For example, a patient with psychopathology suggestive of borderline, antisocial, and narcissistic personality disorders in the DSM-IV system would instead be characterized as high in neuroticism (with corresponding subscales reflecting propensity for anger, irritability, stress tolerance, and so forth), low in agreeableness (antagonistic), and high in openness to experience (exaggerated mood states, preoccupation with fantasy).
However, the lack of discrete categories does not lend itself to the study of (ostensibly) distinct clinical populations, and also does not provide a straightforward answer to the question, "does this patient have a personality disorder?" Providing such a categorical answer to this question is important both for treatment planning (which often requires a categorical judgment of whether to treat the patient), and for practical concerns such as determining eligibility for disability. Moreover, most health information systems (eg, for clinical information or insurance) are not equipped to incorporate dimensional approaches.
Another diagnostic strategy that would reduce comorbidity is to impose additional diagnostic hierarchies. As mentioned above, exclusionary hierarchies are based on the concept that 1 diagnosis takes precedence over 1 or more subordinate diagnoses. The underlying assumption is that the symptoms of the subordinate diagnosis are associated features of the primary disorder (and thus do not warrant an additional psychiatric diagnosis). Some have recommended that certain Axis II diagnoses, such as borderline personality disorder, take precedence over others, such as dependent or histrionic personality disorder.13
The problem with imposing diagnostic hierarchies is that they imply a certain knowledge of symptom attribution that is at odds with the descriptive approach of DSM. Some of the remaining hierarchies, such as the exclusion of generalized anxiety disorder in the setting of comorbid major depressive disorder, have continued to draw criticism.14 While expanding the number of diagnostic hierarchies in DSM would certainly reduce comorbidity, this assumes an understanding of the etiologies of mental illnesses and increases the risk of losing clinically relevant distinctions in complex cases.
Mixed and combined diagnostic categories
Another strategy that has been used to address comorbidity is the development of mixed diagnostic categories, which lump together categories that have been separated in DSM (eg, combining major depressive disorder and generalized anxiety disorder into the single category, mixed anxiety/depression).15 One drawback of combining single categories into combined categories based on known co-occurring syndromes is that the number of possible combinations and permutations of categories (in a mathematical sense at least) could easily reach into the thousands.
Another way to reduce diagnostic comorbidity is to combine separate categories into higher-order constructs. One example of a "lumping" diagnostic strategy already in use in DSM-IV is combining the 10 specific personality disorders of DSM into 3 personality disorder clusters based on presumed common characteristics. Clinicians commonly incorporate these clusters into diagnostic formulations, such as personality disorder not otherwise specified, with cluster B traits; researchers have used these mixed categories to delineate patient populations in studies of Axis I comorbidity, treatment responsiveness, and prognosis.16
The co-occurrence of personality disorders within each cluster is generally higher than between-cluster comorbidity. For example, in the previously mentioned study by Stuart and colleagues,9 73% of patients in whom narcissistic personality disorder was diagnosed also met criteria for histrionic personality disorder, and many met criteria for a third and fourth diagnosis. Under the cluster system, comorbidity of personality disorders is reduced because the clinician would simply note "cluster B personality disorder" regardless of the number of specific disorders actually present. However, currently there is insufficient research to justify lumping the personality disorders into clusters for all diagnostic purposes.
In a seminal article on psychiatric diagnosis, Robins and Guze17 posited that validity could be enhanced via more precise clinical description, delineation of syndromes, treatment response profiles, and biological correlates. This was based on the expectation that empirical evidence would eventually become the mainstay of psychiatric diagnosis. More than 3 decades later, and just a few years before the publication of DSM-V, our understanding of the etiology and pathogenesis of mental illness is still very limited. The research that will fully illuminate our understanding of mental illness is still many years down the road.
As a field, we in psychiatry need to address the implications of our lack of understanding about pathophysiology for the use of our current diagnostic system. The "atheoretical" approach of DSM explicitly acknowledges the limits of our understanding and encourages co-occurring diagnoses in the hope that the maximum amount of clinically relevant information will be captured. Unfortunately, this also makes the current system quite cumbersome to use as it was intended. Clinicians and health information systems intrinsically place certain information at a higher order of importance, and they use clinical judgment to prioritize and perhaps omit diagnoses. Consequently, clinicians may fail to account for or communicate diagnostic complexity where it exists. Because of the unwieldy nature of making numerous diagnoses, the application of the DSM system in the real world may be veering toward more idiosyncratic use. More research is needed if psychiatry, as a field, aspires to accuracy and uniformity in diagnosis. The ultimate goal is to increase the clinical usefulness of DSM to provide better case conceptualization, communication, and accuracy of prognosis.18
Given that the next edition of DSM will be saddled with many of the same limitations as its predecessors, some practical modifications may lessen the burden of multiple diagnoses. Accepting that the maximal comorbidity strategy is too cumbersome, we could consider modifying the current multiaxial system. Rather than directing providers to list every Axis I diagnosis for which the patient meets criteria, providers could report the 1 or 2 that are the immediate focus of care, followed by a brief list of target symptoms and relevant clinical issues.
For example, a primary diagnosis of major depressive disorder could be followed by a target symptom list of "depressed mood and insomnia (severe); panic attacks (moderate); homelessness (in shelter)." In such a system, defining whether a third or fourth set of symptoms meets independent diagnostic criteria (such as whether the panic attacks are a facet of the major depressive disorder or their own clinical entity) is less important than efficiently communicating and recording diagnostic and nondiagnostic information. This may, in fact, more closely resemble how pro-viders think about treatment: starting with diagnosis, but tracking individual symptoms to monitor the effectiveness of interventions. Such a model would defer to the providers what constitutes clinically relevant information. Again, how to guide providers in making such differentiations in a consistent manner is not immediately clear and requires future investigation.
In summary, the current strategy of diagnosing maximal comorbidity may not be optimal. The practice of listing multiple diagnoses has the power to both enhance and obscure important clinical information. As DSM-V is developed, opportunities for reducing comorbidity by lumping diagnoses (eg, replacing the 8 specific paraphilias with a single disorder); formalizing conventions for omitting nonrelevant diagnoses; using a dimensional system to characterize personality pathology; and simplifying or eliminating the multiaxial system and using one that resembles a list of the kind more commonly used in medical practice should be explored. As we move forward in addressing these challenges, we must strive to implement explicit decision rules based on the best data available or risk reverting to subjective and impressionistic formulations like those used more than half a century ago in DSM-I.
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