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Background: Kidney Disease Outcomes Quality Initiative (KDOQI) chronic kidney disease (CKD) guidelines have focused on the utility of using the modified four-variable MDRD equation (now traceable by isotope dilution mass spectrometry IDMS) in calculating estimated glomerular filtration rates (eGFRs). This study assesses the practical implications of eGFR correction equations on the range of creatinine assays currently used in the UK and further investigates the effect of these equations on the calculated prevalence of CKD in one UK region, Methods: Using simulation, a range of creatinine data (30-300 micromol/l) was generated for male and female patients aged 20-100 years. The maximum differences between the IDMS and MDRD equations for all 14 UK laboratory techniques for serum creatinine measurement were explored with an average of individual eGFRs calculated according to MDRD and IDMS < 60 ml/min/1.73 m(2) and 30 ml/min/1.73 m(2). Similar procedures were applied to 712,540 samples from patients > or = 18 years (reflecting the five methods for serum creatinine measurement utilized in Northern Ireland) to explore, graphically, maximum differences in assays. CKD prevalence using both estimation equations was compared using an existing cohort of observed data., Results: Simulated data indicates that the majority of laboratories in the UK have small differences between the IDMS and MDRD methods of eGFR measurement for stages 4 and 5 CKD (where the averaged maximum difference for all laboratory methods was 1.27 ml/min/1.73 m(2) for females and 1.59 ml/min/1.73 m(2) for males). MDRD deviated furthest from the IDMS results for the Endpoint Jaffe method: the maximum difference of 9.93 ml/min/1.73 m(2) for females and 5.42 ml/min/1.73 m(2) for males occurred at extreme ages and in those with eGFR > 30 ml/min/1.73 m(2). Observed data for 93,870 patients yielded a first MDRD eGFR < 60 ml/min/1.73 m(2) in 2001. 66,429 (71%) had a second test > 3 months later of which 47,093 (71%) continued to have an eGFR < 60 ml/min/1.73 m(2). Estimated crude prevalence was 3.97% for laboratory detected CKD in adults using the MDRD equation which fell to 3.69% when applying the IDMS equation. Over 95% of this difference in prevalence was explained by older females with stage 3 CKD (eGFR 30-59 ml/min/1.73 m(2)) close to the stage 2 CKD (eGFR 60-90 ml/min/1.73 m(2)) interface., Conclusions: Improved accuracy of eGFR is obtainable by using IDMS correction especially in the earlier stages of CKD 1-3. Our data indicates that this improved accuracy could lead to reduced prevalence estimates and potentially a decreased likelihood of onward referral to nephrology services particularly in older females.

The Coxian phase-type distribution is a special type of Markov model which can be utilised both to uncover underlying stages of a survival process and to make inferences regarding the rates of flow of individuals through these latent stages before an event of interest occurs. Such models can be utilised, for example, to identify individuals who are likely to deteriorate faster through a series of disease states and thus require more aggressive medical intervention. Within this paper, a two-stage approach to the analysis of longitudinal and survival data is presented. In Stage 1, a linear mixed effects model is first used to represent how some longitudinal response of interest changes through time. Within this linear mixed effects model, the individuals' random effects can be considered as a proxy measure for the effect of the individuals' genetic profiles on the response of interest. In Stage 2, the Coxian phase-type distribution is employed to represent the survival process. The individuals' random effects, estimated in Stage 1, are incorporated as covariates within the Coxian phase-type distribution so as to evaluate their effect on the individuals' rates of flow through the system represented by the Coxian. The approach is illustrated using data collected on individuals suffering from chronic kidney disease, where focus is given to an emerging longitudinal biomarker of interest - an individual's haemoglobin level. [ABSTRACT FROM AUTHOR]

The use of joint modelling approaches is becoming increasingly popular when an association exists between survival and longitudinal processes. Widely recognized for their gain in efficiency, joint models also offer a reduction in bias compared with naïve methods. With the increasing popularity comes a constantly expanding literature on joint modelling approaches. The aim of this paper is to give an overview of recent literature relating to joint models, in particular those that focus on the time-to-event survival process. A discussion is provided on the range of survival submodels that have been implemented in a joint modelling framework. A particular focus is given to the recent advancements in software used to build these models. Illustrated through the use of two different real-life data examples that focus on the survival of end-stage renal disease patients, the use of the JM and joineR packages within R are demonstrated. The possible future direction for this field of research is also discussed. [ABSTRACT FROM AUTHOR]