Het volume van chirurgische ingrepen en de impact ervan op ... - KCE

216 Volume Outcome KCE reports 113
• In cardiology, the Belgian Working Group for Interventional Cardiology
(BWGIC) disposes of data for CABG and PCI, and, although we did not
link MCD data to BWGIC registry due to time constraints, this type of
linkage has already been performed in the past and could be redone.
• For orthopaedic procedures, only very recent the National Institute for
Health and Disability Insurance (NIHDI) started the electronic registry
ORTHOpedic (Prosthesis Identification Data ORTHOpride) which will
include all prostheses of hip and knee. 343 Such initiatives will definitely be
of benefit to the quality of future studies.
We cannot rule out confounding by unmeasured characteristics of patients in our study.
Nevertheless, we do not believe that limitations related to risk adjustment threaten our main
conclusions about the association between volume and outcome.
5. Appropriate statistical methods should be used. Regression models are
available that respect the hierarchical nature of the data (patients nested
within surgeons, surgeons nested within centres), and account for the
correlations within these clusters. The failure to include any type of
adjustment for those correlations would lead to falsely high statistically
significant effects. Generalized Estimating Equations (GEE) and Generalized
Linear Mixed Models (GLMM) and are two examples of methods that can be
used to analyze hierarchical data.
6. The graphical presentation of results. The funnel plot is a good and “easy to
use” tool. It avoids spurious ranking of institutions, spurious stigmatization of
low volume centres, and allows for an informal assessment of any volume
outcome relationship.
7. Sensitivity analyses and robustness of the results. Results should be
transparent. In our study, effects of volume were always presented with and
without adjustment for case mix (based on administrative data only or based
on all clinical data available) so that one can assess the influence of case mix
on the volume effect. Sensitivity analyses were also performed when there
were huge volume outliers, where it is difficult to differentiate the effect of
the volume or other characteristics of that centre.
8. Problem of missing data on cancer stage: on average 30% missing data,
sometimes 40% in small volume hospitals. The fact that mortality rate was
not substantially higher in the patients whose stage was missing in the BCR
seems to indicate, that, in this study, these patients are randomly divided into
the four disease stages. Ideally, though, this assumption should have been
checked with the help of sensitivity analyses, but this was not done due to
time constraints.
In addition, we noticed that many hospitals – low-volume as well as highvolume
– missed data on stage and that the percentage of missing data varied
among these hospitals. Despite the failure to retrieve information on disease
stage, this problem did not restrain us from drawing conclusions on the
volume outcome association. Nevertheless, this problem of missing data on
disease stage (and other variables useful for risk adjustment) supports the
need for complete and accurate data collection.
9. Sample size is sometimes not sufficient in one year: analysis of several years
for rare tumours or procedures (pancreas, oesophagus, heart transplant) is
required.