Abstract
Depression is a heterogeneous condition, with multiple possible symptom-profiles leading to the same diagnosis. Descriptive depression subtypes based on observation and theory have so far proven to have limited clinical utility. To identify depression subtypes and to examine their time-course and prognosis using data-driven methods. Latent transition analysis was applied to a large (N = 8380) multi-service sample of depressed patients treated with cognitive behavioral therapy (CBT) in outpatient clinics. Patients were classed into initial latent states based on their responses to the Patient Health Questionnaire-9 of depression symptoms, and transition probabilities to other states during treatment were quantified. Qualitatively similar states were clustered into overarching depression subtypes and we statistically compared indices of treatment engagement and outcomes between subtypes using post hoc analyses. Fourteen latent states were clustered into five depression subtypes: mild (2.7%), severe (9.8%), cognitive-affective (23.7%), somatic (21.4%), and typical (42.4%). These subtypes had high temporal stability, and the most common transitions during treatment were from severe toward milder states within the same subtype. Differential response to treatment was evident, with the highest improvement rate (63.6%) observed in the cognitive-affective subtype. Replicated evidence indicates that depression subtypes are temporally stable and associated with differential response to CBT.
Highlights
Depression, a highly common mental health problem that affects approximately 264 million people worldwide (James et al, 2018), is characterized by a wide range of symptoms, including cognitive, affective and somatic indicators
An alternative argument is that depression is a highly heterogeneous condition (Goldberg, 2011)— potentially characterized by various subtypes—and clinical outcomes could be improved if treatment was based on more precise assessments of each individual's symptom profile (Fried, 2017)
Latent transition analysis (LTA) is an extension of latent class analysis (LCA) which uses longitudinal data to explore transitions between classes over time (Ni et al, 2017; Ulbricht et al, 2016). This technique is better suited to examine how patients with different depression subtypes respond to treatment, which is potentially informative for personalized treatment planning
Summary
Depression, a highly common mental health problem that affects approximately 264 million people worldwide (James et al, 2018), is characterized by a wide range of symptoms, including cognitive (e.g., repetitive negative thoughts, suicidal ideas), affective (e.g., anhedonia, avolition) and somatic (e.g., problems with sleep, psychomotor disturbances) indicators. Latent transition analysis (LTA) is an extension of LCA which uses longitudinal data to explore transitions between classes over time (Ni et al, 2017; Ulbricht et al, 2016) This technique is better suited to examine how patients with different depression subtypes respond to treatment, which is potentially informative for personalized treatment planning. The present study aimed to address a gap in knowledge concerning the generalizability and clinical utility of depression subtyping based on LTA methods To this end, we applied the methods used by Catarino et al (2020) in a large multi‐service sample of depressed patients accessing routinely‐delivered CBT in community (outpatient) settings
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