Investigators Seek Alternative Methods to Quantify Depression Treatment Success

R. Yates Coley, PhD

The 2020 American Psychiatric Association (APA) Annual Meeting was cancelled this year, with plans made to convert the world-leading psychiatry conference into a two-part virtual session and educational platform for attendees.

In lieu of regular on-site coverage, HCPLive® will be running a series of interviews, insights, and reporting on topics that frequently headline the APA meeting—featuring familiar experts.

There may be some alternative ways to measure depression treatment success outside of response and remission.

A team, led by R. Yates Coley, PhD, Assistant Investigator, Kaiser Permanente Washington Health Research Institute, compared measures such as effect size and severity-adjusted effect size (SAES) for a successful depression treatment and examined the relationship between baseline symptom severity and treatment success.

The National Committee for Quality Assurance recommends response and remission are indicators of successful depression treatment for the Healthcare Effectiveness and Data Information Set.

The investigators examined electronic records from a pair of large integrated health systems and identified 5554 new psychotherapy episodes using a baseline Patient Health Questionnaire (PHQ-9) score of ≥10 and a follow-up score from 14—180 days after treatment initiation. They defined treatment success from 4 different measures—response (≥50% reduction in PHQ-9 score), remission (PHQ-9 score <5), effect size ≥0.8, and SAES ≥0.8.

The investigators also used logistic regression to estimate the link between baseline severity and success on each measure and sensitivity analyses to evaluate the impact of various outcome definitions and loss to follow-up.

The measure most frequently attained was effect size ≥0.8 (72% across sites), followed by SAES ≥0.8 (66%), response (46%), and remission (22%).

However, the only measure not associated with baseline PHQ-9 score was response.

Effect size ≥0.8 favored episodes with a higher baseline PHQ-9 score (OR, 2.3; P <0.001, for 10-point difference in baseline PHQ-9 score), while SAES ≥0.8 (OR, 0.61; P <0.001) and remission (OR, 0.43; P <0.001) favored episodes with lower baseline scores.

“Response is preferable for comparing treatment outcomes, because it does not favor more or less baseline symptom severity, indicates clinically meaningful improvement, and is transparent and easy to calculate,” the authors wrote.

Last year, research suggested a neurocomputational approach could help better predict treatment response classification in depressed patients.

An investigative team, led by Filippo Queirazza, MD, PhD, from the University of Glasgow, used a conventional mass-univariate fMRI analysis to determine that patients exhibit greater pretreatment neural activity by encoding a weighted reward prediction error (RPE) in the right striatum and right amygdala.

The study involved 37 participants with a primary diagnosis of depressive disorder, but were not medicated at the time of the study. Each patient attended an appointment that included a clinical diagnosis prior to the completion of a computerized cognitive behavioral therapy (cCBT) program, as well as 2 months following treatment.

After the first appointment, participants also engaged in an online CBT-based guided self-help program developed at the University of Glasgow. After the final appointment, the investigators classified 19 participants as responders to treatment and 18 subjects as nonresponders.

“The advantage of using self-help CBT is two-fold,” the authors wrote. “First, it substantially reduces the confounding effect of therapist variability on treatment response, and second, it permits monitoring of treatment adherence defined as the number of online modules completed.”

CBT has long been known for effectively treating MDD for approximately 45% of depressed patients. However, there is not a clinically viable neuroimaging predictor of CBT response, largely due to a lack of identified predicative biomarkers.

Multivariate classification of brain imaging data can inform the discovery of neural signatures in the response to treatment of MDD patients because they can yield predictions on the individual level and can be tuned to the fine-grained and spatially distributed information patterns encoded in large-scale brain activity and may be more sensitive in identifying neural biomarkers than conventional imaging analysis methods.

The study, “Defining Success in Measurement-Based Care for Depression: A Comparison of Common Metrics,” was published online in Psychiatric Services.