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Department of Psychological Medicine, School of Medicine, Cardiff University
Department of Psychiatry, Division of Neuroscience, University of Birmingham
Department of Psychological Medicine, School of Medicine, Cardiff University
Department of Psychiatry, Division of Neuroscience, University of Birmingham
Department of Psychological Medicine, School of Medicine, Cardiff University
Department of Psychiatry, Division of Neuroscience, University of Birmingham
Medical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London
Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff
Correspondence: Professor Nick Craddock, Department of Psychological Medicine, Henry Wellcome Building, Wales School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK. Email: craddockn{at}cf.ac.uk
Funding from the Wellcome Trust, the UK Medical Research Council and GlaxoSmithKline.
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Participants completed the Beck Depression Inventory (BDI)2 and were interviewed using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN),3 which provides detailed information about lifetime psychopathology. Psychiatric and general practice case notes were reviewed. These data were combined to form a written case vignette. Based on this vignette, best-estimate lifetime diagnoses were made according to DSM–IV criteria.4 Vignettes were also used to rate key clinical variables (such as age at onset, and number and severity of episodes of illness). The operational criteria (OPCRIT) symptom checklist was used to rate presence or absence of depressive, manic and psychotic symptoms on a lifetime-ever basis.5,6 Each participant was diagnosed and had key clinical variables rated independently by at least two members of the research team, and a consensus was reached. Team members involved in the interview, rating and diagnostic procedures were either fully trained research psychologists or psychiatrists. Interrater reliability was assessed using joint ratings of 20 cases with a range of mood disorder diagnoses. Mean overall kappa was 0.85 for DSM–IV diagnoses. Mean kappa statistics and intraclass correlation coefficients for other key clinical variables were in the range 0.81–0.99 and 0.85–0.97 respectively. This study received all necessary multiregion and local research ethics committee approval, and all participants gave written informed consent.
Forward stepwise likelihood ratio binary logistic regression was performed using the Statistical Package for the Social Sciences, SPSS version 12 for Windows, to identify variables significantly predictive of bipolar disorder v. major depressive disorder classification.
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Forward stepwise logistic regression was used to establish the best depression-related predictors of bipolar v. unipolar group membership. All lifetime variables relating to depressive episodes that were significant at the 1% level in univariate analyses comparing the two groups were entered into the regression. To control for sample differences in recruitment and current mental state, BDI score at interview and method of recruitment were included in the regression. Gender was also included in the logistic regression analysis. Significant predictors of diagnosis are shown in Table 1.
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View this table: [in a new window] | Table 1 Lifetime clinical characteristics predicting bipolar v. unipolar group membership |
Although there were, of course, similarities between unipolar and bipolar depression, we found important clinical differences: characteristics that best predicted bipolar rather than unipolar depression were the presence of psychosis, diurnal mood variation and hypersomnia during depressive episodes, a greater number of depressive episodes and a shorter duration of the longest depressive episode. Participants with major depressive disorder were characterised by the presence of excessive self-reproach, loss of energy and diminished libido.
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Distinguishing between bipolar disorder and major depressive disorder is of great clinical importance because optimal management of the two conditions is very different. For example, anti-depressants should be used with caution in bipolar depression because of the risk of precipitating mood switches, cycling, or mixed or agitated states.10 It is desirable that clinicians use all available information to guide management (including choice of treatment, advice to patient and intensity of monitoring). The clinical features of depression are not a definitive guide to diagnosis but can help to alert the clinician to a possible bipolar course. These findings also have important implications for future research on type II bipolar disorder and sub-threshold bipolar disorders. Evidence suggests that 25–50% of individuals with recurrent major depression (particularly those within atypical, early-onset or treatment-refractory subgroups) may in fact have a broadly defined bipolar disorder.11 We currently know little about how best to treat such patients. Future studies will need to move beyond strict diagnostic categories and examine subgroups of patients defined by extended phenotypic measures such as dimensional assessments of bipolar features, bipolar symptom clusters and longitudinal illness course variables.
An important limitation of our study is that there might have been differences between the two groups of participants that we were not able to examine, such as subtle differences in treatment regimens or patterns of comorbid illness. We also note that although the proportion of women in the major depressive disorder sample is typical of studies of this nature, the proportion of women in the bipolar disorder sample is higher than is typically reported (nearly three-quarters compared with a half) and that this may limit the generalisability of our findings. A further limitation was the use of retrospective rather than prospective assessments, even though we used an in-depth semi-structured clinical interview supplemented by case-note review. Prospective ratings, though preferable, can be prohibitively expensive.
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