma N, Zaklyazminskaya E, Ikeda T. ISE/ISHNE Expert Consensus Statement on ECG Diagnosis of Left Ventricular Hypertrophy: The Change of the Paradigm. The joint paper of the International Society of Electrocardiology and the International Society for Holter Monitoring and Noninvasive Electrocardiology. J Electrocardiol 2023; 81:85-93. [PMID: 37647776 DOI: 10.1016/j.jelectrocard.2023.08.005] [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: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/01/2023]
Abstract
The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria, i.e. the increased QRS complex amplitude in defined leads. The classical ECG diagnostic paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces. These increased forces are reflected in the augmented QRS amplitude in the corresponding leads. However, the clinical observations document increased QRS amplitude only in the minority of patients with LVH. The low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm.
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Affiliation(s)
- Ljuba Bacharova
- International Laser Center CVTI, Ilkovicova 3, 841 04 Bratislava, Slovak Republic.
| | - Philippe Chevalier
- Neuromyogene Institute, Claude Bernard University, Lyon 1, Villeurbanne, France; Service de Rythmologie, Hospices Civils de Lyon, Lyon, France.
| | - Bulent Gorenek
- Eskisehir Osmangazi University, Cardiology Department, Eskisehir, Turkiye.
| | - Christian Jons
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Yi-Gang Li
- Department of Cardiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 200092 Shanghai, PR China.
| | - Emanuela T Locati
- Department of Arrhythmology and Electrophysiology, IRCCS Policlinico San Donato, Piazza E. Malan 2, 20097 San Donato Milanese, Milano, Italy.
| | - Maren Maanja
- Department of Clinical Physiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.
| | | | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden.
| | - Antonio Luiz P Ribeiro
- Internal Medicine, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Telehealth Center, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Douglas Schocken
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA.
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Jana Svehlikova
- Institute of Measurement Sciences, Slovak Academy of Sciences, Bratislava, Slovak Republic.
| | - Larisa G Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave JJN3-01, Cleveland, OH 44195, USA.
| | - Martin Ugander
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Department of Clinical Physiology, Karolinska Institute, Stockholm, Stockholm, Sweden
| | - Niraj Varma
- Cardiac Pacing & Electrophysiology, Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Ave J2-2, Cleveland, OH 44195, USA.
| | - Elena Zaklyazminskaya
- Medical Genetics Laboratory, Petrovsky National Research Centre of Surgery, Moscow 119991, Russia
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Topriceanu CC, Dev E, Ahmad M, Hughes R, Shiwani H, Webber M, Direk K, Wong A, Ugander M, Moon JC, Hughes AD, Maddock J, Schlegel TT, Captur G. Accelerated DNA methylation age plays a role in the impact of cardiovascular risk factors on the human heart. Clin Epigenetics 2023; 15:164. [PMID: 37853450 PMCID: PMC10583368 DOI: 10.1186/s13148-023-01576-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/29/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND DNA methylation (DNAm) age acceleration (AgeAccel) and cardiac age by 12-lead advanced electrocardiography (A-ECG) are promising biomarkers of biological and cardiac aging, respectively. We aimed to explore the relationships between DNAm age and A-ECG heart age and to understand the extent to which DNAm AgeAccel relates to cardiovascular (CV) risk factors in a British birth cohort from 1946. RESULTS We studied four DNAm ages (AgeHannum, AgeHorvath, PhenoAge, and GrimAge) and their corresponding AgeAccel. Outcomes were the results from two publicly available ECG-based cardiac age scores: the Bayesian A-ECG-based heart age score of Lindow et al. 2022 and the deep neural network (DNN) ECG-based heart age score of Ribeiro et al. 2020. DNAm AgeAccel was also studied relative to results from two logistic regression-based A-ECG disease scores, one for left ventricular (LV) systolic dysfunction (LVSD), and one for LV electrical remodeling (LVER). Generalized linear models were used to explore the extent to which any associations between biological cardiometabolic risk factors (body mass index, hypertension, diabetes, high cholesterol, previous cardiovascular disease [CVD], and any CV risk factor) and the ECG-based outcomes are mediated by DNAm AgeAccel. We derived the total effects, average causal mediation effects (ACMEs), average direct effects (ADEs), and the proportion mediated [PM] with their 95% confidence intervals [CIs]. 498 participants (all 60-64 years) were included, with the youngest ECG heart age being 27 and the oldest 90. When exploring the associations between cardiometabolic risk factors and Bayesian A-ECG cardiac age, AgeAccelPheno appears to be a partial mediator, as ACME was 0.23 years [0.01, 0.52] p = 0.028 (i.e., PM≈18%) for diabetes, 0.34 [0.03, 0.74] p = 0.024 (i.e., PM≈15%) for high cholesterol, and 0.34 [0.03, 0.74] p = 0.024 (PM≈15%) for any CV risk factor. Similarly, AgeAccelGrim mediates ≈30% of the relationship between diabetes or high cholesterol and the DNN ECG-based heart age. When exploring the link between cardiometabolic risk factors and the A-ECG-based LVSD and LVER scores, it appears that AgeAccelPheno or AgeAccelGrim mediate 10-40% of these associations. CONCLUSION By the age of 60, participants with accelerated DNA methylation appear to have older, weaker, and more electrically impaired hearts. We show that the harmful effects of CV risk factors on cardiac age and health, appear to be partially mediated by DNAm AgeAccelPheno and AgeAccelGrim. This highlights the need to further investigate the potential cardioprotective effects of selective DNA methyltransferases modulators.
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Affiliation(s)
- Constantin-Cristian Topriceanu
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Eesha Dev
- UCL Medical School, Gower Street, London, UK
| | - Mahmood Ahmad
- Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital, Pond Street, Hampstead, London, UK
| | - Rebecca Hughes
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Hunain Shiwani
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Matthew Webber
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
| | - Kenan Direk
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
| | - Andrew Wong
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
| | - Martin Ugander
- Kolling Institute Royal North Shore Hospital, and Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - James C Moon
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
- Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, UK
| | - Alun D Hughes
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
| | - Jane Maddock
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK
| | - Todd T Schlegel
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
- Nicollier-Schlegel SARL, Trélex, Switzerland
| | - Gabriella Captur
- UCL MRC Unit for Lifelong Health and Ageing, University College London, 1-19 Torrington Place, London, UK.
- UCL Institute of Cardiovascular Science, University College London, 62 Huntley St, London, WC1E 6BT, UK.
- Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital, Pond Street, Hampstead, London, UK.
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Lindow T, Maanja M, Schelbert EB, Ribeiro AH, Ribeiro ALP, Schlegel TT, Ugander M. Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:384-392. [PMID: 37794867 PMCID: PMC10545529 DOI: 10.1093/ehjdh/ztad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/05/2023] [Indexed: 10/06/2023]
Abstract
Aims Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods and results Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice. Conclusion A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.
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Affiliation(s)
- Thomas Lindow
- Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Research and Development, Växjö Central Hospital, Region Kronoberg, Sweden
- Clinical Physiology, Clinical Sciences, Lund University, Sweden
| | - Maren Maanja
- Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | | | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Antonio Luiz P Ribeiro
- Telehealth Center, Hospital das Clínicas, and Internal Medicine Department, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Todd T Schlegel
- Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
- Nicollier-Schlegel SARL, Trélex, Switzerland
| | - Martin Ugander
- Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
- Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
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Heart age estimated using explainable advanced electrocardiography. Sci Rep 2022; 12:9840. [PMID: 35701514 PMCID: PMC9198017 DOI: 10.1038/s41598-022-13912-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 05/30/2022] [Indexed: 11/24/2022] Open
Abstract
Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-s 12-lead ECG could successfully predict Bayesian 5-min ECG Heart Age. Advanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict patients’ Bayesian 5-min ECG Heart Ages from their standard, resting 10-s 12-lead ECGs. The difference between 5-min and 10-s ECG Heart Ages were analyzed, as were the differences between 10-s ECG Heart Age and the chronological age (the Heart Age Gap). In total, 2,771 subjects were included (n = 1682 healthy volunteers, n = 305 with cardiovascular risk factors, n = 784 with cardiovascular disease). Overall, 10-s Heart Age showed strong agreement with the 5-min Heart Age (R2 = 0.94, p < 0.001, mean ± SD bias 0.0 ± 5.1 years). The Heart Age Gap was 0.0 ± 5.7 years in healthy individuals, 7.4 ± 7.3 years in subjects with cardiovascular risk factors (p < 0.001), and 14.3 ± 9.2 years in patients with cardiovascular disease (p < 0.001). Heart Age can be accurately estimated from a 10-s 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without deep neural network-type artificial intelligence techniques. The Heart Age Gap increases markedly with cardiovascular risk and disease.
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7
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Sapra R, Hallqvist L, Schlegel TT, Ugander M, Bell M, Maanja M. Predicting peri-operative troponin elevation by advanced electrocardiography. J Electrocardiol 2021; 68:1-5. [PMID: 34246860 DOI: 10.1016/j.jelectrocard.2021.06.008] [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: 04/30/2021] [Revised: 06/22/2021] [Accepted: 06/28/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Peri-operative mortality remains a global problem and an improved pre-operative risk assessment identifying those at highest risk for peri-operative myocardial injury might improve postsurgical outcomes. AIMS To determine whether pre-operative measures of advanced electrocardiography (A-ECG) could predict elevated serum troponin T (TnT) in patients undergoing elective, major non-cardiac surgery. MATERIAL AND METHODS This observational cohort study included 257 surgical patients who underwent elective major non-cardiac surgery between the years 2012-2013 and 2015-2016 at Karolinska University Hospital. All selected patients were ≥ 18 years of age [median age 70 (63-75) years], had a pre-operative digital 12‑lead ECG < 6 months prior to the procedure and a postoperative high-sensitivity cardiac TnT (hs-cTnT) sample. A-ECG confounders including atrial fibrillation or flutter, abundant premature atrial or ventricular contractions, bundle branch blocks, QRS duration >110 ms, heart rate > 100 beats/min and paced rhythms were excluded. Previously validated A-ECG diagnostic scores that detect cardiovascular pathologies were calculated and compared in patients with and without peri-operative myocardial injury, defined as hs-cTnT >14 ng l-1. RESULTS Pre-operative left ventricular systolic dysfunction by A-ECG was more probable in patients with than without peri-operative myocardial injury (p = 0.03). CONCLUSIONS While a pre-operative A-ECG score for LVSD was able to differentiate between patients with versus without elevated peri-operative TnT levels, it did not add any further utility to standard clinical parameters for predicting troponin-related events in the studied population.
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Affiliation(s)
- Richa Sapra
- Department of Anaesthesia and Intensive Care Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Linn Hallqvist
- Department of Anaesthesia and Intensive Care Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Physiology and Pharmacology, Karolinska University Hospital, Stockholm, Sweden
| | - Todd T Schlegel
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden; Nicollier-Schlegel SARL, Trélex, Switzerland
| | - Martin Ugander
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden; The Kolling Institute, Royal North Shore Hospital, Northern Clinical School, Sydney Medical School, University of Sydney, Sydney, Australia
| | - Max Bell
- Department of Anaesthesia and Intensive Care Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Physiology and Pharmacology, Karolinska University Hospital, Stockholm, Sweden
| | - Maren Maanja
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden.
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