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A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation
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Zhang, Yan, Briggs, David, Lowe, David Philip, Mitchell, Daniel A., Daga, Sunil, Krishnan, Nithya, Higgins, Rob and Khovanova, N. A. (2017) A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation. Mathematical Biosciences, 284 . pp. 3-11. doi:10.1016/j.mbs.2016.04.008
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Official URL: http://dx.doi.org/10.1016/j.mbs.2016.04.008
Abstract
The dynamics of donor specific human leukocyte antigen (HLA) antibodies during early stage after transplantation are of great clinical interest as they are considered to be associated with short and long term outcomes (graft function and rejection). However, the limited number of such detailed donor-specific antibody (DSA) time series currently available and their diverse patterns have made the task of modelling difficult. Focusing on one typical dynamic pattern with rapid falls and stable settling levels, a novel data-driven model in the form of a third order differential equation has been developed to describe such post-transplant dynamics in DSAs for the first time. A variational Bayesian inference method has been applied to select a model and learn its parameters for 39 time series from two groups of graft recipients, i.e. patients with and without acute antibody-mediated rejection (AMR) episodes. Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups. Eigenvalues have been calculated for each fitting, and phase portraits have been plotted to show the trajectories of the system states for both groups. The results from our previous study with fewer cases have been further confirmed: the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates, which may potentially lead to better laboratory measurement strategy and a better chance of understanding the underlying immunological mechanisms.
| Item Type: | Journal Article | ||||||||||
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| Subjects: | R Medicine > RD Surgery | ||||||||||
| Divisions: | Faculty of Science > Engineering | ||||||||||
| Library of Congress Subject Headings (LCSH): | Kidneys -- Transplantation -- Mathematical models, Eigenvalues | ||||||||||
| Journal or Publication Title: | Mathematical Biosciences | ||||||||||
| Publisher: | Elsevier Science Inc. | ||||||||||
| ISSN: | 0025-5564 | ||||||||||
| Official Date: | February 2017 | ||||||||||
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| Volume: | 284 | ||||||||||
| Page Range: | pp. 3-11 | ||||||||||
| DOI: | 10.1016/j.mbs.2016.04.008 | ||||||||||
| Status: | Peer Reviewed | ||||||||||
| Publication Status: | Published | ||||||||||
| Access rights to Published version: | Open Access | ||||||||||
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