Dataset Information

Does Symptom Linkage Density Predict Outcomes in Cognitive Therapy for Recurrent Depression?

ABSTRACT:

Background

Acute-phase cognitive therapy (CT) is an efficacious treatment for major depressive disorder (MDD), but how CT helps patients is incompletely understood. As a potential means to clarify CT mechanisms, we defined "symptom linkage density" (SLD) as a patient's mean time-lagged correlation among nine depressive symptoms across 13 weekly assessments. We hypothesized that patients with higher SLD during CT have better outcomes (treatment response, and fewer symptoms after response), and we explored whether SLD correlated with other possible CT processes (growth in social adjustment and CT skills).

Method

Data were drawn from two clinical trials of CT for adult outpatients with recurrent MDD (primary sample n = 475, replication sample n = 146). In both samples, patients and clinicians completed measures of depressive symptoms and social adjustment repeatedly during CT. In the primary sample, patients and cognitive therapists rated patients' CT skills. After CT, responders were assessed for 32 (primary sample) or 24 (replication sample) additional months to measure long-term depression outcomes.

Results

Higher SLD predicted increases in social adjustment (both samples) and CT skills (primary sample) during CT, CT response (both samples), and lower MDD severity for at least 2 years after CT response (both samples). Analyses controlled patient-level symptom means and variability to estimate SLD's incremental predictive validity.

Conclusions

These novel findings from two independent samples with longitudinal follow-up require further replication and extension. SLD may reflect or facilitate generalization of CT skills, improvement in social functioning, or other processes responsible for CT's shorter and longer term benefits.

SUBMITTER: Vittengl JR

PROVIDER: S-EPMC9354858 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Does Symptom Linkage Density Predict Outcomes in Cognitive Therapy for Recurrent Depression?

Vittengl Jeffrey R JR Clark Lee Anna LA Thase Michael E ME Jarrett Robin B RB

Journal of psychopathology and behavioral assessment 20210817 2

<h4>Background</h4>Acute-phase cognitive therapy (CT) is an efficacious treatment for major depressive disorder (MDD), but how CT helps patients is incompletely understood. As a potential means to clarify CT mechanisms, we defined "symptom linkage density" (SLD) as a patient's mean time-lagged correlation among nine depressive symptoms across 13 weekly assessments. We hypothesized that patients with higher SLD during CT have better outcomes (treatment response, and fewer symptoms after response) ...[more]

PMID: 35937855

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Project description:ObjectiveWe tested nomothetic and idiographic convergence and change in 3 symptom measures during acute-phase cognitive therapy (CT) for depression and compared outcomes among patients showing different change patterns.MethodOutpatients (N = 362; 69% women; 85% White; age M = 43 years) with recurrent major depressive disorder according to criteria in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000) completed the Hamilton Rating Scale for Depression (Hamilton, 1960), Beck Depression Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961), and Inventory for Depressive Symptomatology-Self-Report (Rush, Gullion, Basco, Jarrett, & Trivedi, 1996) on 14 occasions as well as pre/post-CT measures of social-interpersonal functioning and negative cognitive content.ResultsThe 3 symptom measures marked the same severity and change constructs, and we offer improved formulas for intermeasure score conversions via their common factor. Pre/post-CT symptom reductions were large (ds = 1.71-1.92), and nomothetic symptom curves were log-linear (larger improvements earlier and smaller improvements later in CT). Nonetheless, only 30% of individual patients showed clear log-linear changes, whereas other patients showed linear (e.g., steady decreases; 20%), 1-step (e.g., a quick drop; 16%), and unclassified (34%) patterns. Log-linear, linear, and 1-step patients were generally similar to one another and superior to unclassified patients post-CT in symptom levels, response and stable remission rates, social-interpersonal functioning, and cognitive content (median d = 0.69).ConclusionsReaching a low-symptom "destination" at the end of CT via any coherent "path" is more important in the short term than which path patients take. We discuss implications for theories of change, clinical monitoring of individuals' progress in CT, and the need to investigate long-term outcomes of patients with differing patterns of symptom change.

Project description:Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment response in those receiving CBTp (n = 22) could be predicted by pre-therapy neural responses to facial affect that was either threat-related (ambiguous 'neutral' faces perceived as threatening in psychosis, in addition to angry and fearful faces) or prosocial (happy faces). The models predicted improvement in psychotic (r = 0.63, p = 0.003) and affective (r = 0.31, p = 0.05) symptoms following CBTp, but not in the treatment-as-usual group (n = 16). Psychotic symptom improvement was predicted by neural responses to threat-related affect across sensorimotor and frontal-limbic regions, whereas affective symptom improvement was predicted by neural responses to fearful faces only as well as prosocial affect across sensorimotor and frontal regions. These findings suggest that CBTp most likely improves psychotic and affective symptoms in those endorsing more threatening appraisals and mood-congruent processing biases, respectively, which are explored and reframed as part of the therapy. This study improves our understanding of the neurobiology of treatment response and provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients.

Dataset Information

Does Symptom Linkage Density Predict Outcomes in Cognitive Therapy for Recurrent Depression?

Background

Method

Results

Conclusions

Publications

Does Symptom Linkage Density Predict Outcomes in Cognitive Therapy for Recurrent Depression?

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