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Namasivayam M, Meredith T, Muller DWM, Roy DA, Roy AK, Kovacic JC, Hayward CS, Feneley MP. Machine learning prediction of progressive subclinical myocardial dysfunction in moderate aortic stenosis. Front Cardiovasc Med 2023; 10:1153814. [PMID: 37324638 PMCID: PMC10266266 DOI: 10.3389/fcvm.2023.1153814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Background Moderate severity aortic stenosis (AS) is poorly understood, is associated with subclinical myocardial dysfunction, and can lead to adverse outcome rates that are comparable to severe AS. Factors associated with progressive myocardial dysfunction in moderate AS are not well described. Artificial neural networks (ANNs) can identify patterns, inform clinical risk, and identify features of importance in clinical datasets. Methods We conducted ANN analyses on longitudinal echocardiographic data collected from 66 individuals with moderate AS who underwent serial echocardiography at our institution. Image phenotyping involved left ventricular global longitudinal strain (GLS) and valve stenosis severity (including energetics) analysis. ANNs were constructed using two multilayer perceptron models. The first model was developed to predict change in GLS from baseline echocardiography alone and the second to predict change in GLS using data from baseline and serial echocardiography. ANNs used a single hidden layer architecture and a 70%:30% training/testing split. Results Over a median follow-up interval of 1.3 years, change in GLS (≤ or >median change) could be predicted with accuracy rates of 95% in training and 93% in testing using ANN with inputs from baseline echocardiogram data alone (AUC: 0.997). The four most important predictive baseline features (reported as normalized % importance relative to most important feature) were peak gradient (100%), energy loss (93%), GLS (80%), and DI < 0.25 (50%). When a further model was run including inputs from both baseline and serial echocardiography (AUC 0.844), the top four features of importance were change in dimensionless index between index and follow-up studies (100%), baseline peak gradient (79%), baseline energy loss (72%), and baseline GLS (63%). Conclusions Artificial neural networks can predict progressive subclinical myocardial dysfunction with high accuracy in moderate AS and identify features of importance. Key features associated with classifying progression in subclinical myocardial dysfunction included peak gradient, dimensionless index, GLS, and hydraulic load (energy loss), suggesting that these features should be closely evaluated and monitored in AS.
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Affiliation(s)
- Mayooran Namasivayam
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - Thomas Meredith
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - David W. M. Muller
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - David A. Roy
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Andrew K. Roy
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
| | - Jason C. Kovacic
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Vascular Biology Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- Icahn School of Medicine at Mount Sinai, Cardiovascular Research Institute, New York, NY, United States
| | - Christopher S. Hayward
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Cardiac Mechanics Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | - Michael P. Feneley
- Department of Cardiology, St Vincent’s Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Cardiac Mechanics Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
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Chao CJ, Kato N, Scott CG, Lopez-Jimenez F, Lin G, Kane GC, Pellikka PA. Unsupervised Machine Learning for Assessment of Left Ventricular Diastolic Function and Risk Stratification. J Am Soc Echocardiogr 2022; 35:1214-1225.e8. [PMID: 35840082 DOI: 10.1016/j.echo.2022.06.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND The 2016 American Society of Echocardiography (ASE) guidelines have been widely used to assess left ventricular diastolic function. However, limitations are present in the current classification system. We aimed to develop a data-driven, unsupervised machine learning approach for diastolic function classification and risk stratification using the left ventricular diastolic function parameters recommended by the 2016 ASE guidelines; the guideline grading was used as the reference standard. METHODS Baseline demographics, heart failure hospitalization and all-cause mortality data were obtained for all adult patients who underwent transthoracic echocardiography at Mayo Clinic Rochester in 2015. Patients with prior mitral valve intervention, congenital heart disease, cardiac transplant, or cardiac assist device were excluded. Nine left ventricular diastolic function variables (mitral E and A wave peak velocities, E/A, deceleration time, medial and lateral annulus e' and E/e', and tricuspid regurgitation peak velocity) were used for an unsupervised machine learning algorithm to identify different phenotype clusters. The cohort average of each variable was used for imputation. Patients were grouped according to the algorithm-determined clusters for Kaplan-Meier survival analysis. RESULTS Among 24,414 patients, age 63.6 ±16.2 years, all-cause mortality occurred in 4,612 (18.9%) patients during median follow-up 3.1 years. The algorithm determined 3 clusters with echocardiographic measurement characteristics corresponding to normal diastolic function (n= 8,312), impaired relaxation (n=11,779) and increased filling pressure (n =4,323), with 3-year cumulative mortality of 11.8%, 19.9% and 33.4%, respectively (p<0.0001). All 10,694 (43.8%) patients classified as indeterminate were reclassified into the 3 clusters (3,324, 5,353, and 2,017, respectively) with 3-year mortality of 16.6%, 22.9% and 34.4%, respectively. The clusters also outperformed guideline-based grade for prognostication (c-index: 0.607 vs. 0.582, p=0.013). CONCLUSIONS Unsupervised machine learning identified physiologically and prognostically distinct clusters based on 9 diastolic function Doppler variables. The clusters can be potentially applied in echocardiography laboratory practice and future clinical trials for simple, replicable diastolic function related risk stratification.
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Affiliation(s)
- Chieh-Ju Chao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Nahoko Kato
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Christopher G Scott
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | | | - Grace Lin
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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Tseng AS, Lopez-Jimenez F, Pellikka PA. Future Guidelines for Artificial Intelligence in Echocardiography. J Am Soc Echocardiogr 2022; 35:878-882. [DOI: 10.1016/j.echo.2022.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/14/2022] [Accepted: 04/16/2022] [Indexed: 11/28/2022]
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Strom JB, Sengupta PP. Predicting Preclinical Heart Failure Progression: The Rise of Machine-Learning for Population Health. JACC Cardiovasc Imaging 2022; 15:209-211. [PMID: 34656485 DOI: 10.1016/j.jcmg.2021.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/25/2021] [Accepted: 09/07/2021] [Indexed: 11/28/2022]
Affiliation(s)
- Jordan B Strom
- Division of Cardiovascular, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts, USA; Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Boston, Massachusetts, USA.
| | - Partho P Sengupta
- Division of Cardiology, Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA. https://twitter.com/SmithBIDMC
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Loncaric F, Marti Castellote PM, Sanchez-Martinez S, Fabijanovic D, Nunno L, Mimbrero M, Sanchis L, Doltra A, Montserrat S, Cikes M, Crispi F, Piella G, Sitges M, Bijnens B. Automated Pattern Recognition in Whole-Cardiac Cycle Echocardiographic Data: Capturing Functional Phenotypes with Machine Learning. J Am Soc Echocardiogr 2021; 34:1170-1183. [PMID: 34245826 DOI: 10.1016/j.echo.2021.06.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/29/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Echocardiography provides complex data on cardiac function that can be integrated into patterns of dysfunction related to the severity of cardiac disease. The aim of this study was to demonstrate the feasibility of applying machine learning (ML) to automate the integration of echocardiographic data from the whole cardiac cycle and to automatically recognize patterns in velocity profiles and deformation curves, allowing the identification of functional phenotypes. METHODS Echocardiography was performed in 189 clinically managed patients with hypertension and 97 healthy individuals without hypertension. Speckle-tracking analysis of the left ventricle and atrium was performed, and deformation curves were extracted. Aortic and mitral blood pool pulsed-wave Doppler and mitral annular tissue pulsed-wave Doppler velocity profiles were obtained. These whole-cardiac cycle deformation and velocity curves were used as ML input. Unsupervised ML was used to create a representation of patients with hypertension in a virtual space in which patients are positioned on the basis of the similarity of their integrated whole-cardiac cycle echocardiography data. Regression methods were used to explore patterns of echocardiographic traces within this virtual ML-derived space, while clustering was used to define phenogroups. RESULTS The algorithm captured different patterns in tissue and blood-pool velocity and deformation profiles and integrated the findings, yielding phenotypes related to normal cardiac function and others to advanced remodeling associated with pressure overload in hypertension. The addition of individuals without hypertension into the ML-derived space confirmed the interpretation of normal and remodeled phenotypes. CONCLUSIONS ML-based pattern recognition is feasible from echocardiographic data obtained during the whole cardiac cycle. Automated algorithms can consistently capture patterns in velocity and deformation data and, on the basis of these patterns, group patients into interpretable, clinically comprehensive phenogroups that describe structural and functional remodeling. Automated pattern recognition may potentially aid interpretation of imaging data and diagnostic accuracy.
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Affiliation(s)
- Filip Loncaric
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain.
| | - Pablo-Miki Marti Castellote
- Department of Information Technologies and Communication, Simulation, Imaging and Modelling for Biomedical Systems, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Dora Fabijanovic
- University of Zagreb School of Medicine, Department of Cardiovascular Diseases, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Loredana Nunno
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Maria Mimbrero
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Laura Sanchis
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Adelina Doltra
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Silvia Montserrat
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; CIBERCV, Instituto de Salud Carlos III (CB16/11/00354), Madrid, Spain
| | - Maja Cikes
- University of Zagreb School of Medicine, Department of Cardiovascular Diseases, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Fatima Crispi
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases, Barcelona, Spain
| | - Gema Piella
- Department of Information Technologies and Communication, Simulation, Imaging and Modelling for Biomedical Systems, Universitat Pompeu Fabra, Barcelona, Spain
| | - Marta Sitges
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; CIBERCV, Instituto de Salud Carlos III (CB16/11/00354), Madrid, Spain
| | - Bart Bijnens
- Institute of Biomedical Research August Pi Sunyer, Barcelona, Spain; ICREA, Barcelona, Spain
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