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Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.
JCO Clin Cancer Inform
September 2024
Memorial Sloan Kettering Cancer Center, New York, NY.
Purpose: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to identify patients at high risk of impaired CRF and poor CRF response to AT.
Methods: We evaluated heterogeneity in CRF among 64 women with metastatic breast cancer randomly assigned to 12 weeks of highly structured AT (n = 33) or control (n = 31).
A deep patient-similarity learning framework for the assessment of diastolic dysfunction in elderly patients.
Eur Heart J Cardiovasc Imaging
June 2024
Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital (RWJUH), Rutgers Robert Wood Johnson Medical School (RWJMS), 1 Robert Wood Johnson Place, New Brunswick, NJ 08901, USA.
Aims: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF).
Methods And Results: A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study.
Machine-learning-based exploration to identify remodeling patterns associated with death or heart-transplant in pediatric-dilated cardiomyopathy.
J Heart Lung Transplant
April 2022
Division of Cardiology, The Labatt Family Heart Center, Hospital for Sick Children, and University of Toronto, Toronto, Ontario, Canada. Electronic address:
Aims: We investigated left ventricular (LV) remodeling, mechanics, systolic and diastolic function, combined with clinical characteristics and heart-failure treatment in association to death or heart-transplant (DoT) in pediatric idiopathic, genetic or familial dilated cardiomyopathy (DCM), using interpretable machine-learning.
Methods And Results: Echocardiographic and clinical data from pediatric DCM and healthy controls were retrospectively analyzed. Machine-learning included whole cardiac-cycle regional longitudinal strain, aortic, mitral and pulmonary vein Doppler velocity traces, age and body surface area.
Phenogrouping Diastolic Dysfunction by Artificial Intelligence: Learning From What We Teach the Machines.
JACC Cardiovasc Imaging
October 2021
Centre Hospitalier Universitaire Sart Tilman, GIGA Cardiovascular Sciences, University of Liège Hospital, Liege, Belgium; Gruppo Villa Maria Care and Research, Maria Cecilia Hospital, Cotignola, and Anthea Hospital, Bari, Italy.
Echocardiographic phenogrouping by machine learning for risk stratification in the general population.
Eur Heart J Digit Health
September 2021
Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 35, Block D, Box 7001, B 3000 Leuven, Belgium.
Aims: There is a need for better phenotypic characterization of the asymptomatic stages of cardiac maladaptation. We tested the hypothesis that an unsupervised clustering analysis utilizing echocardiographic indexes reflecting left heart structure and function could identify phenotypically distinct groups of asymptomatic individuals in the general population.
Methods And Results: We prospectively studied 1407 community-dwelling individuals (mean age, 51.
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