Thomas Hangartner (Committee Member), Yan Liu (Advisor), Pratik Parikh (Committee Member)
Master of Science in Engineering (MSEgr)
Gaucher disease (GD) is a monogenic disorder with autosomal recessive inheritance, which results from an acid lysosomal hydrolase, the beta-glucocerebrosidase deficiency. Clinical manifestations of the disease include anemia, thrombocytopenia, hepatosplenomegaly, and skeletal complications. Enzyme replacement therapy (ERT) has been used to treat type 1 GD for more than a decade, and many patients have shown remarkable clinical responses to the treatment, with normalization of blood counts, reduction in liver and spleen size, and improvement in bone symptoms. Many researchers have tried to study the effectiveness of ERT, but previous research has been mainly based on some predetermined hypotheses and traditional analysis methods, which assumed some statistical distributions of the underlying data. In addition, studies have suggested significant individual differences in patients' bone mineral density (BMD) responses to ERT. In this project, we used non-parametric regression tree methods to analyze the BMD data of patients with type 1 GD, in combination with other potentially relevant parameters, including patients' demographics, hematological, visceral, and bone manifestations, to define a parameter subspace that explains the patients' BMD response. Models have been derived for the patient's initial dual-energy X-ray absorptiometry (DXA) Z-score, the rate of change of the patient's DXA Z-scores from his/her first infusion to the current DXA assessment visit, and the rate of change of the patient's DXA Z-scores between two consecutive DXA assessment visits. Modeling results suggest that the patient's initial DXA Z-score is affected by his/her region, treatment with bisphosphonates, gender, and the period between the patient's first infusion and first DXA visit date. The rate of change of the patient's DXA Z-scores from his/her first infusion to the current DXA assessment visit is mostly related to the patient's region, initial DXA Z-score, and ethnicity. In addition, the most predictive covariate of the rate of change of the patient's DXA Z-scores between two consecutive DXA assessment visits is the patient's immediately previous DXA Z-score.
Department or Program
Department of Biomedical, Industrial & Human Factors Engineering
Year Degree Awarded
Copyright 2012, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.