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Using machine learning to characterize heart failure across the scales

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Abstract
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain 52.7% of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.
Keywords
Biotechnology, Mechanical Engineering, Modelling and Simulation, Machine learning, Gaussian process regression, Bayesian inference, Uncertainty quantification, Heart failure, Growth and remodeling, Multiscale, CARDIAC GROWTH, FINITE GROWTH, DISTRIBUTIONS, ADAPTATION, HYPERTROPHY, MYOCYTES, STRESS

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Citation

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MLA
Peirlinck, Mathias, et al. “Using Machine Learning to Characterize Heart Failure across the Scales.” BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, vol. 18, no. 6, 2019, pp. 1987–2001, doi:10.1007/s10237-019-01190-w.
APA
Peirlinck, M., Sahli Costabal, F., Sack, K. L., Choy, J. S., Kassab, G. S., Guccione, J. M., … Kuhl, E. (2019). Using machine learning to characterize heart failure across the scales. BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, 18(6), 1987–2001. https://doi.org/10.1007/s10237-019-01190-w
Chicago author-date
Peirlinck, Mathias, F. Sahli Costabal, K. L. Sack, J. S. Choy, G. S. Kassab, J. M. Guccione, M. De Beule, Patrick Segers, and E. Kuhl. 2019. “Using Machine Learning to Characterize Heart Failure across the Scales.” BIOMECHANICS AND MODELING IN MECHANOBIOLOGY 18 (6): 1987–2001. https://doi.org/10.1007/s10237-019-01190-w.
Chicago author-date (all authors)
Peirlinck, Mathias, F. Sahli Costabal, K. L. Sack, J. S. Choy, G. S. Kassab, J. M. Guccione, M. De Beule, Patrick Segers, and E. Kuhl. 2019. “Using Machine Learning to Characterize Heart Failure across the Scales.” BIOMECHANICS AND MODELING IN MECHANOBIOLOGY 18 (6): 1987–2001. doi:10.1007/s10237-019-01190-w.
Vancouver
1.
Peirlinck M, Sahli Costabal F, Sack KL, Choy JS, Kassab GS, Guccione JM, et al. Using machine learning to characterize heart failure across the scales. BIOMECHANICS AND MODELING IN MECHANOBIOLOGY. 2019;18(6):1987–2001.
IEEE
[1]
M. Peirlinck et al., “Using machine learning to characterize heart failure across the scales,” BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, vol. 18, no. 6, pp. 1987–2001, 2019.
@article{8622189,
  abstract     = {Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain 52.7% of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.},
  author       = {Peirlinck, Mathias and Sahli Costabal, F. and Sack, K. L. and Choy, J. S. and Kassab, G. S. and Guccione, J. M. and De Beule, M. and Segers, Patrick and Kuhl, E.},
  issn         = {1617-7959},
  journal      = {BIOMECHANICS AND MODELING IN MECHANOBIOLOGY},
  keywords     = {Biotechnology,Mechanical Engineering,Modelling and Simulation,Machine learning,Gaussian process regression,Bayesian inference,Uncertainty quantification,Heart failure,Growth and remodeling,Multiscale,CARDIAC GROWTH,FINITE GROWTH,DISTRIBUTIONS,ADAPTATION,HYPERTROPHY,MYOCYTES,STRESS},
  language     = {eng},
  number       = {6},
  pages        = {1987--2001},
  title        = {Using machine learning to characterize heart failure across the scales},
  url          = {http://dx.doi.org/10.1007/s10237-019-01190-w},
  volume       = {18},
  year         = {2019},
}

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