Model-based analysis of the graft liver recovery in the neo-hepatic period

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Dr. Benyó Balázs István
Department of Control Engineering and Information Technology

The liver plays a critical role maintaining metabolic balance in the body, the primary function is, to ensure constant blood glucose level. In critically ill patients stress-induced hyperglycemia is often experienced .This glycemic variability and elevated blood glucose are associated with increased mortality. In these cases tight glycemic control (TGC) is necessary and can reduce the rate of negative outcomes.

I studied the ICING (Intensive Control Insulin Nutrition Glucose) model, which was evaluated for the liver transplantation patients’ data. By them, in the absence of the liver the well-known metabolic pathways are upset, which can’t be described by the original model. One goal of this study was to fit the model to the changed states, and to make a specific model for liver transplanted patients.

In previous studies I got information of the measurements of the liver function. In this project I suppose, that one of the model’s parameter: the insulin sensitivity (SI) can give more significant information of the donor liver (graft) function. If the value of this parameter is not restricted from above and can move to a non- physiological range, we receive a new analyzing parameter, which can describe the status of the patient. The SI values were displayed on graphs. Next to it a clustering analysis were evaluated for the patient cohort data. I used some types of clustering methods, where the optimal number of the groups were estimated by the average distance of the clusters.

Based on the results of the analysis we received an extra information about the grafted liver function. We indexed the data points in the clusters and based on the indices we were able to divide each patient’s SI function based on the index of the clusters. My further plans are to collect more circumstantial data from these patients and the donors, to conclude these information and find correlation with the parameters defined above.


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