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Bipolar disorders following initial depression: Modeling predictive clinical factors
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Bipolar Disorders following initial depression: Modeling predictive clinical Factors Leonardo Tondo, Caterina Visioli, Antonio Preti, Ross J. Baldessarini
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S0165-0327(14)00338-3 http://dx.doi.org/10.1016/j.jad.2014.05.043 JAD6788
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Journal of Affective Disorders
Received date: 30 April 2014 Accepted date: 23 May 2014 Cite this article as: Leonardo Tondo, Caterina Visioli, Antonio Preti, Ross J. Baldessarini, Bipolar Disorders following initial depression: Modeling predictive clinical Factors, Journal of Affective Disorders, http://dx.doi.org/10.1016/j. jad.2014.05.043 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Bipolar Disorders Following Initial Depression: Modeling Predictive Clinical Factors by a,b b Leonardo Tondo, M.D., M.Sc.; Caterina Visioli, Psy.D. b a,c Antonio Preti, M.D., and Ross J. Baldessarini, M.D. From a. International Consortium for Bipolar Disorder Research, Mailman Research Center, McLean Division of Massachusetts General Hospital, Boston, MA; b. Centro Lucio Bini Mood Disorders Center, Cagliari and Rome, Italy; c. Department of Psychiatry, Harvard Medical School, Boston, MA Running Title: Prediction of bipolar versus unipolar disorders following depression
Acknowledgements: Supported in part by the Aretæus Research Fund and Stanley Medical Research Institute (to LT), by a Sardinia Regional Master-and-Back Fellowship (to CV), a grant from the Bruce J. Anderson Foundation and by the McLean Private Donors Psychopharmacology Research Fund (to RJB). Disclosures: No author or immediate family member has financial relationships that might represent a conflict of interest in the work presented: none receives research support, is a consultant to, or is on a speakers’ panel of a pharmaceutical or biomedical corporation, nor holds equity positions in such corporations. Submitted to: Journal of Affective Disorders, as a research article, April, 2014 [230-word abstract, 2540-word text, 40 references; 3 tables, 1 figure].
Correspondence to: Dr. Leonardo Tondo, Mailman Research Center 212; McLean Hospital, 115 Mill Street, Belmont, MA 02478-9106 USA; Tel 1-617-855-3212; Fax 1-617-855-3479; E-mail: [email protected].
Bipolar versus unipolar depression
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Abstract Objective: Most first lifetime episodes among persons eventually diagnosed with bipolar disorder are depressive, often with years of delay to a final differentiation from unipolar major depression. To support early differentiation, we tested several predictive factors for association with later diagnoses of bipolar disorder. Method: With data from mood-disorder patients with first-lifetime episodes of major depression, we used multivariate, logistic modeling and Bayesian methods including Receiver Operating Characteristic curves to evaluate ability of one or more selected factors to differentiate patients who later met DSM-IV-TR diagnostic criteria for bipolar disorder and not unipolar major depressive disorder. Results: We analyzed data from 2146 patients (642 bipolar, 1504 unipolar) at risk for 13 years following initial depressive episodes. In multivariate modeling for 812 subjects with information on all clinical factors considered, seven significantly and independently differentiated bipolar from unipolar disorders, ranking (by significance): [a] ≥4 previous depressive episodes, [b] suicidal acts, [c] cyclothymic temperament, [d] family history of bipolar disorder, [e] substance-abuse, [f] younger-at-onset, or onset-age <25, and [g] male sex; four of these (c, d, f, g) can be identified at illness-onset. Bayesian analysis indicated optimal sensitivity and specificity at 2–4 factors/person and correct classification of 64%–67% of cases, and ROC analysis of factors/person yielded a significant area-under-the-curve of 0.72 [CI: 0.68–0.75]. Conclusions: In multivariate modeling, 7 factors were significantly and independently associated with bipolar disorder diagnosed up to 13 years after initial depression. ——————————————————————————————————————————————————————————
Keywords: Bipolar disorder, depressive onset, diagnosis, major depression, prediction —————————————————————————————————————————————————————————
The onset of bipolar disorders reportedly involves a major depressive episode in approximately half of bipolar I disorder patients, and three-quarters of those diagnosed with bipolar II syndrome (Goodwin and Jamison, 2007; Baldessarini et al., 2013). Variable proportions of bipolar disorder patients have two or more episodes of depression before a manic, mixed, or hypomanic episode required for diagnosis of bipolar disorder (Goodwin and Jamison, 2007, Angst et al., 2011). The observed interval from an initial episode of depression to clinical diagnosis of bipolar disorder typically is from 5 to 15 years (Angst et al., 2005; Fiedorowicz et al., 2011; Baldessarini et al., 2013, 2014; Dudek et al., 2013). It is important clinically to predict as soon as possible whether a patient presenting with depression may later meet diagnostic criteria for a bipolar disorder (Fiedorowicz et al. 2011; Phillips and Kupfer, 2013). Such prognostication can improve long-term planning of clinical management, and guide use of moodstabilizing or antidepressant medicines, including efforts to limit risk of potentially dangerous moodswitching (Ghaemi et al., 2001; Baldessarini, 2013; Pacchiarotti et al., 2013).
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Several factors have been proposed as possible predictors of the diagnosis of a bipolar disorder, based mainly on comparing early clinical characteristics of patients eventually meeting diagnostic criteria for a bipolar versus unipolar depressive disorder (Goodwin and Jamison, 2007; Angst et al., 2011). Among others, they include: [a] family history of bipolar disorder, [b] cyclothymic or hyperthymic temperament, [c] illness-onset before age 25 years, [d] multiple (especially >4) and probably shorter depressive episodes, [e] stressful precipitants at onset, [f] being unmarried, [g] substance-abuse, [h] presence of attention deficit hyperactivity disorder (ADHD); and [i] pathological mood-elevation during treatment with an antidepressant or other mood-elevating agent (stimulant, corticosteroid). Factors with an unclear ability to differentiate bipolar from unipolar mood disorders include sex and suicidal behavior or ideation, as well as initial presentation in an episode of anxiety or depression. In addition, some symptoms of depressive episodes—including prominent agitation, anxiety, psychotic features, as well as so-called “atypical” symptoms (hypersomnia, hyperphagia, psychomotor retardation), as well as other personality traits and psychological or biological factors— also may be associated selectively with bipolar disorders (Goodwin and Jamison, 2007). Remaining uncertain are the individual and combined, relative, strengths of association of such factors with bipolar disorder, and their predictive value in distinguishing it from nonbipolar major affective disorders (Ghaemi et al., 2001; Angst et al., 2003, 2005; Goodwin and Jamison, 2007; Fiedorowicz et al., 2011; Gilman et al., 2012). An exception is a study using multivariate modeling and Bayesian methods to evaluate risk factors for ability to differentiate major depressive disorder from bipolar-II, bipolar-NOS, and “soft” bipolar disorders among patients with an index depressive episode (Takeshima and Oka, 2013). Given the importance of early identification of bipolar disorders by assessing predictive factors such as those just outlined, we compared rates of clinical characteristics in a large sample of patients who presented initially in a major depressive episode and eventually met DSM-IV-TR diagnostic criteria for a bipolar disorder or unipolar major depressive disorder. We hypothesized that specific clinical factors
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differed individually and in combination in their ability to predict later diagnoses of bipolar disorder. Accordingly, we used multivariate regression modeling and Bayesian methods to test for individual and combined associations of reported risk factors with later diagnoses of bipolar disorder in a large sample of patients who presented in first-lifetime episodes of major depression. Methods Subjects We analyzed information from systematic clinical assessments of consecutive adult, mood-disorder patients enrolled at the Lucio Bini Mood Disorders Centers in Cagliari and Rome, Italy, between 1977 and 2013. We considered the 2146 subjects with a depressive first-lifetime major episode of affective illness, seeking predictive factors associated with later diagnoses of bipolar disorder (n=642) or unipolar major depressive disorder (n=1504), updated to accord with DSM-IV-TR criteria after 2000. Patients with only depressive illness were required to have been at risk for at least two years from illness-onset to clinic entry, to allow time for potential mania-like illness to occur. All subjects underwent diagnostic assessments and follow-up evaluations by the same mood-disorders expert (LT), based on semistructured interviews at intake as well as prospective, clinical follow-up assessments. Diagnoses were updated to meeting DSM-IV-TR criteria after the year 2000. After full description of the study to potential subjects, written informed consent was obtained for collection and analysis of their data to be presented anonymously in aggregate form, in accord with the requirements of Italian law. Datamanagement complied with US federal Health Insurance Portability and Accountability Act (HIPAA) regulations pertaining to confidentiality of patient records. Required data were entered into a computerized database (by CV and LT) in coded form to protect subject identity; all authors participated in literature searching, data-analysis and reporting. All study-participants were treated clinically in accord with international community-standards. Data analyses
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We compared patients diagnosed with a bipolar disorder (n=642; 29.9%) versus unipolar major depressive disorder (n=1504; 70.1%) for selected clinical characteristics, initially using bivariate 2
analyses (contingency tables [χ ] for categorical, and ANOVA [t-score] for continuous measures. Guided by these preliminary analyses, factors tentatively differentiating bipolar from unipolar disorders (p<0.10) supported by the literature cited above, were tested in multivariate, step-forward logistic regression modeling to verify clinical factors associated significantly and independently with bipolar disorder versus unipolar major depressive disorder. These analyses provided Odds Ratios (ORs) with 95% confidence intervals (CIs). We also included Bayesian analyses to evaluate the diagnostic impact of rising numbers of differentiating factors, including use of a Receiver Operating Characteristic (ROC) ®
function. Statistical analyses employed commercial computer programs (for spreadsheet: Statview-5 , ®
SAS Institute, Cary, NC; for analyses: Stata-12 , StataCorp, College Station, TX). Results The total of 2146 mood-disorder subjects were evaluated on entry into the study sites, later categorized by DSM-IV-TR diagnostic criteria, for clinical factors of interest associated with bipolar disorder after an initial depressive episode. Of these patients presenting in a first episode of major depression, 642 (29.9%) later met criteria for a bipolar, and 70.1% were diagnosed with a unipolar depressive disorder. Mean ages at intake were 42.9±15.9 and 46.4±16.1 years among bipolar and unipolar disorder patients, respectively. Time from initial episodes averaged 13.1±12.1 years for those diagnosed with bipolar disorder, and 13.3±11.1 years with unipolar major depressive disorder (13.2±11.4 years, overall). Patients with single episodes of major depression were observed for an average of 10.1±9.81 years, and were included among those considered to have unipolar major depression. We contrasted selected clinical characteristics between patients from the two diagnostic groups, considering all subjects (n=812) with information about the presence or absence of all factors considered (Table 1). Factors tentatively associated with bipolar disorder (p≤0.10) included: [a] male sex; [b] firstdegree family history of any psychiatric illness, mood-disorder, bipolar disorder, or suicide; [c] younger
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age at onset and, in particular, onset-age less than 25 years (as had been suggested previously [1]); [d] clinically ascertained cyclothymic temperament compared to other types or lack of a defined temperament; [e] occurrence of other psychiatric disorders, including ADHD specifically; [f] drug or alcohol abuse; [g] depressive episodes (and presence of more or fewer than 4/person), episodes/year, and their estimated duration; and [h] suicidal ideation or behavior (Table 1). An additional factor that was not selectively associated with bipolar disorder diagnoses was previous diagnosis of attention deficithyperactivity disorder (ADHD). [Table 1 about here]
We examined factors identified in preliminary bivariate analyses for selective association with bipolar versus unipolar diagnoses, using multivariate logistic regression modeling (Table 2). From some factors (family history, onset age, depressions before intake, and suicidal behavior [Table 1]), we selected those of greatest significance in differentiating bipolar from unipolar disorders for multivariate modeling (family history for bipolar disorder, age at illness onset, presence of ≥4 depressive episodes, and suicidal acts). In multivariate modeling, seven factors remained significantly and independently associated with diagnoses of bipolar disorder (Table 2). Ranked by statistical significance, they were: [a] ≥4 depressive episodes before diagnosis; [b] lifetime suicide acts; [c] cyclothymic versus other temperaments; [d]; family history of bipolar disorder [e] substance abuse; [f] younger onset-age; and [g] male sex. We also considered separately, four factors that can be identified at first lifetime episodes of major depression (factors c, d, f, g); logistic regression modeling with these four factors again was highly significant (likelihood ratio = 87.6, p<0.0001; not shown). [Table 2 about here]
The seven factors identified with logistic-regression modeling were further tested for their rates of occurrence in bipolar versus unipolar depressive disorder patients (Table 3). Presence of 2–4 factors yielded the strongest separation of bipolar from unipolar depressive disorder subjects. In addition, Bayesian analysis for various numbers of predictive factors indicated that the presence of 2
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factors yielded a favorable balance of sensitivity (70.8%) and specificity (62.2%), with 66.8% of diagnoses correctly classified (Table 3). [Table 3 about here]
We also computed a Bayesian receiver-operating characteristic (ROC) function to illustrate the effects on sensitivity (probability of the factor criteria being associated selectively with bipolar disorder, or true-positive rate) and specificity (probability of absence of the factor criteria in unipolar patients, or true-negative rate, analyzed as 1 – specificity or false positive rate) with the simultaneous presence of increasing numbers of factors identified in Table 2 (Figure). The computed area-underthe-curve (AUC; 0.72; CI: 0.68–0.75) across the number of factors present per patient was significant. Optimal differentiation of bipolar from unipolar disorders (greatest displacement of the function from the null baseline (dotted line) was associated with the presence of 2 or 3 factors per subject. [Figure about here]
Discussion Several factors have been found to predict later diagnosis of bipolar disorder among depressed patients (Goodwin and Jamison, 2007). These include: younger age at onset (Coryell et al., 1995; Rao et al., 1995; Geller et al., 2001; Goldberg et al., 2001; Angst et al., 2005; Seemüller et al., 2010; Tondo et al., 2010; Baldessarini et al., 2012; Takeshima and Oka, 2013); more depressive recurrences (Perris, 1966; Angst et al., 1978; Geller et al., 2001; Baldessarini et al., 2012); substance abuse (Regier et al., 1990; Akiskal et al., 1995; Judd et al., 2003; Leyton and Barrera, 2010; Parker et al., 2013); attention deficit hyperactivity disorder (ADHD) (Scheffer et al., 2004; Citrome and Goldberg, 2005; Catalano et al., 2011; Chen et al., 2013); cyclothymic, hyperthymic or irritable temperament (Akiskal et al., 1995; Akiskal and Benazzi, 2005; Mechri et al., 2011; Aguiar-Ferreira et al., 2013); bipolar disorder in firstdegree relatives (Perris, 1966; Rao et al., 1995; Angst et al., 2005; Seemüller et al., 2010; Tondo et al., 2010; Aguiar-Ferreira et al., 2013; Parker et al., 2013); suicidal behavior (Tondo et al., 2007; Leyton and Barrera, 2010; Seemüller et al., 2010; Parker et al., 2013); and mood-switching during antidepressant
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treatment (Ghaemi et al., 2001; Angst et al., 2003, 2011; Goodwin and Jamison, 2007; Gilman et al., 2012; Takeshima et al., 2013). These factors were reported in separate studies and not considered all together. However, a recent study (Takeshima et al., 2013) used multivariate and Bayesian analyses with 199 depressed subjects to identify five factors independently associated with diagnoses of “soft” bipolar disorder (excluding bipolar-I): family history of bipolar disorders, cyclothymic temperament, early age at a first depressive episode, more depressive episodes, and depressive-mixed states. The present study is the first to identify seven factors that significantly and independently predicted diagnoses of bipolar disorder among patients with a first-lifetime depressive episode. In the present, large sample of mood-disorder patients who presented initially in a major depressive episode, later diagnoses of bipolar disorder were made in 30% of subjects. In both bipolar and unipolar cases, the average time of observation averaged 13 years, sufficient to minimize the risk of missing episodes of mania or hypomania. In considering the strength of association of predictive factors with later diagnoses, we found that the presence of 2–4 factors per person especially strongly distinguished bipolar from unipolar cases (Table 2). Based on Bayesian analyses, even the presence of any two factors differentiated bipolar from unipolar disorders and correctly categorized two-thirds of subjects (Table 3 and Figure). Of note, among the seven factors identified as being selectively associated with later diagnoses of bipolar disorder, multivariate modeling indicated that they differed in their strength of association. Strongest association (all p≤0.0003) with bipolar disorder was found with four factors, ranking: ≥4 depressive episodes, suicidal acts, cyclothymic temperament, and family history of bipolar disorder. Other factors (p=0.007 to 0.04) ranked: substance abuse, younger onset, and male sex (Table 2). Another reported factor of major importance is mood-switching during exposure to antidepressants or other mood-elevating substances. We did not consider sustained mood-elevation during antidepressant treatment as a predictive factor since such reactions can be considered tantamount to a diagnosis of bipolar disorder.
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Identification of factors anticipating DSM diagnoses of bipolar disorder can contribute to guiding prognosis and clinical management, as well as earlier identification of patients who may be likely to later attain diagnostic criteria for bipolar disorder. In fact, four of the identified factors (family history, sex, onset-age, and temperament), can usually be assessed at initial depressive episodes and so may be of particular importance for prognosis and for treatment. When these four factors were analyzed separately, the likelihood ratio in logistic regression modeling was highly significant. Increased probability of later meeting diagnostic criteria for a bipolar disorder should help to guide treatmentselection, including timely consideration of the use of mood-stabilizers and cautious use of antidepressants (Baldessarini, 2013; Pacchiarotti et al., 2013). In short, we suggest that the presence of multiple predictive factors associated selectively with bipolar disorder should raise suspicion about the risk of emergence of spontaneous or drug-associated mania, even with a current diagnosis of unipolar major depression. The present study addressed differentiation of bipolar from major depressive disorders, but leaves unresolved the nature and extent of major affective disorders—questions pursued, but not resolved, since the time of Kraepelin (Trede et al., 2005). Are bipolar disorder and major depressive disorder two different illnesses or variations in the expression of one? In particular, it is not clear if a patient who experiences one or more depressive episode has had the potential for bipolarity from the start, or may change spontaneously in syndrome-type over time, or in response to environmental factors, perhaps including life-events or effects of treatments (Goldberg et al., 2001; Judd et al., 2003; Akiskal and Benazzi, 2005; Citrome and Goldberg, 2005; Seemüller et al., 2010; Gilman et al., 2012; Phillips and Kupfer, 2013). Such questions may eventually yield to genetic and other biological methods to distinguish specific disorders (Szczepankiewicz, 2013; ENIGMA, 2014; Kerner, 2014). In particular, genetic analysis may help to distinguish bipolar and unipolar mood-disorder phenotypes and to identify depressed patients at increased risk of later mania.
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This study has important limitations. Not all patients with initial depression provided information about all predictive factors for the diagnosis of bipolar disorder. In addition, assessment of initial and early types of morbidity was retrospective, and recall of reported first-episodes may reflect memorable severity, special effects of treatment, or need for hospitalization, and not necessarily represent some details of accurately, including presence of psychotic or mixed symptoms and other details of the nature of depressive episodes. Nevertheless, it is likely that recall bias would arise similarly among bipolar and unipolar disorder patients and so limit distortion of their comparison. In conclusion, we found seven factors which predicted that an initially depressed person would later meet diagnostic criteria for a bipolar disorder in a sample of more than 2000 mood-disorder patients. Such efforts are encouraged by the high proportion of initial depression among patients bipolar disorder patients, by the substantial proportion of patients presenting initially in depression but later meeting DSM diagnostic criteria for a bipolar disorder, and by the typically prolonged delay to diagnosis of bipolar disorder from such onsets. It is important to ascertain diagnoses and formulate prognoses early so as to guide planning for optimal clinical care of mood-disorder patients, including timely consideration of mood-stabilizing medicines and cautious use of antidepressants so as to limit risk of unanticipated and potentially dangerous mood-switching. References Aguiar-Ferreira, A.D., Vasconcelos, A.G., Neves F.S., Laks, J., Correa, H., 2013. Affective temperaments: familiality and clinical use in mood disorders. J. Affect. Disord. 148, 53-56. Akiskal, H.S., Benazzi, F., 2005. Atypical depression: a variant of bipolar II or a bridge between unipolar and bipolar II? J. Affect. Disord. 84, 209-217. Akiskal, H.S., Maser, J.D., Zeller, P.J., Endicott, J., Coryell, W., Keller, M., Warshaw, M., Clayton, P., Goodwin, F., 1995. Switching from 'unipolar' to bipolar II. An 11-year prospective study of clinical and temperamental predictors in 559 patients. Arch. Gen. Psychiatry. 52, 114-123. Angst, J., Azorin, J.M., Bowden, C.L., Perugi, G., Vieta, E., Gamma, A., Young, A.H., 2011. Prevalence and characteristics of undiagnosed bipolar disorders in patients with a major depressive episode: the BRIDGE study. Arch. Gen. Psychiatry. 68, 791-798.
Bipolar versus unipolar depression
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Angst, J., Felder, W., Frey, R., Stassen, H.H., 1978. Course of affective disorders. I. Change of diagnosis of monopolar, unipolar, and bipolar illness. Arch. Psychiatr. Nervenkr. 226, 57-64. Angst, J., Gamma, A., Benazzi, F., Ajdacic, V., Eich, D., Rössler, W., 2003. Toward a re-definition of subthreshold bipolarity: epidemiology and proposed criteria for bipolar-II, minor bipolar disorders and hypomania. J. Affect. Disord. 73, 133-146. Angst. J., Sellaro, R., Stassen, H.H., Gamma, A., 2005. Diagnostic conversion from depression to bipolar disorders: results of a long-term prospective study of hospital admissions. J. Affect. Disord. 2005; 84, 149157. Baldessarini, R.J., 2013. Chemotherapy in Psychiatry, third edition. New York: Springer Press, New York, NY. Baldessarini, R.J., Faedda, G.L., Offidani, E., Vázquez, G.H., Marangoni, C., Serra, G., Tondo, L., 2013. Antidepressant-associated mood-switching and transition from unipolar major depression to bipolar disorder: a review. J. Affect. Disord. 148, 129-135. Baldessarini, R.J., Tondo, L., Vázquez, G.H., Bolzani, L., Khalsa, H.-M.K., Lai, M., Lepri, B., Lolich, M., Maffei, P.M., Salvatore, P., Tohen, M., 2012. International study of functional and symptomatic outcome versus onset-age in 1437 bipolar-I disorder patients. World Psychiatry. 11, 40-46. Baldessarini, R.J., Tondo, L., Visioli, C., 2014. First-episode types in bipolar disorder: predictive associations with later illness. Acta Psychiatr. Scand. 129, 383-392. Catalano, V., Harnic, D., Di Nicola, M., Mazza, M., Martinotti, G., Bruschi, A., Di Felice, C., Carnevale, E., Marano, G., Pozzi, G., Bria, P., Janiri, L., 2011. Diagnosis of deficit and attention/hyperactivity disorders (ADHD) in patients with bipolar or unipolar depression: an experimental study [Italian]. Clin. Ter. 162, 107111. Chen, M.H., Su, T.P., Chen, Y.S., Hsu, J.W., Huang, K.L., Chang, W.H., Chen, T.J., Bai, Y.M., 2013. Higher risk of developing mood disorders among adolescents with co-morbidity of attention deficit hyperactivity disorder and disruptive behavior disorder: a nationwide prospective study. J. Psychiatr. Res, 47, 1019-1023. Citrome, L., Goldberg, J.F., 2005. The many faces of bipolar disorder: how to tell them apart. Postgrad. Med. 117, 15-23. Coryell, W., Endicott, J., Maser, J.D., Keller, M.B., Leon, A.C., Akiskal, H.S., 1995. Long-term stability of polarity distinctions in the affective disorders. Am. J. Psychiatry. 152, 385-390. Dudek, D., Siwek, M., Zielińska, D., Jaeschke, R., Rybakowski, J., 2013. Diagnostic conversions from major depressive disorder into bipolar disorder in an outpatient setting: results of a retrospective chart review. J. Affect. Disord. 144, 112-115. ENIGMA Consortium, 2014. large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. [Epub ahead of print (8 Jan)].
Bipolar versus unipolar depression
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Fiedorowicz, J.G., Endicott, J., Leon, A.C., Solomon, D.A., Keller, M.B., Coryell, W.H., 2011. Sub-threshold hypomanic symptoms in progression from unipolar major depression to bipolar disorder. Am. J. Psychiatry. 168, 40-48 [and written communication from Dr. Fiedorowicz of 28 Jan 2014]. Geller, B., Zimerman, B., Williams, M., Bolhofner, K., Craney, J.L., 2001. Bipolar disorder at prospective followup of adults who had prepubertal major depressive disorder. Am. J. Psychiatry. 158, 125-127. Ghaemi, S.N., Ko, J.M., Goodwin, F.K., 2001. Bipolar spectrum and the antidepressant view of the world. J. Psychiatr. Pract. 7, 287-297. Gilman, S.E., Dupuy, J.M., Perlis, R.H., 2012. Risks for the transition from major depressive disorder to bipolar disorder in the National Epidemiologic Survey on Alcohol and Related Conditions. J. Clin. Psychiatry. 73, 829-836. Goldberg, J.F., Harrow, M., Whiteside, J.E., 2001. Risk for bipolar illness in patients initially hospitalized for unipolar depression. Am. J. Psychiatry. 158, 1265-1270. Goodwin, F.K., Jamison, K.R., 2007. Manic-Depressive Illness, second edition. Oxford University Press, New York, NY, pp. 1-19, 122-123. Judd, L.L,, Akiskal, H.S., Schettler, P.J., Coryell, W., Maser, J., Rice, J.A., Solomon, D.A., Keller, M.B., 2003. The comparative clinical phenotype and long term longitudinal episode course of bipolar I and II: a clinical spectrum or distinct disorders? J. Affect. Disord. 73, 19-32. Kerner, B., 2014. Genetics of bipolar disorder. Appl. Clin. Genet. 7, 33-42. Leyton, F., Barrera, A., 2010. Bipolar depression and unipolar depression: differential diagnosis in clinical practice [Spanish] Rev. Med. Chile 138, 773-779. Mechri, A., Kerkeni, N., Touati, I., Bacha, M., Gassab, L., 2011. Association between cyclothymic temperament and clinical predictors of bipolarity in recurrent depressive patients. J. Affect. Disord. 132, 285-258. Pacchiarotti, I., Bond, D.J., Baldessarini, R.J., Nolen, W.A., Grunze, H., Licht, R.W., Post, R.M., Berk, M., Goodwin, G.M., Sachs, G.S., Tondo, L., Findling, R.L., Youngstrom, E.A., Tohen, M., Undurraga, J., González-Pinto, A., Goldberg, J.F., Yildiz, A., Altshuler, L.L., Calabrese, J.R., Mitchell, P.B., Thase, M.E., Koukopoulos, A., Colom, F., Frye, M., Malhi, G.S., Fountoulakis, K.N., Vázquez, G., Perlis, R.H., Ketter, T.A., Cassidy, F., Akiskal, H., Azorin, J.-M., Valentí, M., Mazzei, D.H., Lafer, B., Kato, T., Mazzarini, L., Martínez-Aran, A., Parker, G., Souery, D., Özerdem, A., McElroy, S.L., Girardi, P., Bauer, M., Yatham, Y.N., Zarate, C.A. Jr., Nierenberg, A.A., Birmaher, B., Kanba, S., El-Mallakh, R.S., Serretti, A., Rihmer, Z., Young, A.H., Kotzalidis, G.D., MacQueen, G.M., Bowden, C.L., Ghaemi, S.N., Lopez-Jaramillo, C., Rybakowski, J., Ha, K., Perugi, G., Kasper, S., Amsterdam, J.D., Hirschfeld, R.M., Kapczinski, F., Vieta, E., 2013. International Society for Bipolar Disorders task-force report on antidepressant use in bipolar disorders. Am. J. Psychiatry. 170, 1249-1262. Parker, G., Fletcher, K., McCraw, S., Futeran, S., Hong, M., 2013. Identifying antecedent and illness course variables differentiating bipolar I, bipolar II and unipolar disorders. J. Affect. Disord. 148, 202-209.
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Perris, C., 1966. A study of bipolar (manic-depressive) and unipolar recurrent depressive psychoses. Acta Psychiatr. Scand. Suppl. 194, 83-91. Phillips, M.L., Kupfer D.J., 2013. Bipolar disorder diagnosis: challenges and future directions. Lancet. 381. 16631671. Rao, U., Ryan, N.D., Birmaher, B., Dahl, R.E., Williamson, D.E., Kaufman, J., Rao, R., Nelson, B., 1995. Unipolar depression in adolescents: clinical outcome in adulthood. J. Am. Acad. Child Adolesc. Psychiatry. 34, 566-578. Regier, D.A., Farmer, M.E., Rae, D.S., Locke, B.Z., Keith, S.J., Judd, L.L., Goodwin, F.K., 1990. Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 264, 2511-2518. Scheffer, R.E., Niskala-Apps, J.A., 2004. Diagnosis of preschool bipolar disorder presenting with mania: open pharmacological treatment. J. Affect. Disord. 82 (Suppl. 1), S25-S34. Seemüller, F., Riedel, M., Dargel, S., Djaja, N., Schennach-Wolff, R., Dittmann, S., Möller, H.J., Severus, E., 2010. [Bipolar depression. Spectrum of clinical pictures and differentiation from unipolar depression [German]. Nervenarzt. 81, 531-538. Szczepankiewicz, A., 2013. Evidence for single nucleotide polymorphisms and their association with bipolar disorder. Neuropsychiatr. Dis. Treat. 9, 1573-1582. Takeshima, M., Oka, T., 2013. Comprehensive analysis of features that suggest bipolarity in patients with a major depressive episode: which is the best combination to predict soft-bipolarity diagnosis? J. Affect. Disord. 147, 150-155. Tondo, L., Lepri, B., Baldessarini, R.J., 2007. Risks of suicidal ideation, attempts and suicides among 2826 men and women with types I and II bipolar, and recurrent major depressive disorders. Acta Psychiatr. Scand. 116, 419-428. Tondo, L., Lepri, B., Cruz, N., Baldessarini, R.J., 2010. Age at onset in 3014 Sardinian bipolar and major depressive disorder patients. Acta Psychiatr. Scand. 121, 446-452. Trede, K., Salvatore, P., Baethge, C., Gerhard, A., Maggini, C., Baldessarini, R.J., 2005. Manic-Depressive Illness: evolution in Kraepelin’s Textbook, 1883-1926. Harv. Rev. Psychiatry. 13, 155-178.
Table 1. Characteristics of bipolar disorder versus unipolar major depressive disorder patients with depressive first-episodes Characteristic
Prevalence or Mean ± SD
Rate
Statistic
p-value
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Bipolar (n)
Ratio (BD/UD)
Unipolar (n)
2
(χ or t)
Men (%) 39.1 642 30.7 1504 1/1.27 14.2 Family history (%) Any psychiatric illness 71.3 548 63.3 972 1.13 10.2 Mood disorders 62.8 548 51.1 972 1.23 19.2 Bipolar disorder 29.4 548 12.7 972 2.31 64.5 Suicide 8.21 548 4.63 972 1.77 8.07 Onset age 29.8±13.3 642 34.1±15.3 1504 1/1.14 37.8 Years Onset <25 years (%) 45.3 642 33.9 1504 1.34 25.1 Cyclothymic vs. other temperaments 22.4 490 9.35 695 2.40 39.2 a (%) Depressions before intake ≥4 Depressions (%) 37.0 611 16.1 669 2.31 72.0 Depressions/year 1.14±2.52 611 0.44±0.65 669 2.59 48.7 Duration of depressive episodes 5.24±4.88 605 7.31±13.8 463 1/1.39 11.6 (months) Suicidal (%) Any suicidal behavior or thinking 52.9 624 24.7 1460 2.14 156 Suicidal acts vs. ideation 22.6 624 5.62 1460 4.02 132 b Occurrence of other disorders (%) 53.6 293 63.5 345 1/1.18 6.41 Any concurrent disorder 14.7 620 4.02 1468 3.66 74.3 Substance-abuse Total N=2146: bipolar disorder (BD) 642 (29.9%), unipolar recurrent major depressive disorder (UD) 1504 (70.1%). All degrees-of-freedom [df] = 1). a. Compared to lack of a clinically categorizable temperament type. b. Lifetime risk vs. absence of such disorders, based on clinical assessment; substance abuse includes illicit drug use and alcohol-abuse. Factors in boldface were used in multivariate modeling, as having the strongest differentiating ability within each category.
Table 2. Logistic regression model: factors associated with bipolar disorder (BD) versus unipolar depressive disorder among patients with an initial depressive episode Factors ≥4 Depressive episodes before intake a Suicidal acts Cyclothymic versus other b Family history for bipolar disorder c Substance abuse Younger median onset-age Male sex
Present in BD (%)
OR
95% CI
χ
39.3 22.7 23.1 30.4 18.2 57.5 38.1
2.74 3.50 2.27 1.99 1.96 1.01 1.41
1.93–3.89 2.16–5.65 1.46–3.54 1.37–2.90 1.20–3.19 1.00–1.03 1.01–1.96
31.2 26.0 13.1 13.1 7.32 6.97 4.27
2
p-value <0.0001 <0.0001 0.0003 0.0003 0.007 0.008 0.04
<0.0001 0.001 <0.0001 <0.0001 0.004 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0006 <0.0001 <0.0001 0.01 <0.0001
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2
Factors listed in rank order of statistical significance (χ ); the overall likelihood ratio of the model is highly 2 significant (df=6; χ =148, p<0.0001) among 812 subjects (428 [52.7%] with a bipolar disorder and 384 [47.3%] with recurrent depressive disorder in whom the characteristics could be compared. a. Gestures and attempts. b. First-degree family history. c. All use or abuse of illegal substances or abuse of alcohol. Other factors not significantly related to bipolar disorder included psychiatric co-morbidity, ADHD (attention disorder with hyperactivity), depressive episodes/year, average duration of depressive episodes, lifetime hospitalization, and HDRS (21-item Hamilton Depression Rating Scale) score at the start of a later depressive episode.
Table 3. Numbers of factors selectively associated with bipolar disorder Distribution of Risk Factors Factors (n)
0 1 2 3 4 5 6 7
Total (n [%])
Bipolar (n [%])
134 (16.5) 230 (28.3) 217 (26.7) 140 (17.2) 65 (8.00) 24 (1.50) 2 (0.25) 0 (0.00)
37 (8.64) 88 (20.6) 119 (27.8) 107 (25.0) 53 (12.4) 22 (5.14) 2 (0.47) 0 (0.00)
Unipolar (n [%]) 97 (25.3) 142 (37.0) 98 (25.5) 33 (8.59) 12 (3.12) 2 (0.52) 0 (0.00) 0 (0.00)
Bayesian Analysis Correctly SensitivitySpecificity Classified p-value (%) (%) (%)
OR [95%CI]
χ
1.00
–––
–––
100.0
0.00
52.7
1.62 [1.02–2.58]
4.22
0.04
91.4
25.3
60.1
3.18 [2.00–5.05]
24.0
<0.0001
70.8
62.2
66.8
8.47 [4.93–14.7]
59.5
<0.0001
43.0
87.8
64.2
11.6 [9.26–23.8]
43.0
<0.0001
18.0
96.4
55.1
28.6 [6.45–125]
19.4
0.002
5.61
99.5
50.0
[0–∞] 0.001
0.98
0.47
100.0
47.5
–––
0.00
100.0
47.3
–7
7.04x10
–––
2
–––
Shows the numbers and proportions (%) of subjects with stated numbers of factors (from Table 2; none had all 7), among subjects later diagnosed with a bipolar disorder (BD) or a unipolar depressive disorder (UD), with Odds Ratios (OR; and their 95% confidence intervals). Differentiation of future diagnoses of bipolar vs. unipolar disorder was maximal when any 2–4 antecedent factors were present per person. By Bayesian analysis, optimal sensitivity (70.8%) and specificity (62.2%) was found with any 2 factors per subject, with 66.8% of subject correctly classified by diagnostic type.
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Figure Legend
Figure 1. Bayesian receiver-operating characteristic (ROC) curve (sensitivity versus 1–specificity, or true positive versus false positive rates; dotted straight line is the null baseline) for increasing numbers of factors identified by multivariate regression modeling (Table 2) that differentiate bipolar disorders from recurrent depressive disorder. The computed area-under-the-curve (AUC) was 0.72 [95% CI: 0.68–0.75]. Optimal differentiation of bipolar from recurrent depressive disorders was with ≥2 factors (the point on the ROC curve most displaced from the null line, with sensitivity = 70.8%, specificity = 62.2%), which correctly classified 66.8% of cases.
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100
75
50
25
0 0
25
50
75
100
1 – Specificity (False Positive Rate) Acknowledgements: Supported in part by the Aretæus Research Fund and Stanley Medical Research Institute (to LT), by a Sardinia Regional Master-and-Back Fellowship (to CV), a grant from the Bruce J. Anderson Foundation and by the McLean Private Donors Psychopharmacology Research Fund (to RJB). Disclosures: No author or immediate family member has financial relationships that might represent a conflict of interest in the work presented: none receives research support, is a consultant to, or is on a speakers’ panel of a pharmaceutical or biomedical corporation, nor holds equity positions in such corporations.
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Authors: Leonardo Tondo, M.D., M.Sc.; Caterina Visioli, Psy.D. Antonio Preti, M.D., and Ross J. Baldessarini, M.D.
Bipolar Disorders following initial depression: Modeling predictive clinical Factors Leonardo Tondo, Caterina Visioli, Antonio Preti, Ross J. Baldessarini
www.elsevier.com/locate/jad
PII: DOI: Reference:
S0165-0327(14)00338-3 http://dx.doi.org/10.1016/j.jad.2014.05.043 JAD6788
To appear in:
Journal of Affective Disorders
Received date: 30 April 2014 Accepted date: 23 May 2014 Cite this article as: Leonardo Tondo, Caterina Visioli, Antonio Preti, Ross J. Baldessarini, Bipolar Disorders following initial depression: Modeling predictive clinical Factors, Journal of Affective Disorders, http://dx.doi.org/10.1016/j. jad.2014.05.043 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Bipolar Disorders Following Initial Depression: Modeling Predictive Clinical Factors by a,b b Leonardo Tondo, M.D., M.Sc.; Caterina Visioli, Psy.D. b a,c Antonio Preti, M.D., and Ross J. Baldessarini, M.D. From a. International Consortium for Bipolar Disorder Research, Mailman Research Center, McLean Division of Massachusetts General Hospital, Boston, MA; b. Centro Lucio Bini Mood Disorders Center, Cagliari and Rome, Italy; c. Department of Psychiatry, Harvard Medical School, Boston, MA Running Title: Prediction of bipolar versus unipolar disorders following depression
Acknowledgements: Supported in part by the Aretæus Research Fund and Stanley Medical Research Institute (to LT), by a Sardinia Regional Master-and-Back Fellowship (to CV), a grant from the Bruce J. Anderson Foundation and by the McLean Private Donors Psychopharmacology Research Fund (to RJB). Disclosures: No author or immediate family member has financial relationships that might represent a conflict of interest in the work presented: none receives research support, is a consultant to, or is on a speakers’ panel of a pharmaceutical or biomedical corporation, nor holds equity positions in such corporations. Submitted to: Journal of Affective Disorders, as a research article, April, 2014 [230-word abstract, 2540-word text, 40 references; 3 tables, 1 figure].
Correspondence to: Dr. Leonardo Tondo, Mailman Research Center 212; McLean Hospital, 115 Mill Street, Belmont, MA 02478-9106 USA; Tel 1-617-855-3212; Fax 1-617-855-3479; E-mail: [email protected].
Bipolar versus unipolar depression
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Abstract Objective: Most first lifetime episodes among persons eventually diagnosed with bipolar disorder are depressive, often with years of delay to a final differentiation from unipolar major depression. To support early differentiation, we tested several predictive factors for association with later diagnoses of bipolar disorder. Method: With data from mood-disorder patients with first-lifetime episodes of major depression, we used multivariate, logistic modeling and Bayesian methods including Receiver Operating Characteristic curves to evaluate ability of one or more selected factors to differentiate patients who later met DSM-IV-TR diagnostic criteria for bipolar disorder and not unipolar major depressive disorder. Results: We analyzed data from 2146 patients (642 bipolar, 1504 unipolar) at risk for 13 years following initial depressive episodes. In multivariate modeling for 812 subjects with information on all clinical factors considered, seven significantly and independently differentiated bipolar from unipolar disorders, ranking (by significance): [a] ≥4 previous depressive episodes, [b] suicidal acts, [c] cyclothymic temperament, [d] family history of bipolar disorder, [e] substance-abuse, [f] younger-at-onset, or onset-age <25, and [g] male sex; four of these (c, d, f, g) can be identified at illness-onset. Bayesian analysis indicated optimal sensitivity and specificity at 2–4 factors/person and correct classification of 64%–67% of cases, and ROC analysis of factors/person yielded a significant area-under-the-curve of 0.72 [CI: 0.68–0.75]. Conclusions: In multivariate modeling, 7 factors were significantly and independently associated with bipolar disorder diagnosed up to 13 years after initial depression. ——————————————————————————————————————————————————————————
Keywords: Bipolar disorder, depressive onset, diagnosis, major depression, prediction —————————————————————————————————————————————————————————
The onset of bipolar disorders reportedly involves a major depressive episode in approximately half of bipolar I disorder patients, and three-quarters of those diagnosed with bipolar II syndrome (Goodwin and Jamison, 2007; Baldessarini et al., 2013). Variable proportions of bipolar disorder patients have two or more episodes of depression before a manic, mixed, or hypomanic episode required for diagnosis of bipolar disorder (Goodwin and Jamison, 2007, Angst et al., 2011). The observed interval from an initial episode of depression to clinical diagnosis of bipolar disorder typically is from 5 to 15 years (Angst et al., 2005; Fiedorowicz et al., 2011; Baldessarini et al., 2013, 2014; Dudek et al., 2013). It is important clinically to predict as soon as possible whether a patient presenting with depression may later meet diagnostic criteria for a bipolar disorder (Fiedorowicz et al. 2011; Phillips and Kupfer, 2013). Such prognostication can improve long-term planning of clinical management, and guide use of moodstabilizing or antidepressant medicines, including efforts to limit risk of potentially dangerous moodswitching (Ghaemi et al., 2001; Baldessarini, 2013; Pacchiarotti et al., 2013).
Bipolar versus unipolar depression
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Several factors have been proposed as possible predictors of the diagnosis of a bipolar disorder, based mainly on comparing early clinical characteristics of patients eventually meeting diagnostic criteria for a bipolar versus unipolar depressive disorder (Goodwin and Jamison, 2007; Angst et al., 2011). Among others, they include: [a] family history of bipolar disorder, [b] cyclothymic or hyperthymic temperament, [c] illness-onset before age 25 years, [d] multiple (especially >4) and probably shorter depressive episodes, [e] stressful precipitants at onset, [f] being unmarried, [g] substance-abuse, [h] presence of attention deficit hyperactivity disorder (ADHD); and [i] pathological mood-elevation during treatment with an antidepressant or other mood-elevating agent (stimulant, corticosteroid). Factors with an unclear ability to differentiate bipolar from unipolar mood disorders include sex and suicidal behavior or ideation, as well as initial presentation in an episode of anxiety or depression. In addition, some symptoms of depressive episodes—including prominent agitation, anxiety, psychotic features, as well as so-called “atypical” symptoms (hypersomnia, hyperphagia, psychomotor retardation), as well as other personality traits and psychological or biological factors— also may be associated selectively with bipolar disorders (Goodwin and Jamison, 2007). Remaining uncertain are the individual and combined, relative, strengths of association of such factors with bipolar disorder, and their predictive value in distinguishing it from nonbipolar major affective disorders (Ghaemi et al., 2001; Angst et al., 2003, 2005; Goodwin and Jamison, 2007; Fiedorowicz et al., 2011; Gilman et al., 2012). An exception is a study using multivariate modeling and Bayesian methods to evaluate risk factors for ability to differentiate major depressive disorder from bipolar-II, bipolar-NOS, and “soft” bipolar disorders among patients with an index depressive episode (Takeshima and Oka, 2013). Given the importance of early identification of bipolar disorders by assessing predictive factors such as those just outlined, we compared rates of clinical characteristics in a large sample of patients who presented initially in a major depressive episode and eventually met DSM-IV-TR diagnostic criteria for a bipolar disorder or unipolar major depressive disorder. We hypothesized that specific clinical factors
Bipolar versus unipolar depression
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differed individually and in combination in their ability to predict later diagnoses of bipolar disorder. Accordingly, we used multivariate regression modeling and Bayesian methods to test for individual and combined associations of reported risk factors with later diagnoses of bipolar disorder in a large sample of patients who presented in first-lifetime episodes of major depression. Methods Subjects We analyzed information from systematic clinical assessments of consecutive adult, mood-disorder patients enrolled at the Lucio Bini Mood Disorders Centers in Cagliari and Rome, Italy, between 1977 and 2013. We considered the 2146 subjects with a depressive first-lifetime major episode of affective illness, seeking predictive factors associated with later diagnoses of bipolar disorder (n=642) or unipolar major depressive disorder (n=1504), updated to accord with DSM-IV-TR criteria after 2000. Patients with only depressive illness were required to have been at risk for at least two years from illness-onset to clinic entry, to allow time for potential mania-like illness to occur. All subjects underwent diagnostic assessments and follow-up evaluations by the same mood-disorders expert (LT), based on semistructured interviews at intake as well as prospective, clinical follow-up assessments. Diagnoses were updated to meeting DSM-IV-TR criteria after the year 2000. After full description of the study to potential subjects, written informed consent was obtained for collection and analysis of their data to be presented anonymously in aggregate form, in accord with the requirements of Italian law. Datamanagement complied with US federal Health Insurance Portability and Accountability Act (HIPAA) regulations pertaining to confidentiality of patient records. Required data were entered into a computerized database (by CV and LT) in coded form to protect subject identity; all authors participated in literature searching, data-analysis and reporting. All study-participants were treated clinically in accord with international community-standards. Data analyses
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We compared patients diagnosed with a bipolar disorder (n=642; 29.9%) versus unipolar major depressive disorder (n=1504; 70.1%) for selected clinical characteristics, initially using bivariate 2
analyses (contingency tables [χ ] for categorical, and ANOVA [t-score] for continuous measures. Guided by these preliminary analyses, factors tentatively differentiating bipolar from unipolar disorders (p<0.10) supported by the literature cited above, were tested in multivariate, step-forward logistic regression modeling to verify clinical factors associated significantly and independently with bipolar disorder versus unipolar major depressive disorder. These analyses provided Odds Ratios (ORs) with 95% confidence intervals (CIs). We also included Bayesian analyses to evaluate the diagnostic impact of rising numbers of differentiating factors, including use of a Receiver Operating Characteristic (ROC) ®
function. Statistical analyses employed commercial computer programs (for spreadsheet: Statview-5 , ®
SAS Institute, Cary, NC; for analyses: Stata-12 , StataCorp, College Station, TX). Results The total of 2146 mood-disorder subjects were evaluated on entry into the study sites, later categorized by DSM-IV-TR diagnostic criteria, for clinical factors of interest associated with bipolar disorder after an initial depressive episode. Of these patients presenting in a first episode of major depression, 642 (29.9%) later met criteria for a bipolar, and 70.1% were diagnosed with a unipolar depressive disorder. Mean ages at intake were 42.9±15.9 and 46.4±16.1 years among bipolar and unipolar disorder patients, respectively. Time from initial episodes averaged 13.1±12.1 years for those diagnosed with bipolar disorder, and 13.3±11.1 years with unipolar major depressive disorder (13.2±11.4 years, overall). Patients with single episodes of major depression were observed for an average of 10.1±9.81 years, and were included among those considered to have unipolar major depression. We contrasted selected clinical characteristics between patients from the two diagnostic groups, considering all subjects (n=812) with information about the presence or absence of all factors considered (Table 1). Factors tentatively associated with bipolar disorder (p≤0.10) included: [a] male sex; [b] firstdegree family history of any psychiatric illness, mood-disorder, bipolar disorder, or suicide; [c] younger
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age at onset and, in particular, onset-age less than 25 years (as had been suggested previously [1]); [d] clinically ascertained cyclothymic temperament compared to other types or lack of a defined temperament; [e] occurrence of other psychiatric disorders, including ADHD specifically; [f] drug or alcohol abuse; [g] depressive episodes (and presence of more or fewer than 4/person), episodes/year, and their estimated duration; and [h] suicidal ideation or behavior (Table 1). An additional factor that was not selectively associated with bipolar disorder diagnoses was previous diagnosis of attention deficithyperactivity disorder (ADHD). [Table 1 about here]
We examined factors identified in preliminary bivariate analyses for selective association with bipolar versus unipolar diagnoses, using multivariate logistic regression modeling (Table 2). From some factors (family history, onset age, depressions before intake, and suicidal behavior [Table 1]), we selected those of greatest significance in differentiating bipolar from unipolar disorders for multivariate modeling (family history for bipolar disorder, age at illness onset, presence of ≥4 depressive episodes, and suicidal acts). In multivariate modeling, seven factors remained significantly and independently associated with diagnoses of bipolar disorder (Table 2). Ranked by statistical significance, they were: [a] ≥4 depressive episodes before diagnosis; [b] lifetime suicide acts; [c] cyclothymic versus other temperaments; [d]; family history of bipolar disorder [e] substance abuse; [f] younger onset-age; and [g] male sex. We also considered separately, four factors that can be identified at first lifetime episodes of major depression (factors c, d, f, g); logistic regression modeling with these four factors again was highly significant (likelihood ratio = 87.6, p<0.0001; not shown). [Table 2 about here]
The seven factors identified with logistic-regression modeling were further tested for their rates of occurrence in bipolar versus unipolar depressive disorder patients (Table 3). Presence of 2–4 factors yielded the strongest separation of bipolar from unipolar depressive disorder subjects. In addition, Bayesian analysis for various numbers of predictive factors indicated that the presence of 2
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factors yielded a favorable balance of sensitivity (70.8%) and specificity (62.2%), with 66.8% of diagnoses correctly classified (Table 3). [Table 3 about here]
We also computed a Bayesian receiver-operating characteristic (ROC) function to illustrate the effects on sensitivity (probability of the factor criteria being associated selectively with bipolar disorder, or true-positive rate) and specificity (probability of absence of the factor criteria in unipolar patients, or true-negative rate, analyzed as 1 – specificity or false positive rate) with the simultaneous presence of increasing numbers of factors identified in Table 2 (Figure). The computed area-underthe-curve (AUC; 0.72; CI: 0.68–0.75) across the number of factors present per patient was significant. Optimal differentiation of bipolar from unipolar disorders (greatest displacement of the function from the null baseline (dotted line) was associated with the presence of 2 or 3 factors per subject. [Figure about here]
Discussion Several factors have been found to predict later diagnosis of bipolar disorder among depressed patients (Goodwin and Jamison, 2007). These include: younger age at onset (Coryell et al., 1995; Rao et al., 1995; Geller et al., 2001; Goldberg et al., 2001; Angst et al., 2005; Seemüller et al., 2010; Tondo et al., 2010; Baldessarini et al., 2012; Takeshima and Oka, 2013); more depressive recurrences (Perris, 1966; Angst et al., 1978; Geller et al., 2001; Baldessarini et al., 2012); substance abuse (Regier et al., 1990; Akiskal et al., 1995; Judd et al., 2003; Leyton and Barrera, 2010; Parker et al., 2013); attention deficit hyperactivity disorder (ADHD) (Scheffer et al., 2004; Citrome and Goldberg, 2005; Catalano et al., 2011; Chen et al., 2013); cyclothymic, hyperthymic or irritable temperament (Akiskal et al., 1995; Akiskal and Benazzi, 2005; Mechri et al., 2011; Aguiar-Ferreira et al., 2013); bipolar disorder in firstdegree relatives (Perris, 1966; Rao et al., 1995; Angst et al., 2005; Seemüller et al., 2010; Tondo et al., 2010; Aguiar-Ferreira et al., 2013; Parker et al., 2013); suicidal behavior (Tondo et al., 2007; Leyton and Barrera, 2010; Seemüller et al., 2010; Parker et al., 2013); and mood-switching during antidepressant
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treatment (Ghaemi et al., 2001; Angst et al., 2003, 2011; Goodwin and Jamison, 2007; Gilman et al., 2012; Takeshima et al., 2013). These factors were reported in separate studies and not considered all together. However, a recent study (Takeshima et al., 2013) used multivariate and Bayesian analyses with 199 depressed subjects to identify five factors independently associated with diagnoses of “soft” bipolar disorder (excluding bipolar-I): family history of bipolar disorders, cyclothymic temperament, early age at a first depressive episode, more depressive episodes, and depressive-mixed states. The present study is the first to identify seven factors that significantly and independently predicted diagnoses of bipolar disorder among patients with a first-lifetime depressive episode. In the present, large sample of mood-disorder patients who presented initially in a major depressive episode, later diagnoses of bipolar disorder were made in 30% of subjects. In both bipolar and unipolar cases, the average time of observation averaged 13 years, sufficient to minimize the risk of missing episodes of mania or hypomania. In considering the strength of association of predictive factors with later diagnoses, we found that the presence of 2–4 factors per person especially strongly distinguished bipolar from unipolar cases (Table 2). Based on Bayesian analyses, even the presence of any two factors differentiated bipolar from unipolar disorders and correctly categorized two-thirds of subjects (Table 3 and Figure). Of note, among the seven factors identified as being selectively associated with later diagnoses of bipolar disorder, multivariate modeling indicated that they differed in their strength of association. Strongest association (all p≤0.0003) with bipolar disorder was found with four factors, ranking: ≥4 depressive episodes, suicidal acts, cyclothymic temperament, and family history of bipolar disorder. Other factors (p=0.007 to 0.04) ranked: substance abuse, younger onset, and male sex (Table 2). Another reported factor of major importance is mood-switching during exposure to antidepressants or other mood-elevating substances. We did not consider sustained mood-elevation during antidepressant treatment as a predictive factor since such reactions can be considered tantamount to a diagnosis of bipolar disorder.
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Identification of factors anticipating DSM diagnoses of bipolar disorder can contribute to guiding prognosis and clinical management, as well as earlier identification of patients who may be likely to later attain diagnostic criteria for bipolar disorder. In fact, four of the identified factors (family history, sex, onset-age, and temperament), can usually be assessed at initial depressive episodes and so may be of particular importance for prognosis and for treatment. When these four factors were analyzed separately, the likelihood ratio in logistic regression modeling was highly significant. Increased probability of later meeting diagnostic criteria for a bipolar disorder should help to guide treatmentselection, including timely consideration of the use of mood-stabilizers and cautious use of antidepressants (Baldessarini, 2013; Pacchiarotti et al., 2013). In short, we suggest that the presence of multiple predictive factors associated selectively with bipolar disorder should raise suspicion about the risk of emergence of spontaneous or drug-associated mania, even with a current diagnosis of unipolar major depression. The present study addressed differentiation of bipolar from major depressive disorders, but leaves unresolved the nature and extent of major affective disorders—questions pursued, but not resolved, since the time of Kraepelin (Trede et al., 2005). Are bipolar disorder and major depressive disorder two different illnesses or variations in the expression of one? In particular, it is not clear if a patient who experiences one or more depressive episode has had the potential for bipolarity from the start, or may change spontaneously in syndrome-type over time, or in response to environmental factors, perhaps including life-events or effects of treatments (Goldberg et al., 2001; Judd et al., 2003; Akiskal and Benazzi, 2005; Citrome and Goldberg, 2005; Seemüller et al., 2010; Gilman et al., 2012; Phillips and Kupfer, 2013). Such questions may eventually yield to genetic and other biological methods to distinguish specific disorders (Szczepankiewicz, 2013; ENIGMA, 2014; Kerner, 2014). In particular, genetic analysis may help to distinguish bipolar and unipolar mood-disorder phenotypes and to identify depressed patients at increased risk of later mania.
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This study has important limitations. Not all patients with initial depression provided information about all predictive factors for the diagnosis of bipolar disorder. In addition, assessment of initial and early types of morbidity was retrospective, and recall of reported first-episodes may reflect memorable severity, special effects of treatment, or need for hospitalization, and not necessarily represent some details of accurately, including presence of psychotic or mixed symptoms and other details of the nature of depressive episodes. Nevertheless, it is likely that recall bias would arise similarly among bipolar and unipolar disorder patients and so limit distortion of their comparison. In conclusion, we found seven factors which predicted that an initially depressed person would later meet diagnostic criteria for a bipolar disorder in a sample of more than 2000 mood-disorder patients. Such efforts are encouraged by the high proportion of initial depression among patients bipolar disorder patients, by the substantial proportion of patients presenting initially in depression but later meeting DSM diagnostic criteria for a bipolar disorder, and by the typically prolonged delay to diagnosis of bipolar disorder from such onsets. It is important to ascertain diagnoses and formulate prognoses early so as to guide planning for optimal clinical care of mood-disorder patients, including timely consideration of mood-stabilizing medicines and cautious use of antidepressants so as to limit risk of unanticipated and potentially dangerous mood-switching. References Aguiar-Ferreira, A.D., Vasconcelos, A.G., Neves F.S., Laks, J., Correa, H., 2013. Affective temperaments: familiality and clinical use in mood disorders. J. Affect. Disord. 148, 53-56. Akiskal, H.S., Benazzi, F., 2005. Atypical depression: a variant of bipolar II or a bridge between unipolar and bipolar II? J. Affect. Disord. 84, 209-217. Akiskal, H.S., Maser, J.D., Zeller, P.J., Endicott, J., Coryell, W., Keller, M., Warshaw, M., Clayton, P., Goodwin, F., 1995. Switching from 'unipolar' to bipolar II. An 11-year prospective study of clinical and temperamental predictors in 559 patients. Arch. Gen. Psychiatry. 52, 114-123. Angst, J., Azorin, J.M., Bowden, C.L., Perugi, G., Vieta, E., Gamma, A., Young, A.H., 2011. Prevalence and characteristics of undiagnosed bipolar disorders in patients with a major depressive episode: the BRIDGE study. Arch. Gen. Psychiatry. 68, 791-798.
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Angst, J., Felder, W., Frey, R., Stassen, H.H., 1978. Course of affective disorders. I. Change of diagnosis of monopolar, unipolar, and bipolar illness. Arch. Psychiatr. Nervenkr. 226, 57-64. Angst, J., Gamma, A., Benazzi, F., Ajdacic, V., Eich, D., Rössler, W., 2003. Toward a re-definition of subthreshold bipolarity: epidemiology and proposed criteria for bipolar-II, minor bipolar disorders and hypomania. J. Affect. Disord. 73, 133-146. Angst. J., Sellaro, R., Stassen, H.H., Gamma, A., 2005. Diagnostic conversion from depression to bipolar disorders: results of a long-term prospective study of hospital admissions. J. Affect. Disord. 2005; 84, 149157. Baldessarini, R.J., 2013. Chemotherapy in Psychiatry, third edition. New York: Springer Press, New York, NY. Baldessarini, R.J., Faedda, G.L., Offidani, E., Vázquez, G.H., Marangoni, C., Serra, G., Tondo, L., 2013. Antidepressant-associated mood-switching and transition from unipolar major depression to bipolar disorder: a review. J. Affect. Disord. 148, 129-135. Baldessarini, R.J., Tondo, L., Vázquez, G.H., Bolzani, L., Khalsa, H.-M.K., Lai, M., Lepri, B., Lolich, M., Maffei, P.M., Salvatore, P., Tohen, M., 2012. International study of functional and symptomatic outcome versus onset-age in 1437 bipolar-I disorder patients. World Psychiatry. 11, 40-46. Baldessarini, R.J., Tondo, L., Visioli, C., 2014. First-episode types in bipolar disorder: predictive associations with later illness. Acta Psychiatr. Scand. 129, 383-392. Catalano, V., Harnic, D., Di Nicola, M., Mazza, M., Martinotti, G., Bruschi, A., Di Felice, C., Carnevale, E., Marano, G., Pozzi, G., Bria, P., Janiri, L., 2011. Diagnosis of deficit and attention/hyperactivity disorders (ADHD) in patients with bipolar or unipolar depression: an experimental study [Italian]. Clin. Ter. 162, 107111. Chen, M.H., Su, T.P., Chen, Y.S., Hsu, J.W., Huang, K.L., Chang, W.H., Chen, T.J., Bai, Y.M., 2013. Higher risk of developing mood disorders among adolescents with co-morbidity of attention deficit hyperactivity disorder and disruptive behavior disorder: a nationwide prospective study. J. Psychiatr. Res, 47, 1019-1023. Citrome, L., Goldberg, J.F., 2005. The many faces of bipolar disorder: how to tell them apart. Postgrad. Med. 117, 15-23. Coryell, W., Endicott, J., Maser, J.D., Keller, M.B., Leon, A.C., Akiskal, H.S., 1995. Long-term stability of polarity distinctions in the affective disorders. Am. J. Psychiatry. 152, 385-390. Dudek, D., Siwek, M., Zielińska, D., Jaeschke, R., Rybakowski, J., 2013. Diagnostic conversions from major depressive disorder into bipolar disorder in an outpatient setting: results of a retrospective chart review. J. Affect. Disord. 144, 112-115. ENIGMA Consortium, 2014. large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. [Epub ahead of print (8 Jan)].
Bipolar versus unipolar depression
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Fiedorowicz, J.G., Endicott, J., Leon, A.C., Solomon, D.A., Keller, M.B., Coryell, W.H., 2011. Sub-threshold hypomanic symptoms in progression from unipolar major depression to bipolar disorder. Am. J. Psychiatry. 168, 40-48 [and written communication from Dr. Fiedorowicz of 28 Jan 2014]. Geller, B., Zimerman, B., Williams, M., Bolhofner, K., Craney, J.L., 2001. Bipolar disorder at prospective followup of adults who had prepubertal major depressive disorder. Am. J. Psychiatry. 158, 125-127. Ghaemi, S.N., Ko, J.M., Goodwin, F.K., 2001. Bipolar spectrum and the antidepressant view of the world. J. Psychiatr. Pract. 7, 287-297. Gilman, S.E., Dupuy, J.M., Perlis, R.H., 2012. Risks for the transition from major depressive disorder to bipolar disorder in the National Epidemiologic Survey on Alcohol and Related Conditions. J. Clin. Psychiatry. 73, 829-836. Goldberg, J.F., Harrow, M., Whiteside, J.E., 2001. Risk for bipolar illness in patients initially hospitalized for unipolar depression. Am. J. Psychiatry. 158, 1265-1270. Goodwin, F.K., Jamison, K.R., 2007. Manic-Depressive Illness, second edition. Oxford University Press, New York, NY, pp. 1-19, 122-123. Judd, L.L,, Akiskal, H.S., Schettler, P.J., Coryell, W., Maser, J., Rice, J.A., Solomon, D.A., Keller, M.B., 2003. The comparative clinical phenotype and long term longitudinal episode course of bipolar I and II: a clinical spectrum or distinct disorders? J. Affect. Disord. 73, 19-32. Kerner, B., 2014. Genetics of bipolar disorder. Appl. Clin. Genet. 7, 33-42. Leyton, F., Barrera, A., 2010. Bipolar depression and unipolar depression: differential diagnosis in clinical practice [Spanish] Rev. Med. Chile 138, 773-779. Mechri, A., Kerkeni, N., Touati, I., Bacha, M., Gassab, L., 2011. Association between cyclothymic temperament and clinical predictors of bipolarity in recurrent depressive patients. J. Affect. Disord. 132, 285-258. Pacchiarotti, I., Bond, D.J., Baldessarini, R.J., Nolen, W.A., Grunze, H., Licht, R.W., Post, R.M., Berk, M., Goodwin, G.M., Sachs, G.S., Tondo, L., Findling, R.L., Youngstrom, E.A., Tohen, M., Undurraga, J., González-Pinto, A., Goldberg, J.F., Yildiz, A., Altshuler, L.L., Calabrese, J.R., Mitchell, P.B., Thase, M.E., Koukopoulos, A., Colom, F., Frye, M., Malhi, G.S., Fountoulakis, K.N., Vázquez, G., Perlis, R.H., Ketter, T.A., Cassidy, F., Akiskal, H., Azorin, J.-M., Valentí, M., Mazzei, D.H., Lafer, B., Kato, T., Mazzarini, L., Martínez-Aran, A., Parker, G., Souery, D., Özerdem, A., McElroy, S.L., Girardi, P., Bauer, M., Yatham, Y.N., Zarate, C.A. Jr., Nierenberg, A.A., Birmaher, B., Kanba, S., El-Mallakh, R.S., Serretti, A., Rihmer, Z., Young, A.H., Kotzalidis, G.D., MacQueen, G.M., Bowden, C.L., Ghaemi, S.N., Lopez-Jaramillo, C., Rybakowski, J., Ha, K., Perugi, G., Kasper, S., Amsterdam, J.D., Hirschfeld, R.M., Kapczinski, F., Vieta, E., 2013. International Society for Bipolar Disorders task-force report on antidepressant use in bipolar disorders. Am. J. Psychiatry. 170, 1249-1262. Parker, G., Fletcher, K., McCraw, S., Futeran, S., Hong, M., 2013. Identifying antecedent and illness course variables differentiating bipolar I, bipolar II and unipolar disorders. J. Affect. Disord. 148, 202-209.
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Bipolar versus unipolar depression
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Perris, C., 1966. A study of bipolar (manic-depressive) and unipolar recurrent depressive psychoses. Acta Psychiatr. Scand. Suppl. 194, 83-91. Phillips, M.L., Kupfer D.J., 2013. Bipolar disorder diagnosis: challenges and future directions. Lancet. 381. 16631671. Rao, U., Ryan, N.D., Birmaher, B., Dahl, R.E., Williamson, D.E., Kaufman, J., Rao, R., Nelson, B., 1995. Unipolar depression in adolescents: clinical outcome in adulthood. J. Am. Acad. Child Adolesc. Psychiatry. 34, 566-578. Regier, D.A., Farmer, M.E., Rae, D.S., Locke, B.Z., Keith, S.J., Judd, L.L., Goodwin, F.K., 1990. Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 264, 2511-2518. Scheffer, R.E., Niskala-Apps, J.A., 2004. Diagnosis of preschool bipolar disorder presenting with mania: open pharmacological treatment. J. Affect. Disord. 82 (Suppl. 1), S25-S34. Seemüller, F., Riedel, M., Dargel, S., Djaja, N., Schennach-Wolff, R., Dittmann, S., Möller, H.J., Severus, E., 2010. [Bipolar depression. Spectrum of clinical pictures and differentiation from unipolar depression [German]. Nervenarzt. 81, 531-538. Szczepankiewicz, A., 2013. Evidence for single nucleotide polymorphisms and their association with bipolar disorder. Neuropsychiatr. Dis. Treat. 9, 1573-1582. Takeshima, M., Oka, T., 2013. Comprehensive analysis of features that suggest bipolarity in patients with a major depressive episode: which is the best combination to predict soft-bipolarity diagnosis? J. Affect. Disord. 147, 150-155. Tondo, L., Lepri, B., Baldessarini, R.J., 2007. Risks of suicidal ideation, attempts and suicides among 2826 men and women with types I and II bipolar, and recurrent major depressive disorders. Acta Psychiatr. Scand. 116, 419-428. Tondo, L., Lepri, B., Cruz, N., Baldessarini, R.J., 2010. Age at onset in 3014 Sardinian bipolar and major depressive disorder patients. Acta Psychiatr. Scand. 121, 446-452. Trede, K., Salvatore, P., Baethge, C., Gerhard, A., Maggini, C., Baldessarini, R.J., 2005. Manic-Depressive Illness: evolution in Kraepelin’s Textbook, 1883-1926. Harv. Rev. Psychiatry. 13, 155-178.
Table 1. Characteristics of bipolar disorder versus unipolar major depressive disorder patients with depressive first-episodes Characteristic
Prevalence or Mean ± SD
Rate
Statistic
p-value
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Bipolar (n)
Ratio (BD/UD)
Unipolar (n)
2
(χ or t)
Men (%) 39.1 642 30.7 1504 1/1.27 14.2 Family history (%) Any psychiatric illness 71.3 548 63.3 972 1.13 10.2 Mood disorders 62.8 548 51.1 972 1.23 19.2 Bipolar disorder 29.4 548 12.7 972 2.31 64.5 Suicide 8.21 548 4.63 972 1.77 8.07 Onset age 29.8±13.3 642 34.1±15.3 1504 1/1.14 37.8 Years Onset <25 years (%) 45.3 642 33.9 1504 1.34 25.1 Cyclothymic vs. other temperaments 22.4 490 9.35 695 2.40 39.2 a (%) Depressions before intake ≥4 Depressions (%) 37.0 611 16.1 669 2.31 72.0 Depressions/year 1.14±2.52 611 0.44±0.65 669 2.59 48.7 Duration of depressive episodes 5.24±4.88 605 7.31±13.8 463 1/1.39 11.6 (months) Suicidal (%) Any suicidal behavior or thinking 52.9 624 24.7 1460 2.14 156 Suicidal acts vs. ideation 22.6 624 5.62 1460 4.02 132 b Occurrence of other disorders (%) 53.6 293 63.5 345 1/1.18 6.41 Any concurrent disorder 14.7 620 4.02 1468 3.66 74.3 Substance-abuse Total N=2146: bipolar disorder (BD) 642 (29.9%), unipolar recurrent major depressive disorder (UD) 1504 (70.1%). All degrees-of-freedom [df] = 1). a. Compared to lack of a clinically categorizable temperament type. b. Lifetime risk vs. absence of such disorders, based on clinical assessment; substance abuse includes illicit drug use and alcohol-abuse. Factors in boldface were used in multivariate modeling, as having the strongest differentiating ability within each category.
Table 2. Logistic regression model: factors associated with bipolar disorder (BD) versus unipolar depressive disorder among patients with an initial depressive episode Factors ≥4 Depressive episodes before intake a Suicidal acts Cyclothymic versus other b Family history for bipolar disorder c Substance abuse Younger median onset-age Male sex
Present in BD (%)
OR
95% CI
χ
39.3 22.7 23.1 30.4 18.2 57.5 38.1
2.74 3.50 2.27 1.99 1.96 1.01 1.41
1.93–3.89 2.16–5.65 1.46–3.54 1.37–2.90 1.20–3.19 1.00–1.03 1.01–1.96
31.2 26.0 13.1 13.1 7.32 6.97 4.27
2
p-value <0.0001 <0.0001 0.0003 0.0003 0.007 0.008 0.04
<0.0001 0.001 <0.0001 <0.0001 0.004 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0006 <0.0001 <0.0001 0.01 <0.0001
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2
Factors listed in rank order of statistical significance (χ ); the overall likelihood ratio of the model is highly 2 significant (df=6; χ =148, p<0.0001) among 812 subjects (428 [52.7%] with a bipolar disorder and 384 [47.3%] with recurrent depressive disorder in whom the characteristics could be compared. a. Gestures and attempts. b. First-degree family history. c. All use or abuse of illegal substances or abuse of alcohol. Other factors not significantly related to bipolar disorder included psychiatric co-morbidity, ADHD (attention disorder with hyperactivity), depressive episodes/year, average duration of depressive episodes, lifetime hospitalization, and HDRS (21-item Hamilton Depression Rating Scale) score at the start of a later depressive episode.
Table 3. Numbers of factors selectively associated with bipolar disorder Distribution of Risk Factors Factors (n)
0 1 2 3 4 5 6 7
Total (n [%])
Bipolar (n [%])
134 (16.5) 230 (28.3) 217 (26.7) 140 (17.2) 65 (8.00) 24 (1.50) 2 (0.25) 0 (0.00)
37 (8.64) 88 (20.6) 119 (27.8) 107 (25.0) 53 (12.4) 22 (5.14) 2 (0.47) 0 (0.00)
Unipolar (n [%]) 97 (25.3) 142 (37.0) 98 (25.5) 33 (8.59) 12 (3.12) 2 (0.52) 0 (0.00) 0 (0.00)
Bayesian Analysis Correctly SensitivitySpecificity Classified p-value (%) (%) (%)
OR [95%CI]
χ
1.00
–––
–––
100.0
0.00
52.7
1.62 [1.02–2.58]
4.22
0.04
91.4
25.3
60.1
3.18 [2.00–5.05]
24.0
<0.0001
70.8
62.2
66.8
8.47 [4.93–14.7]
59.5
<0.0001
43.0
87.8
64.2
11.6 [9.26–23.8]
43.0
<0.0001
18.0
96.4
55.1
28.6 [6.45–125]
19.4
0.002
5.61
99.5
50.0
[0–∞] 0.001
0.98
0.47
100.0
47.5
–––
0.00
100.0
47.3
–7
7.04x10
–––
2
–––
Shows the numbers and proportions (%) of subjects with stated numbers of factors (from Table 2; none had all 7), among subjects later diagnosed with a bipolar disorder (BD) or a unipolar depressive disorder (UD), with Odds Ratios (OR; and their 95% confidence intervals). Differentiation of future diagnoses of bipolar vs. unipolar disorder was maximal when any 2–4 antecedent factors were present per person. By Bayesian analysis, optimal sensitivity (70.8%) and specificity (62.2%) was found with any 2 factors per subject, with 66.8% of subject correctly classified by diagnostic type.
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Figure Legend
Figure 1. Bayesian receiver-operating characteristic (ROC) curve (sensitivity versus 1–specificity, or true positive versus false positive rates; dotted straight line is the null baseline) for increasing numbers of factors identified by multivariate regression modeling (Table 2) that differentiate bipolar disorders from recurrent depressive disorder. The computed area-under-the-curve (AUC) was 0.72 [95% CI: 0.68–0.75]. Optimal differentiation of bipolar from recurrent depressive disorders was with ≥2 factors (the point on the ROC curve most displaced from the null line, with sensitivity = 70.8%, specificity = 62.2%), which correctly classified 66.8% of cases.
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100
75
50
25
0 0
25
50
75
100
1 – Specificity (False Positive Rate) Acknowledgements: Supported in part by the Aretæus Research Fund and Stanley Medical Research Institute (to LT), by a Sardinia Regional Master-and-Back Fellowship (to CV), a grant from the Bruce J. Anderson Foundation and by the McLean Private Donors Psychopharmacology Research Fund (to RJB). Disclosures: No author or immediate family member has financial relationships that might represent a conflict of interest in the work presented: none receives research support, is a consultant to, or is on a speakers’ panel of a pharmaceutical or biomedical corporation, nor holds equity positions in such corporations.
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Authors: Leonardo Tondo, M.D., M.Sc.; Caterina Visioli, Psy.D. Antonio Preti, M.D., and Ross J. Baldessarini, M.D.