Radical Changes in Psychiatric Diagnosis Are on the Horizon

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In order to begin to identify transdiagnostic features of different disorders in adults, Grisanzio and colleagues (2018) worked with 497 adults of diverse demographic backgrounds, including 248 with major depressive disorder, PTSD and panic disorder, and 249 people without psychiatric diagnoses. They chose these three diagnoses to work with because they are common, and participants often had received additional diagnoses including ADHD, generalized anxiety disorder, obsessive-compulsive disorder, dysthymia and seasonal affective disorder. They excluded participants with substance use disorder, brain injury, and other conditions which would interfere with testing procedures.

They assessed participants using accepted diagnostic tools including the Hamilton Depression Rating Scale, the Structured Clinical Interview for DSM-IV, and others; self-report scales for psychiatric symptoms measuring moods, anxiety, stress, self-esteem, hopelessness, and myriad other symptoms using the Depression, Anxiety and Stress Scale; a neurocognitive battery (IntegNeuro) to assess cognitive function; EEG (electroencephalogram) to assess basic brain activity; and daily functional capacity using the Brief Risk-Resilience Index for Screening.

Data were analyzed first using “principal component analysis” to identify trends in the main clinical measures, followed by an “unsupervised” analysis using a machine-learning approach which does not require human input, but rather independently identifies significant clusters present within the data. Finally, in addition to testing the main group of 497 participants, they repeated the same measures on a completely different group of 381 adults, to provide an “independent validation sample” to confirm the validity of the results. Their findings held true for both the primary test subjects as well as the independent validation group, suggesting robust applicability.

In the basic analysis (“principal component analysis” they found that 3 factors accounted for the majority (71.2 percent) of the clinical data: anhedonia, anxious arousal, and tension. These 3 factors represent clinical symptoms across the three main diagnostic categories and co-occurring conditions. The unsupervised machine-learning analysis yielded 6 independent clusters: normative mood (the healthy control group), tension, anxious arousal, general anxiety, anhedonia, and melancholia. Here’s what that machine-learning process looks like graphically:

Tree diagram (“dendrogram”) showing derivation of clusters.

Source: Grisanzio et al, 2018

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