Journal of Rehabilitation Medicine 51-1CompleteIssue | Page 11
8
J. Oosterhaven et al.
disability with dropout compared with other studies.
The patients who dropped out in this study had lower
levels of pain intensity and reported less disability
than the completers (28). However, with regard to the
unusually high dropout rate of 51% compared with
10–23% in the other studies (24, 30–33), the results
of this study may be related to unique elements in this
study (poor selection for treatment or inexperienced
therapists). Therefore, we may conclude that higher
levels of pain intensity and higher severity of disability
are predictors for dropout that are worthwhile for future
research (24, 29, 33).
Since all studies in this review investigated different
combinations of potential predictors, it might not be
surprising that conflicting results were demonstra-
ted, for instance, for age, depression, pain intensity,
disability and other predictors. Another explanation
may be the heterogeneity in study populations due to
differences in study design and differences in type of
chronic pain treatment (28, 31–33)). The 3 types of
interventions identified in this review differed in main
programme goal (return to work vs pain management),
duration of the programme (4–20 weeks) and selec-
tion criteria for the study population. Depending on
in which country the study was performed (USA or
in European countries), differences in the organiza-
tion of the healthcare system and referral and funding
patterns might have also caused differences in study
populations.
Study limitations
The results of this systematic review have to be in-
terpreted in the light of some limitations. First of all,
the finding that the methodological quality was con-
sidered to be low in all 8 studies (24, 27–33). Quality
assessment was a difficult process caused by the lack
of consistent and clear reporting of study- and dropout
characteristics. Due to a serious risk of bias in most
studies for: study attrition and statistical reporting and
analysis, the internal validity of the evidence found in
this systematic review can be considered to be low.
This was probably caused by the retrospective design
of most cohort studies (24, 27–33).
Because the lack of conceptualization and operatio-
nalization of dropout, the methodological quality of the
domain of study attrition of the QUIPS was difficult to
judge. Only 4 of the 8 studies provided a definition of
dropout (24, 29, 32, 33). This may have influenced the
internal validity of this review. The huge differences in
dropout rates (10–51%) found in this systematic review
can partly be explained by the variation in definitions
that were used to describe dropout. A high risk of bias
was found for the statistical analysis and reporting
www.medicaljournals.se/jrm
for 4 out of 8 studies in this review. Judgement of the
statistical results in univariate analyses was most often
based on the description in the methods sections of the
original articles, as the results were not reported. The
statistical analyses in these studies followed a more
data-driven approach, which often tend to give a too
optimistic estimate of performance of the prediction
model and may cause overfitting of the data (36).
Only 3 studies reported statistical analyses based on a
conceptual framework (28, 29, 31).
The results in this review may suggest that dropout
is entirely a patient characteristic (see predictors in the
sociodemographic, patient and disease domains). How
ever, as demonstrated in the mental health literature,
the experience level of therapists and the therapeutic
alliance may be important moderators in association
with dropout (10, 12, 13). In this literature it has been
recommended that it may be more relevant to focus
on the interaction between patient-related predictors
and therapist/therapy-related predictors and not on a
single key predictor alone (10, 12, 13).
Judging the available evidence in this review, strong
limitations concerning the external validity may be
raised. A factor that will influence the generalizability
of the multiple logistic regression models for dropout
presented in this review is related to the differences in
chronic pain treatments in the studies. The evidence
found for predictors in the 3 different interventions first
has to be validated outside the context in which it was
gathered (19). For example, most predictors associated
with dropout were found in a large prospective cohort
study within a functional restoration programme in
the USA. These findings may not be generalized to
other contexts (24). Another factor that may limit the
generalizability of the predictors found in the RCT (33)
is the fact that RCTs are known to attract very highly
motivated patients, which does not reflect patients with
low treatment motivation, who are often seen in daily
clinical practice.
It is remarkable that none of the multiple logistic re
gression models that were identified in this review were
tested in independent samples. Currently, there is little
information available on the performance of the mul-
tiple logistic regression models. Three parameters can
be described to gain more insight in the performance of
the logistic regression models: calibration, discrimina-
tion and clinical usefulness (37, 38). Only calibration
was described with the goodness-of-fit for 2 multiple
logistic regression models in 2 studies (28, 30). Due to
the abovementioned limitations, the decision was made
not to perform a best-evidence synthesis (25, 26), but
to systematically generate a broad overview of all po-
tential predictors found in the literature associated with
dropout in chronic pain management programmes. The