The average waiting time for a transplant is about 4 years, but waits of up to 7 years are not uncommon. On average one Australian dies each week while waiting for a transplant. There are also paradoxical factors impacting on the outcome of dialysis patients such as that of high body mass index being Proteases inhibitor associated with improved survival. A similar reverse epidemiology of obesity has been described in geriatric populations. The ‘reverse epidemiology’ of obesity or dialysis-risk-paradoxes’ need to be considered in the decision-making equation. Efforts
to obtain a better understanding of the existence, aetiology and components of the reverse epidemiology and their role in maintenance dialysis patients remain of paramount importance for future study. Newly
emerging predictors of mortality in the non-dialysis population include a high comorbidity score,[4, 5, 13] functional impairment and acute kidney injury secondary to a sentinel event or events on a background of chronic kidney disease (CKD). A predictive model that comprehensively incorporates variables relevant to the prognostic outcome of the non-dialysis population has yet to be developed. The evaluation of the needs in the Australian population in context to these JNK screening scores must also be considered in the decision-making process and remains and unanswered area requiring investigation. The majority of the models below were specifically designed for the dialysis pathway population. The JAMA Kidney Failure Risk Equation (KFRE) is a predictive model, which uses demographic information and routine laboratory markers of
CKD to predict which patients selleck products with CKD stages 3 to 5 will progress to the need for dialysis. Risk is given as a 5-year percentage risk of progression to ESKD. Population validated for: CKD stages 3 to 5 (c-statistic, 0.917 (95% confidence interval, 0.901–0.933)) Advantages: Uses routine demographic and laboratory markers of CKD (Table 1) The first predictive model to accurately predict CKD progression to ESKD Disadvantages: Awaiting validation in the Australian CKD population Requires a risk calculator available as: ● an Office Excel spreadsheet (http://jama.ama-assn.org.wwwproxy0.library.unsw.edu.au/content/305/15/1553.full.pdf+html) ● smartphone app (http://www.qxmd.com/Kidney-Failure-Risk-Equation) The MCS was adapted from the original Charlson Comorbidity Index to identify the subpopulation of sicker dialysis patients with a 50% 1-year mortality rate. It is a simple scoring system that adds scores for comorbidities to scores for age (Tables 2, 3). Population validated for: Dialysis patients (c-statistic = 0.