Clinic and Polyclinic for Psychiatry and Psychotherapy, Technical University of Munich, Germany, and Zucker Hillside Hospital, Albert Einstein College of Medicine, New York, USA
Zucker Hillside Hospital, Albert Einstein College of Medicine, New York, USA
Clinic and Polyclinic for Psychiatry and Psychotherapy, Technical University of Munich
Psychiatric Clinic, Ludwig Maximilians University, Munich, Germany
Correspondence: PD Dr Stefan Leucht, Klinik fürr Psychiatrie und Psychotherapie derTechnischen Universität München, Klinikum rechts der Isar, Ismaningerstrasse 22, 81675 München, Germany. Tel: +49 89 414 0 4249; e-mail: Stefan.Leucht{at}lrz.tu-muenchen.de
Declaration of interest None. Funding detailed in Acknowledgements.
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ABSTRACT |
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Aims To link the BPRSto Clinical Global Impression (CGI) ratings.
Method Equipercentile linking of BPRS and CGI ratings from seven drug trials in acutely ill patients with schizophrenia (n=1979).
Results Mildly ill according to the CGI approximately corresponded to a BPRS total score of 31, moderately illto a BPRS score of 41andmarkedlyillto a BPRS score of 53.Minimally improvedaccording to the CGI score was associated with percentage BPRS reductions of 24, 27 and 30% at weeks1, 2 and 4, respectively. The corresponding numbers for a CGIrating of much improved were 44, 53 and 58%.
Conclusions The results provide a clearer understanding of how to interpret BPRS total and percentage reduction scores in clinical trials with patients acutely ill with schizophrenia who are experiencing positive symptoms.
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INTRODUCTION |
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METHOD |
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Statistical analysis
An often-used, but nevertheless inadequate, method to compare scores would
have been to regress BPRS scores on CGI scores or vice versa. Both measures
showed only median high correlations (see Results) and, therefore, regression
equations would give different results depending on the direction of the
regression equation. Linear regression treats one scale as the independent
variable measured without error and the other as the dependent variable
measured with error. This is conceptually wrong, because both variables are
measured with random error. Within the psychometric literature the search for
corresponding points on different, but correlated, measurement devices is
referred to as linking
(Linn, 1993) or, in its most
strict sense, as equating
(Kolen & Brennan, 1995).
For this study we used equipercentile linking, a technique that identifies
those scores on both measures that have the same percentile rank. We used the
SAS program EQUIPERCENTILE (Price et
al, 2001), a realisation of the algorithms described by Kolen
& Brennan (1995). In the
first step, percentile rank functions are calculated for both variables. Using
the percentile rank function of one variable and the inverse percentile rank
function of the other, one then finds for every score of one variable a score
on the other variable that has the same percentile rank. The exact formulae
are described in Chapter 2 of Kolen & Brennan
(1995). With regard to our
large database, no smoothing was applied, either to the cumulative
distribution functions or to the resulting linking functions. Only evaluations
at baseline and at weeks 1, 2 and 4 were analysed, because although the
duration of the studies ranged from 4 weeks to 51 weeks not all studies
provided data for other time points, so that trial effects could have biased
the results. For each linking task we included all patients with valid values
on both measures, because analysing the data only of those who completed the
studies would have implied a selection. However, approximately 20% of the
patients withdrew between baseline and week 4. In a sensitivity analysis we
therefore included only patients who were still in the studies at week 4, so
that a rating was available at each time point. With the exception of a
somewhat more notable variation concerning the association between the
CGII ratings much worse/very much worse and percentage BPRS worsening
of up to 46% BPRS points, the results were so similar that only those
of the primary analysis are shown.
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RESULTS |
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Linking of CGIS score and BPRS total score
Figure 1 shows the result of
the linking between CGIS rating and the BPRS total score at baseline
and at weeks 1, 2 and 4. They suggest that being considered mildly
ill on the CGI (CGIS score 3) approximately corresponded to a
BPRS total score of 32 at baseline and at week 1 and a total score of 30 at
weeks 2 and 4. Being considered moderately ill (CGIS
score 4) corresponded to BPRS total scores of 44 at baseline and 40 at weeks
1, 2 and 4. Markedly ill (CGIS score 5) corresponded to
BPRS scores of 55 at baseline, 53 at weeks 1 and 2, and 52 at week 4.
Severely ill (CGIS score 6) corresponded to BPRS scores
of 70 at baseline and 68, 67 and 65 at weeks 1, 2 and 4, respectively.
Extremely ill (CGIS score 7) corresponded to BPRS scores of 85 at
baseline and 89, 84 and 88 at weeks 1, 2 and 4, respectively. Thus, the
results were relatively consistent over the four time points examined,
although there was a slight tendency that, for a given BPRS score, CGI ratings
were somewhat less severe at baseline and became more severe during the course
of the treatment. This effect, however, was neither large nor always
consistent.
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Linking of CGII score and percentage BPRS change from baseline
Figure 2 shows the linking
function between the CGII scale and the percentage BPRS change from
baseline at weeks 1, 2 and 4. Ratings of minimally improved
(CGII score 3) at weeks 1, 2 and 4 corresponded to percentage BPRS
reductions of 23, 27 and 30%, respectively. Ratings of much
improved (CGII score 2) corresponded to percentage BPRS
reductions of 44, 53 and 58% at weeks 1, 2 and 4, respectively. Ratings of
very much improved (CGII score 1) corresponded to
percentage BPRS reductions of 71, 79 and 85% at weeks 1, 2 and 4,
respectively. Thus there was a consistent time effect indicating that a
smaller percentage change in BPRS total score was necessary for a patient to
be considered improved 1 week after the initiation of treatment than at later
time points. This effect is also seen for the no change rating
according to the CGII (score 4), which was linked with a 5% BPRS score
reduction at weeks 1 and 2 and an 8% reduction at week 4.
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DISCUSSION |
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These results are relevant not only for the readers of publications on antipsychotic drugs, but also for the definition of response criteria of future trials: considering that a 25% BPRS score reduction means that the patient is just minimally better compared with baseline, this criterion might be a useful cut-off for studying patients with treatment-refractory disease, but not for the average patient. In treatment-refractory cases even a small improvement in symptoms might be clinically important. However, in acutely ill patients with non-refractory conditions, a 50% criterion (i.e. clinically much improved) would seem to be a more appropriate reflection of clinically meaningful improvement, because such patients usually respond well to antipsychotic drugs (Cole, 1964). Considering only a 25% reduction (i.e. only minimally improved) of the overall symptoms as a response would probably not meet clinicians expectations of drug treatment and would be of questionable clinical importance. In contrast to our findings, recent antipsychotic drug trials in patients with acute exacerbations often used a 20 or 30% criterion to distinguish between responders and nonresponders (Marder & Meibach, 1994; Arvanitis et al, 1997; Small et al, 1997). Ironically, the 20% cut-off level was indeed initially used in a study of patients with refractory disease (Kane et al, 1988), but was subsequently widely applied in studies of non-refractory cases.
The main strength of our analysis is the large number of patients, which should make the results rather robust. However, a number of limitations of our analysis must be considered. Despite the widespread use of the CGI in drug trials, there have been only a few studies of its psychometric characteristics, so the CGI is certainly not an ideal measure for evaluating the BPRS. In 116 patients with panic disorder and depression, Leon et al (1993) found good concurrent validity and sensitivity for change using the CGI. In two trials, Khan et al (2002, 2004) showed that the sensitivity of the CGIS and CGII was similar to that of the MontgomeryÅsberg Depression Rating Scale (Montgomery & Åsberg, 1979) and the Hamilton Rating Scale for Depression (Hamilton, 1960). However, Beneke & Rasmus (1992) criticised the CGI on semantic (e.g. asymmetric scaling), logical (e.g. non-meaningful combinations of CGIS and CGII ratings) and statistical grounds (e.g. relatively low testretest reliability in a heterogeneous sample of patients with schizophrenic, depressive and anxiety disorders).
Although the algorithms for linking and equating are the same, the terms have different meanings. For example, equating two forms of a college admission test is done to assure that both forms can be used interchangeably and provide the same decision. In our application the meaning is far less rigorous as the instruments differ, showing correlation coefficients for the CGIS v. BPRS total score comparison of 0.600.76 in weeks 1 to 4 and of only 0.400.41 at the baseline measurement. Linking is thus best understood here as a kind of anchoring that helps in understanding the clinical meaning of a given scale score. The correlation at baseline was especially low. This may in part be explained by the minimum of symptoms required at baseline by most studies, so that variability was reduced, accounting for the relatively low correlation.
From a purely statistical point of view, correlating an implicit difference rating (CGII rating) with an explicit, calculated percentage improvement score is problematic. It was nevertheless reassuring that these two measures showed higher correlations than the severity scores themselves, thus demonstrating that clinicians are able to give meaningful differential global ratings reflecting something like a relative amount of change. There was a time effect in the percentage BPRS reduction, suggesting that a somewhat smaller objective percentage change as measured by the BPRS was necessary for patients to be considered improved according to the CGII at 1 week after the initiation of treatment than at later weeks. This result probably reflects physicians expectations, which may be lower after short durations of treatment than at later stages. Whereas the investigators received training in BPRS rating before the trials, this was usually not the case for the CGI. Interrater reliabilities for the BPRS between 0.87 and 0.97 have been reported (Collegium Internationale Psychiatrae Scalarum, 1996). A small study reported interrater reliabilities for the CGIS and the CGII of 0.66 and 0.51, respectively (37 physicians rating 12 patients with dementia; Dahlke et al, 1992). Recently a somewhat better-anchored CGI scale for patients with schizophrenia has been developed (the Clinical Global Impression Schizophrenia scale) and its validity and reliability have been verified: the interrater reliability was 0.75 (Haro et al, 2003). A replication with this new scale would be useful. Such data could also show that a more objective measure of clinical psychopathology might be obtained by raters who were masked to which week of participation the patient is in.
It is important to emphasise the nature of the patients involved, as the results might not be the same when different patient populations are analysed. We assembled a data-set composed of people suffering from acute exacerbations of schizophrenia with positive symptoms. For example, in patients suffering only from negative symptoms, the relationship between the BPRS and the CGI Severity scale might be very different. Such patients could be considered severely ill according to the CGI, but would have relatively low BPRS total scores owing to a lack of positive symptoms. Similarly, a 50% BPRS reduction might have a different clinical meaning in patients with low baseline BPRS scores. We therefore hasten to emphasise that our results relate only to acutely ill patients with schizophrenia with positive symptoms similar to those included in our database.
Despite these limitations, we consider that the results are an important contribution to a better understanding of the clinical meaning of the BPRS total score and percentage BPRS change in score in acutely ill patients with schizophrenia. Future studies should examine other patient populations (e.g. patients with residual schizophrenia and predominant primary negative symptoms) and should use anchored versions of the CGI and specifically trained raters. In addition, efforts are under way to develop criteria for remission that could be applied to schizophrenia and used in evaluating treatment effects in a more objective and consistent fashion (Andreasen et al, 2005).
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication September 14, 2004. Revision received January 14, 2005. Accepted for publication January 28, 2005.
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