An empirically derived approach to the classification and diagnosis of mood disorders
Authors: DREW WESTEN, JOHANNA C. MALONE, JARED A. DEFIRE
Journal: World Psychiatry (2012)

Two questions are at the heart of psychiatric diagnosis: how to classify psychopathology, and how to apply that taxonomy to diagnose patients, particularly in practice. Initial efforts to address the first question with respect to mood disorders involved a clinical expert approach, in which the premiere psychopathologists of the early 20th century attempted to find order in the clinical cases they were seeing 1. This remained the predominant approach until the 1970s, when criteria designed for research purposes proved useful in standardizing diagnosis across sites. Versions of these criteria became the official taxonomy of affective disorders in the DSM-III 2. Since that time, researchers have gradually honed the criteria for the various disorders for both the DSM and ICD, but this has dramatically increased the number of disorders, reflecting in part the recognition that mood disorders are spectrum disorders, with patients displaying a range of symptom presentations 3,4,5,6. With the emergence of research-based criteria, a new approach came not only to classification (the first question central to psychiatric diagnosis) but also to assigning diagnoses in practice using that taxonomy (the second question). The DSM-III and subsequent editions of the DSM and ICD provided highly specific criteria and algorithms for combining those criteria into a categorical diagnosis. Advantages included substantially higher interrater reliability, at least for research purposes 7,8. Over time, however, a number of disadvantages became apparent, including tradeoffs between validity and reliability, with cutoffs for diagnosis, severity, and duration of illness often arbitrary; and an increasing number of disorders and not-otherwise-specified (NOS) diagnoses (see 9). Further, a consensus has emerged across a range of disorders, including mood disorders, that categorical (present/absent) diagnosis does not reflect the nature of clinical reality as well as dimensional diagnosis (the extent to which a syndrome is present) 10,11,12. For decades, dimensional diagnosis has actually been the "unofficial" norm in research, with instruments such as the Hamilton Rating Scale for Depression 13 and the Beck Depression Inventory 14 used to measure both severity of depression and treatment response, given that a patient in a clinical trial can fall just below threshold but remain highly symptomatic. Another emerging problem was artifactual comorbidity 15,16, which reflects a number of causes, including overlapping criterion sets and the nature of spectrum pathology, in which clear demarcations among disorders may be lacking. A related problem is clinical utility. The mood disorder section of DSM-IV comprises nearly 100 pages of text and includes so many disorders, each with its own criteria, subcriteria, and cut-points, that clinicians find them minimally useful and hence often do not use the manual as designed, instead relying on mental prototypes they have built up over the course of training and experience 17,18. At this point it is worth asking whether there may be other ways of addressing both questions: how to identify diagnostic syndromes and criteria and how to diagnose them in clinical practice. With respect to the first question, a straightforward empirical alternative comes from psychometrics and scale development. A diagnostic manual is, in essence, a set of "scales" (criterion sets) for measuring constructs (diagnoses), which could be optimized empirically by applying procedures such as factor analysis to large, comprehensive item sets comprising potential diagnostic criteria. Factor analysis can identify dimensions (e.g., major depression, dysthymia, mania) and criteria (items with high factor loadings on these dimensions) that could be used to select and construct diagnoses that could be treated both dimensionally and categorical (as occurs in much of medicine, e.g., when a patient who crosses a dimensional threshold is diagnosed as having "high" or "borderline high" blood pressure), without the necessity of complex algorithms. With respect to the second question, a method gaining momentum for both axis I and axis II disorders is prototype matching, which combines diagnostic criteria into standardized prototypes 7,19,20. Clinicians diagnose the similarity or "match" between a patient's clinical presentation and a set of paragraph-length prototypes, considering each prototype as a whole, working with, rather than against, the naturally occurring ways humans tend to categorize 21,22. Prototype matching preserves a syndromal approach to diagnosis 23, consistent with both DSM and ICD diagnosis, while allowing dimensional assessment, using a scale from 1 (no match between the patient's clinical presentation — the default diagnosis) through 5 (very good match). Where categorical diagnosis is desired (e.g., to facilitate clinical communication), ratings >4 indicate presence of the disorder and ratings of 3 indicate "features". Thus, rather than distinguishing multiple categories of bipolar disorder, a patient would be diagnosed for current symptoms as well as history of the extent to which he or she had matched prototypes of major depression and mania. A patient who currently or previously manifested manic features at a level of 3 or above would be diagnosed with bipolar spectrum illness, with the nature of that illness defined by severity of manic/hypomanic and depressive episodes. For clinical communication, patients who experienced subthreshold features of both mania and major depression could be described as "cyclothymic", and for research purposes, the combination of ratings on each of the 1–5 scales (or more reliable scales developed via factor analysis) could help identify candidates for different forms of treatment or predict important outcomes such as global functioning. In this study we derived diagnostic syndromes factor-analytically, producing a set of dimensions that obviates the need for complex diagnostic algorithms. As part of a larger study on the classification of personality pathology, we contacted a random North American sample of 1201 psychiatrists and psychologists with at least 5 years of practice experience post-training 8,24. Because clinicians provided all data and no patient identifying information was disclosed to the investigators, clinicians rather than patients provided informed consent, as approved by the Emory University Institutional Review Board. We asked clinicians to describe "an adult patient you are currently treating or evaluating who has enduring patterns of thoughts, feelings, motivation, or behavior — that is, personality patterns — that cause distress or dysfunction". However, we emphasized that patients need not have a DSM-IV personality disorder diagnosis. Patients met the following additional inclusion criteria: >18 years of age, not currently psychotic, and known reasonably well by the clinician (using the guideline of >6 clinical contact hours but less than 2 years to minimize confounds due to treatment). To ensure random selection of patients, clinicians consulted their appointment calendars to select the last patient they saw during the previous week who met study criteria. Clinicians completed the Clinical Data Form (CDF) using data obtained over the course of regular interactions with the patient and any available prior data (e.g., from psychiatric records). The CDF gathers information on demographic, diagnostic, etiological (family and developmental history), and adaptive functioning variables (e.g., 16) from clinically experienced observers. CDF data show strong agreement with data collected independently from patients 25. To assess axis I diagnoses as made naturalistically in clinical practice, we asked clinicians to make present/absent diagnoses of each of the most prevalent disorders in DSM-IV. For family history variables, to cue memory and maximize reliability and validity, we asked specifically about first and then about second degree relatives and instructed clinicians to respond conservatively if they were unsure. This study utilizes data from a random subsample of 187 clinicians who completed the Mood Disorder Diagnostic Questionnaire (MDDQ). This is a 79-item questionnaire designed to allow clinically experienced informants to rate patients on symptoms of mood disorders written in the form of diagnostic criteria so they can be used both as scales for measuring mood disturbance and for identifying diagnostic groupings and criteria empirically. The items were derived from current mood disorder criteria as well as research and clinical observation 26,27. Items are scored on a 7-point scale. The 187 clinician respondents were highly experienced (20.1±7.7 years practice experience) and diverse in theoretical orientation, with no single orientation endorsed by >25% of the sample. We subjected the MDDQ items to exploratory factor analysis, using percent of variance accounted for, the scree plot, parallel analysis 28,29, replicability across estimation procedures and rotations, and conceptual coherence of the obtained factors to select the final number of factors rotated. We used a Promax (oblique) rotation because of our expectation of correlated factors. We report here the unweighted least squares (ULS) solution. After identifying diagnostic groupings empirically, we created psychometric scales to assess each disorder by selecting the items with the highest factor loadings and deleted items with low item-to-scale correlations or high cross-correlations (e.g., r > .60) with other scales. We assessed initial validity of the diagnostic dimensions in a number of ways. First, to evaluate the accuracy of a dimensional scale against a dichotomous standard (e.g., presence/absence of major depression as diagnosed by the treating clinician), a receiver operating characteristic (ROC) curve charts the relationship between true positive and false positive identification rates at each cut-point on the dimensional scale. The area under the curve (AUC) is then calculated as a summary statistic of the test's ability to classify individuals correctly. The AUC statistic allows for comparison of a test's discriminative ability against chance (where AUC ≤.50 suggests chance discrimination). We used the AUC and logistic regression (testing whether empirically derived diagnoses predicted only the diagnosis they were expected to predict) vis-à-vis naturalistic DSM-IV clinical diagnoses assessed from the CDF to assess validity (diagnostic accuracy). Second, to compare the relative validity of clinicians' categorical DSM-IV diagnoses and the empirically derived dimensions, we correlated both sets of diagnoses with Global Assessment of Functioning (GAF) from the DSM-IV, a composite measure of psychiatric functioning (hospitalizations and suicide attempts), and family history of major depression and bipolar disorder. We then utilized hierarchical multiple regression, predicting GAF scores and psychiatric history from all of the DSM-IV mood disorders as diagnosed naturalistically in Step 1 and the empirically derived syndromes in Step 2, to see whether the empirically derived diagnoses showed incremental validity over DSM-IV diagnosis in clinical practice. Finally, we constructed paragraph-length diagnostic prototypes from these scales that could be used in everyday practice. Patients were 42.8% female, 69% treated in private practice (with the remainder seen in a range of settings, from outpatient clinics to forensic units), 73.3% Caucasian (with the remainder primarily African-American and/or Hispanic), with a mean age of 42.6±11.5 years, and spanning all social classes. GAF scores spanned a broad range, from 10 to 92 (mean 58.4±12.2); 28.9% of the sample had had at least one psychiatric hospitalization. Clinicians knew the patients well, with an average of 17.3±26.5 months in treatment. Factor analysis suggested a 4 or 5 factor solution, with the 4-factor solution yielding 4 coherent dimensions, accounting for 44.0% of the variance. We labeled the factors "major depression", "dysthymia", "mania" and "suicide risk", and created psychometric scales comprised of 6 to 19 items. The internal consistency (reliability) of each scale was very high (Table 1). We expected substantial cross-correlations among the scales because they were all measuring mood pathology and because research has shown mood disorders to be part of a broader internalizing spectrum 30,31. Intercorrelations were moderate as predicted, suggesting independent disorders within a broader spectrum (Table 2). The scales showed high diagnostic accuracy. Table 3 presents AUC performance statistics for each dimension in classifying patients with respect to either their clinical diagnosis or history of suicide attempts. The stepwise logistic regression analyses presented in Table 4 illustrate that the scales also have high discriminant validity (i.e., they are able to distinguish near-neighbor disorders). The dimensions predicted the diagnostic constructs they were expected but not those they were not expected to predict. For example, major depression significantly predicted only a major depressive disorder clinical diagnosis; dysthymia significantly predicted dysthymic disorder diagnosis alone; mania scores predicted bipolar I, bipolar II, and cyclothymic diagnoses but not major depressive disorder or dysthymic disorder (unipolar spectrum disorders); and suicide risk scores significantly predicted prior suicide attempts. As a further test of validity (Table 5), we compared the mood disorder dimensions with clinicians' DSM-IV diagnoses in predicting adaptive functioning, psychiatric history, and family history of major depression and bipolar disorder. The empirically derived diagnoses showed a predictable pattern of associations with these variables, with higher correlations than clinicians' DSM-IV diagnoses. To assess the incremental validity of the empirically derived dimensions relative to DSM-IV categorical diagnoses, we used hierarchical linear regression to GAF scores and a composite measure of psychiatric history (psychiatric hospitalizations and suicide attempts), entering DSM-IV diagnoses in Step 1 and empirically-derived diagnoses in Step 2 (Table 6). In both cases the empirically derived dimensions outperformed DSM-IV diagnoses, accounting for incremental variance over and above the mood disorder diagnoses. Variance inflation factors ranged from 1.2 to 1.8, with a mean of 1.4, indicating no violations of multicollinearity assumptions of multiple regression. Finally, we used the statements from the MDDQ that comprised the scales for each disorder to construct prototypes that could be used in DSM-5 or ICD-11 to diagnose patients in everyday practice (see example of major depression prototype in Figure 1). Diagnostic prototype of major depression Despite enormous advances in the understanding of mood disorders, more than thirty years of incremental adjustments to the categories and criteria has produced a burgeoning number of disorders and diagnostic algorithms that are unwieldy for clinical use. The current diagnostic systems in widespread use today reflect tremendous wisdom drawn from research, theory, and practice. Our study built on this wisdom by including criteria from the DSM-IV and ICD-10 as well as items drawn from research and clinical observation into the instrument from which we derived diagnostic dimensions (e.g., 13,14,27). Factor-analytic results were concordant with the major diagnoses that have evolved over a century of psychiatric classification, identifying three disorders (major depression, dysthymia, and mania) as well as markers for suicide risk. Whereas major depression and dysthymia could have been so highly correlated that factor analysis could not distinguish them, they were in fact clearly distinct. Empirically derived criteria for these disorders were a mixture of current diagnostic criteria, descriptions based on clinical observation, and other relevant research not addressed in current taxonomies, such as the distinction, common to the cognitive-behavioral theory of Beck 26 and the psychoanalytic theory of Blatt 27, of two paths to vulnerability to depressive pathology, one reflecting self-criticism and the other interpersonal concerns such as loss and rejection. The results speak to the strengths of the current nosology, given that factor analysis empirically reproduced the three major mood spectrum disorders despite the fact that the instrument included multiple items not included in the DSM or ICD and that the disorders are overlapping in phenomenology and etiology 15,30,31,32. The study provided initial evidence for the validity of the empirically derived diagnostic dimensions. Diagnostic efficiency statistics and logistic regressions indicated that the identified dimensions predicted clinicians' naturalistic diagnoses, with a pattern suggesting both convergent and discriminant validity, although future research with independent assessors is clearly necessary. Further, in correlational analysis, the empirically-derived dimensions were stronger predictors of global functioning, psychiatric history, and family history than their corresponding DSM-IV diagnoses. Perhaps most strikingly, the hierarchical regression analyses showed strong evidence of incremental validity of the empirically-derived dimensions over and above clinician's naturalistic mood disorder diagnoses in predicting GAF ratings and psychiatric history, with DSM diagnoses dropping out of the equation when the empirically derived dimensions were included. The only partial exception was for bipolar disorders, demonstrating the importance of assessing not only current symptoms (as was done with MDDQ in this study) but also past episodes, which are crucial to distinguishing unipolar and bipolar depression. Despite the ways our results were consistent with current ways of diagnosing mood disorders, the data also suggest important areas of divergence that could both enrich and simplify diagnosis. Perhaps most important is the consensus of researchers and practitioners that clinical conditions are better represented dimensionally than through multiple categorical diagnoses representing variants of the same pathological spectrum. For example, DSM-IV defines bipolar I disorder in terms of the presence of full-threshold mania combined with a history of some level of severity of depressive episodes, usually but not always meeting criteria for a major depressive episode. It defines bipolar II disorder, in contrast, in terms of a positive history of major depression combined with a history of hypomania. Cyclothymia is defined by a mixture of subthreshold major depression and subthreshold mania (hypomania). The problems with this approach are legion. Most importantly, the cutoffs for both a major depressive episode and mania are arbitrary, with major depressive episode requiring at least 5 of 9 symptoms from criterion A for an arbitrary period of two weeks, so that 4 of 9 symptoms could lead to a diagnosis of bipolar II, cyclothymia, depressive disorder NOS, or bipolar disorder NOS. A dimensional system, whether adopting the scales identified here empirically or simple 5-point diagnoses used for prototype matching from paragraph-length descriptions derived from those scales, avoids arbitrary cut-points and categorical diagnostic distinctions, but allows for multivariate research testing the question of what combinations or dosages of different treatments are more useful at what quantitative level of current or past history of major depression spectrum, dysthymia spectrum, or mania spectrum pathology. A related problem is the lack of any evidence for the superiority of the complex diagnostic algorithms that make DSM-IV so difficult to use over a simple prototype-matching approach to clinical diagnosis (or, for research purposes, a set of scales of the sort derived here empirically, in which the mean of item ratings replaces complex algorithms). Given that some patients have atypical presentations (e.g., characterized more by agitation than a subjective sense of sadness), this could readily lead a clinician or researcher to misdiagnosis a patient in the midst of a clear major depressive episode. In contrast, using a prototype matching approach, clinicians would make only three diagnostic decisions: to what extent does this patient's presentation resemble the prototypes of major depression, dysthymia, and mania (and to what extent has the patient likely met each one in the past). As the description of the prototype of major depression derived in this study indicates (Figure 1), clinicians would have little difficulty recognizing the disorders or discriminating them from each other, likely reducing artifactual comorbidity with one another as well as with near-neighbor disorders (e.g., generalized anxiety disorder). As we have argued elsewhere (see 33), for disorders for which a given patient has at least clinically significant features (prototype ratings ≥3 on a 5-point scale), clinicians could make secondary ratings of severity of the syndrome or subsyndromes (e.g., subjective experience of depression, somatic symptoms), age of onset of first episode, number of prior episodes, and presence or absence of psychotic symptoms (for both major depression and mania) or other dimensions that prove empirically or clinically useful as new research emerges, such as rapid cycling or seasonality. For dysthymia, for example, secondary ratings of duration (number of years' duration) and persistence of symptoms across the day would likely yield more information, from both a clinical and research perspective, than an a priori and arbitrary rule that the symptoms must be present most of the day every day for 2 years. The current study has several limitations. First, patients were selected by clinicians to have at least a minimum of personality pathology (because the research was part of a broader taxonomic project on personality pathology). However, no axis II personality disorder diagnosis was required. Westen and Arkowitz-Westen 34 have demonstrated that subthreshold personality pathology occurs in the vast majority of patients in treatment for axis I disorders, particularly mood disorders, so that it is unlikely that this sample differs substantially from a more completely random selection of patients seen in clinical settings. The fact that clear, coherent mood disorder factors emerged in this sample despite this potential source of bias suggests that the findings are particularly robust to sampling error. Nevertheless, future research should use both general clinical samples (e.g., from field trials) and data from research interviews (e.g., 35). Second, the study relied on a single informant per patient, the treating clinician. Previous research has supported the use of clinician-informant ratings using multiple instruments designed for clinically experienced informants, which strongly correlate with data provided by independent psychiatric interviews 36,37,38 and patient self-reports 25. Further, clinicians are the primary users of diagnostic manuals, and relying on quantified clinical observation in their design provides an important and underutilized complement to self-report and structured interview ratings. Nevertheless, future research is needed to determine both the extent to which clinician diagnoses of these empirically-derived syndromes correspond to diagnoses made by other observers, and whether prototype versions of them show the same advantages as assessments based individual items (criteria). A final limitation is the use of an exclusively North American sample. In cultures in which psychiatrists or other mental health professionals are concerned that the diagnostic criteria identified here might not include culturally relevant items, however, the 79-item set of the MDDQ could be expanded to include additional hypothesized culturally-specific indicators, and a factor analysis could readily be performed on an adequately-sized sample. The results could either support or disconfirm hypotheses about the necessity of adding criteria or syndromes relevant to that culture. The advantage of this approach is that it lets clinicians do what they do well (observe) and lets statistical procedures do what they do well (aggregate data). This research was supported by National Institute of Mental Health grants R01-MH62377 and R01-MH78100.