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Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.02.24305232. [PMID: 38633808 PMCID: PMC11023679 DOI: 10.1101/2024.04.02.24305232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.
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Affiliation(s)
- Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Luisa CC Brant
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Patel R, Peesay T, Krishnan V, Wilcox J, Wilsbacher L, Khan SS. Prioritizing the primary prevention of heart failure: Measuring, modifying and monitoring risk. Prog Cardiovasc Dis 2024; 82:2-14. [PMID: 38272339 PMCID: PMC10947831 DOI: 10.1016/j.pcad.2024.01.001] [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: 01/07/2024] [Accepted: 01/07/2024] [Indexed: 01/27/2024]
Abstract
With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.
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Affiliation(s)
- Ruchi Patel
- Department of Medicine, Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Tejasvi Peesay
- Department of Medicine, Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Vaishnavi Krishnan
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jane Wilcox
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lisa Wilsbacher
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Sadiya S Khan
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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Kotta PA, Nambi V, Bozkurt B. Biomarkers for Heart Failure Prediction and Prevention. J Cardiovasc Dev Dis 2023; 10:488. [PMID: 38132656 PMCID: PMC10744096 DOI: 10.3390/jcdd10120488] [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: 09/17/2023] [Revised: 11/13/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Heart failure (HF) is a global pandemic affecting over 64 million people worldwide. Its prevalence is on an upward trajectory, with associated increasing healthcare expenditure. Organizations including the American College of Cardiology (ACC) and the American Heart Association (AHA) have identified HF prevention as an important focus. Recently, the ACC/AHA/Heart Failure Society of America (HFSA) Guidelines on heart failure were updated with a new Class IIa, Level of Evidence B recommendation for biomarker-based screening in patients at risk of developing heart failure. In this review, we evaluate the studies that have assessed the various roles and contributions of biomarkers in the prediction and prevention of heart failure. We examined studies that have utilized biomarkers to detect cardiac dysfunction or abnormality for HF risk prediction and screening before patients develop clinical signs and symptoms of HF. We also included studies with biomarkers on prognostication and risk prediction over and above existing HF risk prediction models and studies that address the utility of changes in biomarkers over time for HF risk. We discuss studies of biomarkers to guide management and assess the efficacy of prevention strategies and multi-biomarker and multimodality approaches to improve risk prediction.
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Affiliation(s)
| | - Vijay Nambi
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA;
- Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA
| | - Biykem Bozkurt
- Department of Medicine, Cardiology Section, Winters Center for Heart Failure Research, Cardiovascular Research Institute, Baylor College of Medicine, DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA;
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Nadarajah R, Younsi T, Romer E, Raveendra K, Nakao YM, Nakao K, Shuweidhi F, Hogg DC, Arbel R, Zahger D, Iakobishvili Z, Fonarow GC, Petrie MC, Wu J, Gale CP. Prediction models for heart failure in the community: A systematic review and meta-analysis. Eur J Heart Fail 2023; 25:1724-1738. [PMID: 37403669 DOI: 10.1002/ejhf.2970] [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: 05/15/2023] [Revised: 05/25/2023] [Accepted: 07/01/2023] [Indexed: 07/06/2023] Open
Abstract
AIMS Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. METHODS AND RESULTS From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.
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Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Tanina Younsi
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Elizabeth Romer
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - Yoko M Nakao
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
| | - Kazuhiro Nakao
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | | | - David C Hogg
- School of Computing, University of Leeds, Leeds, UK
| | - Ronen Arbel
- Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel
- Maximizing Health Outcomes Research Lab, Sapir College, Sderot, Israel
| | - Doron Zahger
- Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel
- Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Zaza Iakobishvili
- Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
- Department of Community Cardiology, Clalit Health Fund, Tel Aviv, Israel
| | - Gregg C Fonarow
- Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Mark C Petrie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jianhua Wu
- School of Dentistry, University of Leeds, Leeds, UK
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Chris P Gale
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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