Chapter 3 Methodology
3.7 Data used for the Sub-Models
3.7.1 Description of the Data
Assessment Stage Data
Information about patients who are referred to a liver unit is only available from that individual liver transplant unit. The data analysed for this thesis were collected from Birmingham Liver Unit (BLU), and consist of all adult patients (aged 16 and above) who were referred to BLU between 1st January 1999 and 31st December 2002. Of interest was whether or not they had been given a place on the waiting list. These data were used to develop an understanding about which patients are likely to be placed on the waiting list following referral to a liver unit, and whether certain patient characteristics play a significant part in the decisions made.
Transplant Data
Information about all patients who join the waiting list is available from UK Transplant. For this thesis, data was obtained for all adult patients (aged 17 or older) who joined the UK liver transplant waiting list between 1st January 1999 and 31st December 2002. Details of their patient history were recorded up to 6th October 2003.
Information about liver donors is also available from UK Transplant. Data were provided on all donors whose livers were transplanted into adult patients between 1st January 1999 and 31st December 2002.
Methodology
These data were used to develop an understanding about the life expectancy of patients on the liver transplant waiting list, and which patient characteristics are the strongest predictors of this. Similarly, the data were also used to estimate life expectancy post-transplant based on patient, donor, and transplant characteristics.
3.7.2 Risk Factors
In order to model patient and donor arrivals, and patient transitions it is necessary to determine the arrival distributions, activity time distributions, and the risk factors that drive these. An initial list of risk factors was determined by looking at previous literature, obtaining the opinions of medical experts, and researching how the system currently operates by consulting UKT documentation (UK Transplant 2001a). The factors considered were then restricted to data attainable from UKT and BLU and covered 4 main categories: Patient Demographics, Donor Related Measures, Transplant Related Measures, and Clinical Measures. The risk factors identified were different for the pre- waiting list and post-waiting list models, and are explained in more detail below.
Assessment Stage Risk Factors
The factors considered in pre-transplant models are summarised in Table 3.8. The first factor considered is primary liver disease, since different diseases will affect patients over various timescales. A study analysing the French liver transplant waiting list identified transplant urgency and region (geographical
Methodology
location of the patient) as being key predictors in determining the life expectancy of a patient on the waiting list (Suc et al. 2000).
The MELD score, measures the severity of illness for chronic liver disease sufferers and it is currently used in the US to determine who should receive a donated liver (Wiesner et al. 2003). The score replaces the Child-Turcotte-Pugh score which had been used previously (Wiesner et al. 2001). The MELD score relies on more objective measures (which are based on clinical tests) and has been found to accurately predict short-term mortality from liver disease (Kamath
et al. 2003), and more accurately than the Child-Turcotte-Pugh score (Wiesner
et al. 2003). The MELD score has also been found to be successful at predicting mortality across a range of chronic liver diseases (Said et al. 2004). Other factors considered include patient gender (Fink et al. 2007), and patient age.
Table 3.8 Pre-Transplant Risk Factors Considered in the Analysis.
Pre-Transplant Risk Factors
Patient and Disease Related Characteristics
Primary Liver Disease at Registration Transplant Urgency
MELD score Gender
Patient Age at Registration
Geographical Location Centre
Transplant Risk Factors
Post-transplant risk factors also include donor characteristics and transplant characteristics. The requirement for this thesis is to investigate medium to long- term survival of patients post-transplant, since we want to determine the effect of a policy on the total number of life years gained. Many studies have previously concerned themselves with determining short-term outcomes (Avolio
Methodology
et al. 2004). Studies have been performed to identify factors affecting post- transplant survival (Adam et al. 2000; Angelis et al. 2003; Gonzalez et al. 1994; Lin et al. 1998).
Table 3.9 Post-Transplant Risk Factors Considered in the Analysis.
Post-Transplant Risk Factors
Patient Characteristics
First Transplant
Primary Liver Disease at Registration Recipient Age
Transplant Urgency
Recipient Body Mass Index (BMI) Recipient Gender
Clinical Characteristics MELD score
Donor Characteristics Donor Age
Donor Cause of Death
Transplant Characteristics
Donor-Recipient ABO match Cold Ischaemic Time (minutes) Completeness of Liver Used Donor-Recipient Rhesus match Donor-Gender to Recipient-Gender Donor Weight minus Recipient Weight
These studies have identified various factors as key to post-transplant survival. Those which have been identified as appropriate to this work are summarised in Table 3.9. Previous studies have also found that transplant centre-related factors, such as the number of liver transplants performed are significant in determining outcomes (Adam et al. 2000). It was decided, however, that there are too few transplant centres in the UK – seven – to be able to make a reliable assessment of this. Other variables that may influence post transplant survival, but were not considered in our analysis due to a lack of data, included: Donor Type (Cadaveric Heartbeating, Cadaveric Non-Heartbeating, Living (related/unrelated), Domino).
Methodology
Some people would expect that the time spent waiting for a liver transplant would be an indicator as to when a patient will die, however it has been shown (Freeman et al. 2000) that waiting time provides a poor indication of actual death time, mainly due to patients joining the waiting list at varying stages of their disease, and so this covariate is not considered in the analysis.
Simplification of Variables
Some of the factors listed in Tables 3.8 and 3.9 took on a number of different values and so had to be simplified. Appendix A lists all the diseases that are indications for transplantation (this list was obtained from UK Transplant). It also shows the groupings which indicate how the diseases were aggregated and simplified so that they could be used in this analysis. The disease groupings implemented were based on a Liver Advisory Group paper (Hudson et al. 2005), amended by splitting the Cirrhotic diseases group into smaller groups so that the groups were more comparable in size. The groupings also reflected the disease categories which have been identified as important because their occurrence is likely to increase in the future (Section 1.2.6). Other risk factors which were simplified, are: donor cause of death, MELD score, and patient body mass index (as outlined in Appendix A).