Journal of Rehabilitation Medicine 51-10 | Page 47

Pain catastrophizing and dropout in chronic pain management
with our study. This disparity may be caused by the
inclusion of patients with more severe levels of anxiety
and depression in the study of Howard et al. (35), who
might have been excluded from our prospective cohort
study. Another explanation may be found in differences
in the instruments used in both studies. In the current
study, the HADS was used to measure anxiety and
depression, while the structural clinical interview for
DSM-IV (SCID) and the Beck Depression Inventory
(BDI) were used in the study of Howard et al. (35). The
HADS differs from the SCID and the BDI in not inclu-
ding items regarding somatic and physical functioning.
Taking into account the considerable overlap between
the somatic symptoms of depression and chronic pain,
research has suggested that the BDI may overestimate
the occurrence of depression in patients with chronic
pain (36, 37). In light of this, further research with
validated instruments for anxiety and depression in
patients with chronic pain may reveal whether these
are associated with dropout.
This study demonstrated that pain catastrophizing
was the main predictor of dropout in this IPMP. How­
ever, one other study that investigated catastrophizing
as a predictor for dropout in an IPMP did not find
significant results (4). In Coughlan et al.’s study (4)
pain catastrophizing was measured with a subscale
of the Coping Strategies Questionnaire (these are the
same items as the helplessness domain of the PCS)
not with the PCS. In our study pain catastrophizing
was operationalized as a multidimensional concept
and includes 3 domains: helplessness, rumination, and
magnification.
In this IPMP all completers showed significant im-
provement on all domains of the PCS. The dropouts
scored significantly higher than the completers on all
3 domains of the PCS and for the PCS total score at
the baseline assessments. No statistical analyses could
be performed post-treatment for the dropouts, since
only 6 of the 35 dropouts participated in these assess-
ments. The literature provides different cut-off points
for pain catastrophizing; above 23 and above 30 (38).
The dropouts in our study scored 27.23 vs. completers
20.08, indicating that the dropouts were more likely to
catastrophize than were the completers.
Pain catastrophizing
In the last 3 decades, pain catastrophizing has emerged
as an important construct in the field of pain. It plays
a role in the response to pain and in pain-related disa-
bility, and is a moderator of treatment outcomes (38).
This study has identified pain catastrophizing as a po-
tentially important variable in the prediction of dropout
in IPMPs. Patients with higher pain catastrophizing
767
scores, approaching the clinical relevance cut-off of
30 (scores around 27, the mean in this study) may
need further follow-up by their clinicians to determine
whether these patients are prone to dropout. Strate-
gies can be developed to prevent early dropout from
treatment and in order to tailor pain interventions to
patients who are prone to dropout. Additional research
is needed to investigate whether pain catastrophizing
is an important construct to unravel with regard to
dropout in IPMPs.
Strengths and limitations
A strength of this study is that it is one of the few
prospective cohort studies on the topic of dropout
in IPMPs (3, 35, 39). A further strength is that the
development of the prediction model was based on a
conceptual framework, the E-CSM of Self-Regulation.
However, due to the small sample size and the explo-
ratory nature of this study no firm conclusions could
be drawn on the predictive value of the E-CSM of
Self-Regulation for dropout in IPMPs.
In addition, the prediction model we developed was
internally validated and was considered “good”. Boots-
trap results confirmed those from multiple logistic
regression analyses and identified a relatively low bias.
Furthermore, the performance of the prediction model
was also good, taking into account the calibration
parameter (the good fit of the model being identified
with the Hosmer-Lemeshow test) and the discrimina-
tion parameter (considering the AUC curve).
This prospective cohort study has some limitations
that must be taken into consideration in interpreting the
results. First, the study focused on the development of
a multivariate prediction model for dropout, a type 1b
study according to the TRIPOD statement (40). Due to
the exploratory nature of the study, a relatively small
dropout sample (35 dropouts), and to avoid “fishing”
and the risk of “overfitting”, only one multivariate
prediction model for dropout was fitted, which resulted
in one predictor for dropout: pain catastrophizing. With
regard to the small sample size, a forward method was
applied to select the most significant and clinically
relevant potential predictors for the multiple logistic
regression model. A maximum of 3 out of 7 potential
predictors was required in our model (29). We therefore
elected not to perform a backward stepwise selection
procedure, since this would entail initially entering all
7 predictors. Although efforts were made to evaluate
the performance of our prediction model (with a des-
cription of the calibration and the discrimination of the
model), the clinical usefulness still has to be identified.
In other words, the question arises as to whether the
prediction model for dropout informs healthcare pro-
J Rehabil Med 51, 2019