P276
USE OF CONTROL CHARTS AND LINEAR REGRESSION ANALYSIS TO ALLOW
EARLIER RECOGNITION OF RENAL TRANSPLANT DYSFUNCTION
Parry, G, Cox, V, McGee, D, Stratton, J, Johnson, P
Royal Cornwall Hospitals Trust
PROBLEM: Kidney transplantation is the optimal treatment for patients with kidney failure.
Immunosuppression is necessary to prevent transplant rejection, but medication can cause
chronic graft injury. This is usually detected by decline of GFR. Earlier detection of decline of
GFR may allow earlier implementation of strategies to slow or halt this decline.
Control charts are widely used in industry to monitor many diverse processes. They allow
recognition of a change in the process from natural variation and highlight significant changes
(special cause variation). Control limits are based on the natural variation within the process.
Linear regression models attempt to relate a group of measurements, such as GFR values, to one
or more variables, in this instance, increasing time since transplant.
PURPOSE:The aim of this study was to identify whether use of control charts and/or linear
regression gave earlier recognition of graft dysfunction than routine clinical practice.
DESIGN: All patients under the care of RCHT with a renal transplant were considered for
study. Data was tested for normality and a best-fit linear regression line applied. Control charts
were based on individual eGFR readings, moving range and control chart limits were calculated
from point to point readings.
FINDINGS:207 patients were considered for study. 64 were excluded (deceased or nonfunctioning graft n=25, less than six values for eGFR available n=6, transferred out n=31,
paediatric patients n=2). 143 patients were studied.
Regression analysis results: 23 (16%) of the 143 patients were shown to have declining renal
function, evident by a graph adherent to >35% of the values for eGFR and a negative slope.
In 10 (7%) patients the decline in renal function had not been noted during routine clinical
review.Control chart results: 24 (13%) patients were found to be ‘in-control’, indicating only
natural variation was present whilst 46 (25%) patients were ‘out of control’ indicating changes
in function over time, the other 114 (62%) could not be analysed due to out of control MR
charts, suggesting significant events causing large variations.Combination results: Of the 24 ‘in
control’ only 1 had declining function by regression analysis. Of the 46 ‘out of control’ 9 had
had declining function by regression analysis.
DISCUSSION: Regression analysis is able to look for trends over time but is not as sensitive as
control charts in detecting point to point variation. Control charts are a sensitive tool which can
identify when, what appears to be natural variation is actually a trend, or when variation in graft
function becomes larger or erratic. However lots of variation causes them to be unreadable. As
linear regression is not as sensitive to small variations in the process, this makes it useful for
identifying long term changes that may otherwise be missed.
CONCLUSION: Regression analysis revealed 23 (16%) patients with declining renal function
10 (7%) of which had not been noted during routine clinical review. Control charts provide a
visual tool to help review results and in this study 46 (25%) of patients had results that were not
explained by natural variation. Of these 9 had declining function as identified by regression
analysis. The remaining variation is likely to be explained by intercurrent events. This study
shows that control charts and regression analysis provide further tools to aid in assessment of
graft function.