DOI
https://doi.org/10.25772/0N86-2W68
Defense Date
2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Biostatistics
First Advisor
Roy T. Sabo, Ph.D.
Second Advisor
Robert Perera, Ph.D.
Third Advisor
Nitai Mukhopadhyay, Ph.D.
Fourth Advisor
Amir Toor, M.D.
Fifth Advisor
Robert Kirkpatrick, Ph.D.
Abstract
Predicting the trajectory of lymphoid recovery following myeloablative hematopoietic stem cell transplantation (SCT) can help guide subsequent therapeutic decisions, since poor recovery has been associated with graft-versus-host disease (GVHD), relapse and mortality. Previous attempts at classifying patients depended on absolute criteria being set prior to modeling absolute lymphocyte counts (ALCs) over time. Having an empirical clinical decision support tool for objectively determining the trajectory an individual might take during their recovery would be advantageous. We propose using growth-based trajectory modeling (GBTM) and growth mixture modeling (GMM), which utilize machine learning algorithms to empirically identify latent groupings of data. Due to lymphocyte reconstitution having complicated and decidedly non-linear longitudinal trends, cubic B-splines were used in place of the polynomials traditionally used in the GBTM and GMM approaches due to their flexibility and ability to fit complex trends. Our methods rely on objective measurements to prospectively predict the course an individual might take following stem cell transplantation. The adapted GBTM and GMM methods classified existing patients into groups with varying rates and magnitudes of lymphoid recovery into distinct groups of trajectories and we demonstrated how this method could be used in practice to prospectively identify lymphoid recovery of a new patient. Our method has the potential to aid clinicians in identifying patients who may be predisposed to varying clinical outcomes based on immune reconstitution, so the immunosuppression intensity may be appropriately adjusted.
Rights
© Brielle A. Forsthoffer
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
7-30-2021