B15(W)
THE USE OF LONGITUDINAL TECHNIQUES TO IDENTIFY THE
CHARACTERISTICS ASSOCIATED WITH HAEMOGLOBIN VARIABILITY IN
HAEMODIALYSIS PATIENTS
McCrink, L1, Marshall, A1, Cairns, K1, Fogarty, D2,3, Casula, A2
1
Centre for Statistical Science and Operational Research, Queen’s University Belfast
2
UK Renal Registry, Bristol, 3Regional Nephrology Unit, Belfast City Hospital & Queen’s
University Belfast
BACKGROUND: Recent work has noted a negative relationship between the variability of
haemoglobin levels and patient survival [1]. Previous research using UK Renal Registry data
used cross-sectional techniques and determined no decrease in survival with higher
haemoglobin levels [2]. However when coupled with the trials in pre-dialysis patients it is clear
that additional work on the achieved haemoglobin, the haemoglobin variability and the
medications used to achieve these are required to assess any causal effect on survival. The aim
of this research is to use statistical techniques to model haemoglobin variability of
haemodialysis patients and thus explore the causes of this variability as a first step towards this.
METHODS: In this study haemoglobin is modelled longitudinally to enable the separation of
baseline characteristics and time-dependent effects. Therefore trends over time can be analysed
accurately whilst also incorporating the natural variation amongst individuals to increase the
precision of inferences made. A haemodialysis cohort was developed using longitudinal
prevalent UKRR data from 67 centres collected over three years (2005-2008). Analysis was
performed using generalised linear models (within various statistical packages) and multilevel
modelling techniques also [3]. The dataset contains repeated measurements for just over 5,500
individuals with approximately 57,500 observations collected over the period of the study. This
research focuses on the effect of various covariates, including Ferritin, RRT start date and
primary renal disease, on the variation of haemoglobin over time.
RESULTS: We observed that this longitudinal data had complex patterns of variability due to
the dependency between observations within individuals. This alone accounted for 25% of the
variation between haemoglobin levels. Additional modelling using imputation for missing
variables and multilevel modelling techniques is being used to accurately assess the additional
within-individual variations over time.
CONCLUSIONS: Ignoring within-individual variation and using traditional linear regression
techniques may result in misleading inferences for longitudinal data. Future work will need to
depend on more complete covariate and treatment data for RRT patients receiving ESAs and
Iron therapy.
REFERENCES:
[1] Gilbertson, D.T., Ebben, J.P., Foley, R.N., Weinhandl, E.D., Bradbury, B.D. & Collins, A.J.
2008 "Hemoglobin level variability: Associations with mortality", Clin J Am Soc
Nephrol.3:133-8.
[2] Macdougall, I.C., Tomson, C.R., Steenkamp, M. & Ansell, D. 2010, "Relative risk of death
in UK haemodialysis patients in relation to achieved haemoglobin from 1999 to 2005: an
observational study using UK Renal Registry data incorporating 30,040 patient-years of followup", Nephrol Dial Transplant. 25:914-9.
[3] Holden, J.E., Kelley, K. & Agarwal, R. 2008, “Analyzing change: a primer on multilevel
models with applications to nephrology”, American Journal of Nephrology 28; 792-801.