Clustering Predictors of Liver Transplant Survival
2016-08
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Clustering Predictors of Liver Transplant Survival
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2016-08
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Transplantation has been increasingly applied as a treatment of choice for severe and end-stage liver diseases. In 2013, there were 25,050 liver transplants worldwide, and the United States (US) accounted for 6,724 cases, the highest number of cases per year in one country. There is considerable research investigating recipient characteristics, organ donor characteristics, management of the allocation and transportation of organs, transplant center related factors, and sociocultural and economic factors. However additional risk factors are needed that address a holistic view of transplant patients and components of their health that address liver transplant heterogeneity. Data analysis knowledge and novel statistical approaches, such as data mining techniques, can be used to manage a wide variety of data and discover unique groups within the liver transplant population. The purpose of this research was to discover predictors for the survival of patients receiving liver transplant. The adopted novel methodology using clustering analysis showed a way to manage large amount of data and incorporate into a single study, and findings have the potential to inform future work regarding liver transplant recipient heterogeneity and enable precision medicine initiative. Results showed that, with novel methodology and severity score building, a hierarchical clustering analysis was useful to identify clusters of patients with similar characteristics that are predictive of risk for mortality. The study showed a new approach to analyze heterogeneous and high dimensional transplant data, and that with the combination of data analysis and statistical approaches, turn possible to model a broad array of risk factors in a single model. Finally, this research showed a novel approach in incorporating health-related quality of life before transplantation as a risk factor, and showed that it is predictive of patient survival after transplantation. Results showed that patients with better health-related quality of life (HRQoL) had a greater survival after liver transplantation, and that HRQoL can be used to estimate the effect on all-cause mortality after transplantation. This research supports the need for additional research to investigate whether improving quality of life while recipients are on the waiting list can achieve better survival after transplantation and inform liver allocation process.
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University of Minnesota Ph.D. dissertation. August 2016. Major: Nursing. Advisors: Bonnie Westra, Karen Monsen. 1 computer file (PDF); 113 pages.
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Pruinelli, Lisiane. (2016). Clustering Predictors of Liver Transplant Survival. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/218063.
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